chemistry problem solving ai

Chemistry Assistant

Ai-powered chemistry problem solver.

• Homework Help: Students can use the Chemistry Assistant to help understand and work through chemistry problems in their homework.
• Teaching Aid: Teachers can use this tool to generate solutions to chemistry problems, aiding in lesson planning and student instruction.
• Exam Preparation: Use the Chemistry Assistant to prepare for chemistry exams by solving practice problems and getting explanations of chemistry terms and principles.
• Research Assistance: Researchers can use this tool to help work through chemistry problems in their work.

Yes, the Chemistry Assistant is designed to handle a wide range of chemistry problems, from basic to advanced. However, it's always important to cross-verify the solutions provided by the AI with trusted resources or professionals in the field to ensure accuracy and understanding, especially with more complex problems and principles.

While the Chemistry Assistant is specifically designed for chemistry problems, HyperWrite offers other AI tools for different subjects and needs. You can explore more tools at app.hyperwriteai.com/tools .

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StudyMonkey

Your personal ai chemistry tutor.

Learn Smarter, Not Harder with Chemistry AI

Introducing StudyMonkey, your AI-powered Chemistry tutor .

StudyMonkey AI can tutor complex Chemistry homework questions, enhance your essay writing and assess your work—all in seconds.

No more long all-nighters

24/7 solutions to Chemistry questions you're stumped on and essays you procrastinated on.

No more stress and anxiety

Get all your Chemistry assignments done with helpful answers in 10 seconds or less.

No more asking friends for Chemistry help

StudyMonkey is your new smart bestie that will never ghost you.

No more staying after school

AI Chemistry tutoring is available 24/7, on-demand when you need it most.

Chemistry is the branch of science that studies the properties and behavior of matter, the changes it undergoes during chemical reactions, and the energy that accompanies those processes.

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9 Best AI Chemistry Problem Solvers

The advent of AI chemistry problem solvers marks a significant milestone in educational technology, providing students with a resource that is accessible, reliable, and tailored to their individual learning needs. Whether it’s a high school student preparing for an exam or a college undergraduate tackling complex organic chemistry, AI tools are becoming an indispensable part of the educational toolkit. As we delve into the world of AI chemistry problem solvers, we’ll explore their necessity, how they function, and the criteria for selecting the best one to suit your academic or research needs.

The Need for AI Chemistry Problem Solver

Chemistry, often referred to as the central science, forms a bridge between physics and biology, making it essential for a wide range of scientific disciplines. However, the complexity of chemical equations, the abstract nature of atomic and molecular interactions, and the vast amount of memorization required can overwhelm even the most diligent students. This is where an AI chemistry problem solver becomes a game-changer. It serves as a personal tutor that is available around the clock, providing step-by-step solutions and clarifications to complex problems. This not only aids in homework completion but also reinforces learning by breaking down difficult concepts into digestible parts.

Moreover, the need for an AI chemistry problem solver extends beyond individual learning. In a classroom setting, educators can leverage these tools to create more dynamic and interactive lessons. By automating the problem-solving process, teachers can focus on imparting a deeper understanding of the underlying principles of chemistry. Additionally, for researchers and professionals in the field, AI problem solvers can expedite the process of experimentation and discovery by predicting outcomes and suggesting alternative pathways for chemical reactions, thus pushing the boundaries of innovation and scientific progress.

How does AI Chemistry Problem Solver work?

At the core of an AI chemistry problem solver is a sophisticated algorithm that utilizes machine learning and natural language processing to interpret and solve chemistry-related queries. When a user inputs a problem, the AI analyzes the question, identifies the relevant chemical principles, and applies the appropriate laws and equations to arrive at a solution. This process mimics the logical reasoning a human tutor would use, but with the added benefits of speed and accuracy that only a machine can provide. The AI draws from a vast database of chemical knowledge, ensuring that the solutions are not only correct but also up to date with the latest scientific findings.

The functionality of these AI tools extends to various branches of chemistry, including but not limited to, general chemistry, organic, inorganic, physical, and analytical chemistry. They can handle a multitude of problem types, from balancing chemical equations to predicting reaction outcomes and explaining molecular structures. The AI’s ability to provide detailed explanations for each step of the solution process is particularly beneficial for students, as it allows them to follow the logic behind the answer and apply similar reasoning to future problems.

9 Best AI Chemistry Problem Solver (Free & Paid)

• Chemistry AI Homework Solver
• Chemistry Assistant
• StudyMonkey
• Chemistry Problem Solver
• Chemistry Homework Helper
• Chemistry X10
• Chemistry Answers

How to choose the Best AI Chemistry Problem Solver?

Selecting the best AI chemistry problem solver requires careful consideration of several factors. The first aspect to consider is the range of chemistry topics the tool covers. A comprehensive solver that spans multiple branches of chemistry is more likely to meet the diverse needs of students and professionals. Another critical factor is the accuracy of the solutions provided. The best AI problem solvers are those that consistently deliver correct answers, as this builds trust and reliability in the tool.

The user interface is also an important consideration. A user-friendly and intuitive platform can significantly enhance the learning experience, making it easier for users to input problems and understand solutions. Additionally, the quality of explanations is paramount. The solver should not only provide the correct answer but also explain the reasoning behind it in a clear and concise manner, which is crucial for learning and comprehension.

Lastly, consider the flexibility and adaptability of the AI tool. The best AI chemistry problem solver is one that is continually learning and updating its knowledge base to incorporate the latest scientific research and methodologies. This ensures that users have access to the most current and relevant information, which is especially important in a field that is constantly advancing.

AI Chemistry Problem Solver

1. chemistry ai homework solver.

Chemistry AI Homework Solver is designed for students grappling with the complexities of chemistry. This innovative AI chemistry problem solver, powered by advanced algorithms and machine learning, is designed to demystify the intricate world of chemical equations, reactions, and concepts. Whether you’re a high school student facing the daunting task of balancing equations or a college attendee delving into organic chemistry, this AI solver is tailored to meet your needs. It offers a seamless, user-friendly interface that transforms your chemistry homework from a challenging chore into an engaging, educational experience. By providing step-by-step solutions, it not only answers your immediate questions but also enhances your overall understanding of the subject matter.

What does Chemistry AI Homework Solver do?

The Chemistry AI Homework Solver is a cutting-edge tool that revolutionizes the way students approach their chemistry homework. By inputting your assignment or specific question into the system, you unlock access to a world of accurate, efficient solutions generated within seconds. This AI-powered solver is adept at tackling a wide array of chemistry problems, from basic compound identification to complex reaction mechanisms. Its capabilities extend beyond mere problem solving, it serves as an educational companion that guides you through the learning process with detailed explanations and insights. This ensures not only the completion of your homework but also a deeper comprehension of the underlying principles, preparing you for future challenges in your academic journey.

Chemistry AI Homework Solver Key Features

Accuracy and Efficiency : The solver’s advanced algorithms ensure that solutions are not only correct but also provided swiftly, making it an invaluable resource during crunch times.

Step-by-Step Solutions : It breaks down answers into comprehensible steps, aiding in the understanding of complex concepts and fostering independent problem-solving skills.

Wide Range of Topics : From general chemistry to more specialized fields like organic and inorganic chemistry, the solver covers an extensive array of topics, catering to students at various levels of education.

User-Friendly Interface : Designed with the user in mind, the platform is easy to navigate, ensuring that students can quickly find the help they need without unnecessary complications.

24/7 Availability : Unlike human tutors, the Chemistry AI Homework Solver is accessible around the clock, providing reliable assistance whenever and wherever it’s needed.

Educational Enhancement : Beyond solving problems, the tool offers explanations and insights that enrich the student’s learning experience, making it a comprehensive educational aid.

Chemistry AI Homework Solver Pricing Plans

Chemistry AI Homework Solver offers a free AI chemistry problem solver.

2. Chemistry Assistant

Chemistry Assistant is AI-powered chemistry problem solver that serves as a beacon for students, educators, and professionals navigating the intricate world of chemistry. This digital aid is engineered to address and dissect chemistry questions, offering a streamlined approach to problem-solving. By harnessing the capabilities of advanced AI models, Chemistry Assistant demystifies complex chemical equations and reactions, delivering solutions that are both comprehensive and comprehensible. Its design is tailored to cater to a diverse audience, ranging from students grappling with homework to researchers seeking to unravel scientific queries.

What does Chemistry Assistant do?

Chemistry Assistant operates as a virtual chemist, adept at tackling a broad spectrum of chemistry-related inquiries. Users can input their chemistry problems or questions into the system, prompting the AI to generate detailed answers and step-by-step solutions. This tool is particularly beneficial for homework assistance, enabling students to deepen their understanding and work through challenging problems. Teachers can leverage Chemistry Assistant as a resource for lesson planning and providing instructional support, while students can utilize it to fortify their exam preparation. Researchers, too, find value in this tool, as it aids in the analytical aspects of their work, offering insights and solutions to complex chemical problems.

Chemistry Assistant Key Features

Homework Help : Chemistry Assistant stands out as a valuable resource for students, offering assistance with homework assignments by breaking down complex problems into manageable steps.

Teaching Aid : Educators can utilize this tool to generate solutions for classroom examples, enhancing their teaching methods and enriching the learning experience for students.

Exam Preparation : For students preparing for exams, this AI tool provides practice problems and explanations of chemistry concepts, aiding in their study efforts.

Research Assistance : Researchers can employ Chemistry Assistant to navigate through intricate chemistry problems, thereby streamlining their research process.

User-Friendly Interface : The tool’s design is intuitive, allowing for easy submission of queries and quick retrieval of solutions, making it accessible to users of all levels.

Accessibility : With a free trial available, Chemistry Assistant is accessible to anyone looking to explore its capabilities before committing to a paid plan.

Chemistry Assistant Pricing Plans

Chemistry Assistant offers a variety of pricing plans to accommodate the needs of different users.

Free Plan : This plan allows users to experience the basic features of Chemistry Assistant without any financial commitment, providing a taste of what the tool can offer.

Premium Plan : For a monthly fee, users can access an extended suite of features, enhancing their problem-solving capabilities and gaining more in-depth assistance. This plan is priced at $19.99 per month.

Ultra Plan : The most comprehensive plan available, it caters to users seeking the full range of services and support from Chemistry Assistant, ideal for those who require extensive use of the tool. This plan is priced at $44.99 per month.

Chemistry Assistant accepts a range of payment methods, including debit and credit cards, and PayPal.

3. StudyMonkey

StudyMonkey is a cutting-edge AI Chemistry Problem Solver designed to transform the way students approach chemistry homework and essay writing. This AI-powered tutor is tailored to assist with complex chemistry questions, offering step-by-step guidance and assessments in mere seconds. Its 24/7 availability ensures that students can find solutions to their chemistry queries anytime, eliminating the need for all-nighters and reducing stress and anxiety associated with challenging assignments. StudyMonkey stands out by providing a seamless and efficient learning experience, making it an invaluable tool for students seeking to enhance their chemistry knowledge and academic performance.

What does StudyMonkey do?

StudyMonkey is engineered to serve as an all-encompassing AI Chemistry Problem Solver, adept at handling a wide array of chemistry-related queries and assignments. Whether it’s tackling multiple-choice questions, short answers, or even essays, StudyMonkey delivers precise answers within seconds. Beyond just providing answers, it offers step-by-step guidance to ensure students not only get the correct answers but understand the underlying concepts. Its support spans across various education levels, from elementary to master’s degree, making it a versatile tool for students of all ages. Additionally, StudyMonkey allows users to review their question history, facilitating better preparation for tests and overall grade improvement.

StudyMonkey Key Features

Step-by-Step Guidance : StudyMonkey breaks down complex chemistry problems into manageable steps, making it easier for students to grasp difficult concepts.

24/7 Availability : This AI tutor is accessible around the clock, providing students with the help they need whenever they need it, without any time constraints.

Supports Multiple Question Types : From multiple-choice questions to essays, StudyMonkey is equipped to handle a diverse range of question formats.

Covers All Education Levels : Whether you’re in elementary school or pursuing a master’s degree, StudyMonkey caters to every level of education.

Review Past Questions : Students can access their history of asked questions and answers, enabling them to revisit and reinforce their learning.

Enhanced Essay Writing : Beyond solving chemistry problems, StudyMonkey also aids in enhancing students’ essay writing skills, offering assessments and improvements in seconds.

StudyMonkey Pricing Plans

StudyMonkey offers 3 pricing plans:

Basic Plan : This plan allows for 3 questions daily across 5 subjects using the standard AI model and standard support. It’s a great starting point for occasional help.

Homework Helper : Offering 50 questions daily with unlimited subjects, this plan features the standard AI model and premium support, making it ideal for regular study sessions. This plan is priced at $8 per month ($6 per month when billed annually).

Homework Helper + Essay : For the most comprehensive support, this plan provides unlimited questions daily, covers unlimited subjects, includes an advanced AI model, and offers premium support. It’s perfect for students seeking extensive help, including essay writing assistance. This plan is priced at $10 per month ($8 per month when billed annually).

4. Chemistry Problem Solver

Chemistry Problem Solver by Easy Peasy AI, stands out as a cutting-edge tool designed to assist students, educators, and professionals in navigating the complex world of chemistry. This AI chemistry problem solver innovative platform leverages artificial intelligence to provide solutions to a wide array of chemistry problems, from basic chemical equations to intricate molecular structures. Its user-friendly interface and comprehensive database make it an indispensable resource for anyone looking to deepen their understanding of chemistry concepts and applications.

What does Chemistry Problem Solver do?

The Chemistry Problem Solver is a versatile tool that simplifies the study and application of chemistry. It excels in breaking down complex chemical equations, calculating molar masses, and providing detailed explanations for various chemical reactions. Whether you’re a high school student preparing for an exam, a college student tackling advanced chemistry courses, or a professional in need of quick chemical analyses, this AI-powered assistant is designed to meet your needs. It offers step-by-step solutions, making it easier to understand the underlying principles of chemistry and apply them in real-world scenarios.

Chemistry Problem Solver Key Features

Interactive Problem Solving : This feature allows users to input specific chemistry problems and receive detailed, step-by-step solutions, enhancing their learning experience.

Comprehensive Chemical Database : It includes a vast repository of chemical compounds and reactions, providing users with a wealth of information at their fingertips.

User-Friendly Interface : Designed with the user in mind, the platform is easy to navigate, making chemistry more accessible to everyone, regardless of their prior knowledge.

Real-Time Feedback : Users receive immediate feedback on their inputs, allowing for a dynamic learning process that adapts to their individual pace and understanding.

Advanced Analytical Tools : The solver is equipped with tools that can analyze and predict chemical reactions, offering insights into the behavior of different compounds.

Educational Resources : Beyond problem-solving, the platform offers tutorials, guides, and educational materials to further support users in their chemistry learning journey.

Chemistry Problem Solver Pricing Plans

Chemistry Problem Solver offers a offers a free AI chemistry problem solver.

5. Chemistry Homework Helper

Chemistry Homework Helper is a great AI suite of tools for students navigating the complex world of chemistry. This AI-powered tool is designed to simplify the learning process, offering a bridge between the intricate concepts of chemistry and the students’ quest for understanding. With its ability to provide instant, accurate, and step-by-step solutions to a wide array of chemistry problems, Chemistry Homework Helper stands out as a versatile companion for students at various levels of their academic journey. Whether it’s a basic chemical equation or a more complex reaction mechanism, this tool is equipped to demystify the subject, making chemistry more accessible and less daunting for learners around the globe, and a great addition to the list of the best AI chemistry problem solvers.

What does Chemistry Homework Helper do?

Chemistry Homework Helper is a cutting-edge tool that transforms the way students approach their chemistry studies. By leveraging advanced AI algorithms, it offers personalized assistance, breaking down complex chemical reactions, equations, and concepts into understandable segments. This AI solver not only provides answers but also explains the underlying principles and steps involved, fostering a deeper comprehension of the subject matter. It’s like having a personal tutor available 24/7, ready to assist with everything from homework assignments to exam preparation. The tool’s ability to adapt to individual learning styles and provide multilingual support further enhances its utility, making it an invaluable resource for a diverse range of students seeking to excel in chemistry.

Chemistry Homework Helper Key Features

All-round Chemistry Support : This feature ensures that students receive comprehensive assistance across all areas of chemistry, from basic concepts to advanced topics.

Step-by-Step Solutions : The AI breaks down solutions into manageable steps, making complex problems easier to understand and learn from.

Efficient and Economical : It streamlines the study process, saving students time and resources while helping them achieve better academic results.

Multilingual Support : The tool is accessible in multiple languages, ensuring that language barriers do not hinder students’ learning experiences.

Available 24/7 : Students can access the tool at any time, providing reliable and instant help whenever it’s needed.

Worldwide Accessibility : Its universal access ensures that students from any part of the world can benefit from its features, making quality chemistry help more accessible.

Chemistry Homework Helper Pricing Plans

Chemistry Homework Helper offers a AI chemistry problem solver.

Mathway is an AI mathematic and chemistry problem solver accessible via web and mobile applications, offers a seamless interface for solving a wide array of chemistry equations and problems. Whether you’re grappling with basic chemical reactions or delving into more intricate organic chemistry queries, Mathway provides instant solutions, making it an indispensable tool for learners at various levels. Its ability to break down complex problems into understandable solutions is particularly beneficial for those looking to deepen their understanding of chemistry concepts.

What does Mathway do?

Mathway serves as a versatile problem solver for a multitude of subjects, with a special emphasis on chemistry. By simply inputting a chemistry problem, users receive immediate answers. However, Mathway’s utility extends beyond just providing solutions, it offers step-by-step explanations for users who opt for a subscription. This feature is invaluable for students seeking to understand the methodology behind the answers. From balancing chemical equations to predicting reaction outcomes, Mathway covers an extensive range of topics. Its user-friendly interface, which allows for problem input via text, photo, or voice, ensures that users can easily navigate and utilize the platform to its full potential.

Mathway Key Features

Comprehensive Chemistry Solver : Mathway excels in offering solutions across a broad spectrum of chemistry topics, from basic stoichiometry to complex organic chemistry problems. This makes it a versatile tool for students at different levels of their academic journey.

Step-by-Step Explanations : For those seeking not just answers but understanding, Mathway provides detailed, step-by-step solutions that elucidate the process of arriving at the answer. This feature is instrumental in enhancing learning and comprehension.

Multiple Input Methods : Recognizing the diverse needs of its users, Mathway accommodates various methods of problem input, including typing, voice commands, and photo capture. This flexibility ensures that users can easily and quickly find solutions to their problems.

Interactive Graphing Tool : For visual learners, Mathway’s graphing tool is a standout feature. It allows users to visualize equations and functions, offering a graphical representation that can aid in the comprehension of complex concepts.

Extensive Symbol Keyboard : Mathway’s comprehensive math symbol keyboard simplifies the process of inputting complex equations, ensuring that users can accurately represent their problems without limitations.

Mobile and Web Accessibility : With both a mobile app and a web platform, Mathway ensures that users can access its services from anywhere, at any time, making it a convenient and reliable resource for solving chemistry problems on the go.

Mathway Pricing Plans

Mathway offers a free AI chemistry problem solver.

Chem AI is a cutting-edge AI chemistry problem solver solution designed to address the complexities and challenges faced by students, educators, and professionals in the field of chemistry. This innovative app leverages artificial intelligence to provide instant, step-by-step solutions to a wide array of chemistry problems, ranging from simple equations to more complex chemical reactions. Whether you’re grappling with multiple choice questions, word problems, diagrams, mathematical equations, or any other type of chemistry challenge, Chem AI stands ready to assist. Its ability to recognize and solve problems through a simple upload or photo of the question simplifies the learning and problem-solving process, making it an invaluable tool for anyone looking to enhance their understanding of chemistry.

What does Chem AI do?

Chem AI is designed to simplify the study and practice of chemistry by offering immediate, detailed explanations for a plethora of chemistry problems. By allowing users to upload or take photos of their chemistry questions, the app can instantly recognize the problem and provide a comprehensive solution. This feature is particularly beneficial for tackling a wide range of problem types, including multiple choice, word problems, diagrams, and mathematical equations. Chem AI covers topics across all levels of chemistry, from high school basics to advanced doctoral research, making it a versatile tool for users at any stage of their chemistry journey. The app’s ability to deliver math solutions with detailed steps further enhances its utility, providing users with a deeper understanding of the problem-solving process.

Chem AI Key Features

Photo Recognition : Chem AI’s photo recognition capability allows users to quickly upload or snap a photo of their chemistry problem, whether it’s handwritten or digital, streamlining the problem-solving process.

Detailed Solutions : The app provides detailed, step-by-step solutions to problems, enabling users to not only get the answer but understand the methodology behind it.

Comprehensive Coverage : From high school chemistry to doctoral-level research, Chem AI covers a broad spectrum of chemistry topics, ensuring users have access to a wide range of study materials.

Math Solution Steps : For problems involving calculations, Chem AI offers math solutions accompanied by detailed steps, aiding in the comprehension of complex equations.

User-Friendly Interface : Designed with the user in mind, the app features an intuitive interface that makes navigating through its functions and features a breeze.

Free Trial : New users can take advantage of a free trial to explore the app’s capabilities before committing, providing an opportunity to test its effectiveness in solving chemistry problems.

Chem AI Pricing Plans

Chem AI offers a free app to download, an AI chemistry problem solver, with in-app purchases.

8. Chemistry X10

Chemistry X10 is a versatile AI chemistry problem solver tool designed to assist students and educators in tackling a variety of chemistry problems. This application stands out for its ability to function without an internet connection, making it a creliable companion for users in any setting. With a substantial number of downloads, Chemistry X10 has proven its utility across different educational levels, from middle school to high school. It simplifies the process of solving complex chemistry assignments by providing clear explanations and accurate answers, which is essential for those looking to enhance their understanding of the subject.

What does Chemistry X10 do?

Chemistry X10 serves as an educational resource that simplifies the process of learning and solving chemistry problems. This AI chemistry problem solver is adept at handling a range of tasks including chemical formulas, solutions, mixtures, reaction equations, and calculations involving excess and deficiency. The app’s capability to find reaction equations and solve transformation chains adds to its comprehensive nature. Moreover, Chemistry X10 aids in the arrangement of coefficients in chemical equations, whether by fitting or by the method of electronic balance, and it is equipped to calculate molar masses of complex substances, thereby providing a well-rounded tool for chemistry students.

Chemistry X10 Key Features

Task Solutions : Chemistry X10 excels in providing step-by-step solutions to chemistry problems by allowing users to input the ‘Given’ and ‘Find’ parameters, resulting in a solved task with a thorough explanation and its answer.

Chemical Reactions : The app boasts a database of 8000 reactions that can be accessed offline, enabling users to find reaction equations by entering a part of the reaction.

Transformation Chains : Users can solve open transformation chains for various compounds, which is particularly useful for understanding the sequence of chemical reactions.

Coefficient Arrangement : Chemistry X10 simplifies the complex task of arranging coefficients in chemical equations, offering methods such as fitting or electronic balance to achieve the correct stoichiometric proportions.

Molar Masses Calculation : The app includes a feature to calculate the molar masses of complex substances, an essential aspect of chemistry that aids in various calculations and understanding of material properties.

Offline Accessibility : One of the most significant advantages of Chemistry X10 is its ability to work without an internet connection, ensuring that users have access to its features regardless of their location or connectivity.

Chemistry X10 Pricing Plans

Chemistry X10 offers a free app to download, an AI chemistry problem solver, with in-app purchases.

Free Plan : Users can download the app at no cost and immediately start solving chemistry problems with the basic features provided.

Chemistry X10 accepts various forms of payment, including debit and credit cards, PayPal, and bank wire transfers, catering to a global audience and ensuring convenience in transactions.

9. Chemistry Answers

Chemistry Answers is an educational AI chemistry problem solver app designed to assist students, educators, and chemistry enthusiasts with various chemistry-related queries and problems. The app serves as a learning aid, offering a clear interface and simple functions that do not require an internet connection to operate. With the ability to switch between Chinese and English versions, Chemistry Answers provides a versatile tool for a diverse user base. It includes a range of features such as chemical equation balancing, a periodic table, molar mass calculations, and queries for common names and substance colors, among others. The app is continuously evolving, with new features being added to enhance the user experience and expand its capabilities.

What does Chemistry Answers do?

Chemistry Answers functions as a comprehensive mobile resource for tackling chemistry challenges. It aids users by providing quick access to a chemical equation balancer, a detailed periodic table, and tools for calculating molar masses. The app also allows users to look up common names of chemicals, inquire about the color of substances, and refer to a metal activity table and solubility chart. These features are particularly useful for students who need to study on the go, educators who require a handy reference tool, and anyone interested in learning more about chemical substances and their properties.

Chemistry Answers Key Features

Chemical Equation Balancer : This feature simplifies the process of balancing chemical equations, a fundamental skill in chemistry that can often be time-consuming and complex.

Periodic Table : The app provides an interactive periodic table, offering detailed information about each element, which is essential for understanding chemical reactions and properties.

Molar Mass Calculation : Users can quickly calculate the molar mass of compounds, a crucial step in many chemistry calculations, such as stoichiometry and preparing solutions.

Common Name Query : This tool allows users to search for chemicals by their common names, bridging the gap between non-scientific and scientific terminology.

Substance Color Query : A unique feature that helps users identify chemicals based on their color, which can be particularly useful in laboratory settings.

Metal Activity Table : The app includes a metal activity series table, which is important for predicting the outcomes of displacement reactions and understanding reactivity trends.

Chemistry Answers Pricing Plans

Chemistry Answers offers a free app to download for AI chemistry problem solving with in-app purchases.

FAQs on AI Chemistry Problem Solver

What is an ai chemistry problem solver.

An AI Chemistry Problem Solver is a sophisticated tool designed to assist students, educators, and professionals in solving complex chemistry problems. Leveraging advanced algorithms, machine learning, and natural language processing, these solvers can interpret chemistry questions and provide detailed solutions, explanations, and predictions for a wide range of chemistry topics. They serve as an invaluable resource for enhancing understanding, improving academic performance, and facilitating research in the field of chemistry.

Who can benefit from using an AI Chemistry Problem Solver?

Virtually anyone studying or working in the field of chemistry can benefit from using an AI Chemistry Problem Solver. This includes high school and college students grappling with chemistry homework, teachers seeking to enrich their lesson plans, researchers aiming to expedite their experiments, and professionals in need of quick solutions to chemical queries. These tools are designed to cater to a broad spectrum of chemistry-related needs, making complex concepts more accessible and manageable.

Can AI Chemistry Problem Solvers handle all types of chemistry problems?

Yes, the best AI Chemistry Problem Solvers are equipped to handle a vast array of problem types across different branches of chemistry, including general, organic, inorganic, physical, and analytical chemistry. From balancing chemical equations and predicting reaction outcomes to explaining molecular structures, these tools are designed to provide comprehensive solutions and explanations for a wide range of chemistry problems.

How accurate are AI Chemistry Problem Solvers?

The accuracy of AI Chemistry Problem Solvers largely depends on the sophistication of their algorithms and the quality of the data they are trained on. Top-tier solvers utilize advanced AI models and are regularly updated with the latest scientific research, ensuring high levels of accuracy in their solutions. However, it’s always advisable to cross-verify solutions with trusted resources, especially for more complex or novel problems.

Are there any limitations to using AI Chemistry Problem Solvers?

While AI Chemistry Problem Solvers are powerful tools, they do have limitations. Their effectiveness can be constrained by the specificity and complexity of the problem posed, the quality of the input data, and the current state of AI technology. Additionally, for highly novel or cutting-edge chemistry problems, the solver’s database may not yet include the most recent research findings, which could affect the accuracy of its solutions.

How do AI Chemistry Problem Solvers enhance learning?

AI Chemistry Problem Solvers enhance learning by providing instant, step-by-step solutions to complex problems, thereby allowing students to understand the logic and methodology behind the answers. This interactive approach helps reinforce learning, improves problem-solving skills, and enables students to apply similar reasoning to future problems, thereby deepening their understanding of chemistry concepts.

Can AI Chemistry Problem Solvers replace human tutors?

While AI Chemistry Problem Solvers are an excellent resource for learning and problem-solving, they are not intended to replace human tutors. These tools complement traditional learning methods by providing additional support and resources. Human tutors offer personalized guidance, adapt teaching methods to individual learning styles, and provide emotional and motivational support, aspects that AI currently cannot replicate fully.

How do I choose the right AI Chemistry Problem Solver for my needs?

Choosing the right AI Chemistry Problem Solver involves considering several factors, including the range of chemistry topics covered, the accuracy and reliability of the solutions, the user interface’s ease of use, the quality of explanations provided, and the tool’s adaptability to incorporate the latest scientific research. Evaluating these aspects will help you select a solver that best meets your academic or professional needs.

Are AI Chemistry Problem Solvers accessible to everyone?

Many AI Chemistry Problem Solvers offer free versions or trials, making them accessible to a wide audience. However, full access to advanced features and unlimited problem-solving capabilities may require a subscription or purchase. The goal is to make these tools as accessible as possible to support learning and research in chemistry across diverse demographics.

What is the future of AI Chemistry Problem Solvers?

The future of AI Chemistry Problem Solvers looks promising, with ongoing advancements in AI and machine learning technologies driving continuous improvements in their capabilities. Future developments are expected to include enhanced accuracy, broader coverage of chemistry topics, more intuitive user interfaces, and increased personalization to cater to individual learning styles and needs. As AI technology evolves, these tools will become even more integral to the study and practice of chemistry.

The integration of AI into the field of chemistry has opened up new avenues for learning, teaching, and research. AI Chemistry Problem Solvers stand at the forefront of this technological revolution, offering users an unparalleled resource for tackling complex chemistry problems. By providing instant, accurate, and detailed solutions, these tools not only enhance understanding and academic performance but also pave the way for innovation and discovery in the scientific community. As we look to the future, the potential of AI Chemistry Problem Solvers to transform the landscape of chemistry education and research is immense. With continued advancements in AI technology, these tools are set to become even more sophisticated, making chemistry more accessible and comprehensible to learners and professionals worldwide.

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Chemistry AI Homework Solver

Omni is the most accurate AI homework solver on the market!

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Introducing Chemistry AI Homework Solver: The Ultimate Solution for Your Chemistry Assignments!

Are you struggling with your chemistry homework? Are you tired of spending hours trying to solve complex equations and formulas? Look no further! Smodin Chemistry Homework Solver is here to help you ace your assignments with ease.

How does it work?

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Chemistry problem solver AI: Chemistry with Artificial intelligence

• Post author: futurethinkersai.com
• Post published: 2 January 2024
• Post category: AI Tools for Education
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Table of Contents

Chemistry problem solver AI

Chemistry problem solver AI is an artificial intelligence technology that helps you solve your chemistry problems with the help of an artificial intelligence system. Imagine you have a super-smart assistant who helps you with chemistry problems. It’s like having a smart friend who helps figure out tricky chemical puzzles. Today, the world is becoming increasingly intelligent, and advanced artificial intelligence helps everyone. Mostly artificial intelligence is used in education ; there are many AI tools for education, social media , writing a blog or article, video editing, and so many more fields. Artificial intelligence is available everywhere. We have also discussed the best AI tools to solve math problems . AI Solves Physics Problems , in our previous blogs. Today in this blog, we will explore chemistry problem-solving AI. How does artificial intelligence help in chemistry? Which chemistry problem-solving AI tools are used? So let’s start!

Can AI help to solve chemistry problems?

Artificial intelligence helps the users to solve their problems. Artificial intelligence uses advanced algorithms to solve individuals’ problems. And provide the best response to the user. AI helps the use and tries to understand the students easily and concisely.

How does AI help to solve chemistry problems?

Artificial intelligence (AI) helps in chemical problem-solving by processing vast datasets, finding trends, and predicting outcomes based on patterns that are learned. The following are some particular ways that AI is beneficial to the study of chemistry:

Reaction Prediction:

Let’s consider you’re a chef and have a lot of ingredients in the kitchen. And you want to make something new but you don’t know how to do it. And on the side, you have a smart friend who already made it done. So your friend will help you more. Exactly Artificial intelligence looks like that. Here’s where AI can help.

When you are going to do a chemical reaction like your friend in the kitchen situation know already same AI has already lot of chemical reactions. Before some chemists make reactions with the help of AI and give you reaction predictions. 

Drug Discoveries:

Instead of experiments in the lab is taking a long time to make new medicines or drugs for a disease. AI makes it faster and more helpful. Where does AI know what medicine is best for such disease based on previous information and data? Think of AI as a super-smart friend who can look at tons of information about different chemicals and diseases.

Spectroscopy Analyses:

Spectroscopy Analysis is like using a magic flashlight in chemistry. Different chemicals make or leave special patterns and fingerprints when exposed to special lights like infrared or ultraviolet, to study chemicals. Identifying these chemicals may be difficult but AI can help. AI can detect the color and interpret the chemicals. It’s a smart helper, decoding chemical secrets and revealing hidden details, making chemical analysis quick and precise. 

Material Designs:

Think of material design as creating the perfect building block, just like a special Lego piece designed for a specific purpose. In chemistry, scientists want to make new materials with cool traits, like being super light or super strong. AI helps by looking at information about different materials and suggesting new combinations with these cool features. It’s like having a super-smart friend who’s great at inventing and can suggest awesome new materials for things like making stronger buildings or better batteries.

Laboratory Automation:

This is like laboratory control by itself means with the help of AI. Scientists doing work in labs spend a long time doing repetitive tasks, AI can handle these for example having a robot. And robot is controlled by smart technology AI, what to do and how to do something like experiment mixing liquids or testing samples, all on their own in the lab.

Literature Review and Information Retrieval:

In doing research a person needs to read lots of books for Literature Review and Information Retrieval that understand what’s already known about a topic. By reading a lot of books and research papers you can simply use tools often powered by AI, to quickly find and collect relevant information from these books.

Big Data Analysis:

Considering you have such a large number of data about a reaction or research to study all of it will take such a long time. Collect main information and conclusion, so it’s difficult. AI can do this in an instant of time simply.  In simpler terms, it’s as if you have a really fast and clever friend who can look at a massive pile of information, pick out the important bits, and tell you the fascinating story hidden within.

Chemistry problem solver AI tools or websites

•          ibm rxn for chemistry.

Description : This tool by IBM uses AI to predict chemical reactions. It can suggest reaction pathways and provide insights into possible chemical transformations.

•             ChemAxon Marvin

Description : Marvin is a suite of chemical drawing and editing tools. It can help with creating and visualizing chemical structures, which is often useful in problem-solving.

•             Chemicalize

Description : Chemicalize is a platform that provides chemical structure recognition and property prediction. It can be used to predict various chemical properties based on a given structure.

•             ChemSpider

Description : ChemSpider is a chemical structure database that can help identify chemical compounds. It’s useful for solving problems related to compound identification.

•             ChemCollective Virtual Lab

Description : This virtual lab allows users to explore different chemistry experiments and concepts. While it’s not specifically a problem solver, it’s a great resource for hands-on learning and experimentation.

•             ChemCalc

Description : ChemCalc is a web-based tool for mass spectrometry calculations. It helps in determining molecular formulas and analyzing mass spectrometry data

•             Chemguide

Description : Chemguide provides tutorials and explanations on various chemistry topics. While not an AI tool, it can be a valuable resource for understanding concepts and solving problems.

•             ACD/Labs Percepta Predictors

Description : ACD/Labs provide a suite of tools for predicting physicochemical properties, ADME (absorption, distribution, metabolism, excretion) properties, and toxicity of chemical compounds.

•             ChemTools

Description : ChemTools is a collection of computational chemistry utilities. It includes tools for analyzing electronic structure calculations and visualizing molecular orbitals.

•             Chemical Identifier Resolver

Description : This tool helps in resolving chemical identifiers, converting between different chemical representations (e.g., SMILES, InChI), and obtaining chemical information based on a given structure.

•             Avogadro

Description : Avogadro is an open-source molecular editor and visualization tool. It can be used for building molecular models, visualizing molecular structures, and performing some computational chemistry calculations.

In this blog, discover the transformative impact of Chemistry Problem Solver AI, an advanced technology streamlining solutions for complex chemistry challenges. Uncover how AI accelerates reaction predictions, expedites drug discoveries, deciphers spectroscopy analyses, sparks innovative material designs, automates laboratory tasks, aids in literature review, and conducts big data analyses. Explore prominent AI tools like IBM RXN, ChemAxon Marvin, and Chemicalize. The blog elucidates how AI serves as a powerful, intelligent companion, revolutionizing problem-solving in the realm of chemistry. Expect to gain insights into AI’s diverse applications, making chemistry more accessible, efficient, and innovative.

Frequently Asked Questions [FAQs]

Q1: can ai solve chemistry problems.

Yes, artificial intelligence (AI) can help solve chemistry problems by using advanced algorithms to process data, understand user queries, and provide accurate responses.

Q2: How does AI assist in solving chemistry problems?

AI aids in chemistry by predicting reactions, expediting drug discoveries, analyzing spectroscopy data, suggesting material designs, automating lab tasks, conducting literature reviews, and performing big data analyses.

Q3: What is reaction prediction, and how does AI contribute to it?

Reaction prediction involves anticipating chemical reactions. AI supports this by leveraging pre-existing chemical reaction data, assisting users in predicting outcomes based on learned patterns.

Q4: Which AI tools are commonly used for chemistry problem-solving?

Some popular AI tools for chemistry include IBM RXN for Chemistry (reaction prediction), ChemAxon Marvin (chemical structure visualization), and Chemicalize (chemical property prediction).

Q5: Can you name a tool that helps with mass spectrometry calculations in chemistry?

ChemCalc is a web-based tool specifically designed for mass spectrometry calculations, aiding in determining molecular formulas and analyzing mass spectrometry data.

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Virtual Lab Finds the Right AI Tool for Each Chemistry Problem

Having the right tool for the job makes the job a lot easier, less expensive and faster. Professor Milad Abolhasani and his colleagues have developed a virtual laboratory that can be used to determine the artificial intelligence (AI) tools best suited for addressing various chemical synthesis challenges in flow chemistry systems.

“Autonomous systems have tremendous potential for accelerating chemical R&D and manufacturing, but they are not in widespread use yet,” says Professor Abolhasani, corresponding author of a paper on the work. “These systems face two kinds of challenges: finding or developing the right hardware for reliable, reproducible automated synthesis; and finding or developing the right ‘brain,’ or AI-guided decision-making algorithm, for efficiently determining the best way of synthesizing the desired material. My team focused on the hardware challenges with our Artificial Chemist project . The work we’re publishing now is focused on addressing the autonomous decision-making challenges.”

Abolhasani’s work stems from his observation that: A) there are a lot of different AI tools available; B) it’s not always clear which tool is the best fit for any given material synthesis problem; and C) whichever tool is selected, it will always need to be fine-tuned based on the chemistry problem.

“Recently, there has been increased interest in using off-the-shelf AI programs for modeling and optimization of chemical reactions,” Abolhasani says. “But those off-the-shelf AI techniques are not one-size-fits-all – they’re not all equally good at solving whatever material synthesis problem you want to address.

“Ultimately, we want to find the best AI model architecture for determining the best material formulation that gives you the target properties you are looking for. Not just identifying the best material, but the best way of producing that material so that it has the best possible combination of characteristics. And the best AI model architecture is going to vary depending on the material and the complexity of the challenge.”

So Abolhasani and his collaborators took an AI-driven approach to finding the best AI tool for each material synthesis problem.

“It would be impossible to do the millions of experiments necessary to determine which AI tools do the best job for addressing different kinds of material synthesis problems,” Abolhasani says. “So, we wanted a model that simulates a real-world microfluidic experimental platform to effectively run those millions of experiments for us.”

The researchers ran 1,000 experiments using their automated Artificial Chemist platform and used those experimental data points to train the virtual experimental platform.

For the work reported in the new paper, the virtual laboratory simulated more than 600,000 experiments, assessing more than 150 AI-guided decision-making strategies. If those experiments were run in the real world, even using automated systems and microscale volumes of material, the experiments would have taken 7.5 years of continuous robotic operation and 400 liters of reagents. Abolhasani’s team did it in about a month.

“We’ve effectively trained our virtual lab to choose the best AI tools for each material synthesis challenge,” Abolhasani says. “And those tools become more efficient every time we use them, helping us resolve increasingly complex challenges in chemistry and chemical engineering. Ultimately, we think that these AI-driven tools will be able to operate quickly enough to adjust operations as needed in real time.”

The paper, “ Accelerated AI development for autonomous materials synthesis in flow, ” is published in the journal Chemical Science . First author of the paper is Robert Epps, a postdoctoral researcher at NC State. The paper was co-authored by Amanda Volk, a Ph.D. student at NC State; and Kristofer Reyes, an assistant professor of materials design and innovation at the University at Buffalo.

The work was done with support from the National Science Foundation, under grants 1902702 and 1936527; the UNC Research Opportunities Initiative; and North Carolina State University.

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• 17 May 2023

For chemists, the AI revolution has yet to happen

You have full access to this article via your institution.

More than 20 years ago, the Cancer Research Screensaver harnessed distributed computing power to assess anti-cancer activity in molecules. Credit: James King-Holmes/SPL

Many people are expressing fears that artificial intelligence (AI) has gone too far — or risks doing so. Take Geoffrey Hinton, a prominent figure in AI, who recently resigned from his position at Google, citing the desire to speak out about the technology’s potential risks to society and human well-being.

But against those big-picture concerns, in many areas of science you will hear a different frustration being expressed more quietly: that AI has not yet gone far enough. One of those areas is chemistry, for which machine-learning tools promise a revolution in the way researchers seek and synthesize useful new substances. But a wholesale revolution has yet to happen — because of the lack of data available to feed hungry AI systems.

Any AI system is only as good as the data it is trained on. These systems rely on what are called neural networks, which their developers teach using training data sets that must be large, reliable and free of bias. If chemists want to harness the full potential of generative-AI tools, they need to help to establish such training data sets. More data are needed — both experimental and simulated — including historical data and otherwise obscure knowledge, such as that from unsuccessful experiments. And researchers must ensure that the resulting information is accessible. This task is still very much a work in progress.

Take, for example, AI tools that conduct retrosynthesis. These begin with a chemical structure a chemist wants to make, then work backwards to determine the best starting materials and sequence of reaction steps to make it. AI systems that implement this approach include 3N-MCTS, designed by researchers at the University of Münster in Germany and Shanghai University in China 1 . This combines a known search algorithm with three neural networks. Such tools have attracted attention, but few chemists have yet adopted them.

What's next for AlphaFold and the AI protein-folding revolution

To make accurate chemical predictions, an AI system needs sufficient knowledge of the specific chemical structures that different reactions work with. Chemists who discover a new reaction usually publish results exploring this, but often these are not exhaustive. Unless AI systems have comprehensive knowledge, they might end up suggesting starting materials with structures that would stop reactions working or lead to incorrect products 2 .

An example of mixed progress comes in what AI researchers call ‘inverse design’. In chemistry, this involves starting with desired physical properties and then identifying substances that have these properties, and that can, ideally, be made cheaply. For example, AI-based inverse design helped scientists to select optimal materials for making blue phosphorescent organic light-emitting diodes 3 .

Computational approaches to inverse design, which ask a model to suggest structures with the desired characteristics, are already in use in chemistry, and their outputs are routinely scrutinized by researchers. If AI is to outperform pre-existing computational tools in inverse design, it needs enough training data relating chemical structures to properties. But what is meant by ‘enough’ training data in this context depends on the type of AI used.

A generalist generative-AI system such as ChatGPT, developed by OpenAI in San Francisco, California, is simply data-hungry. To apply such a generative-AI system to chemistry, hundreds of thousands — or possibly even millions — of data points would be needed.

A more chemistry-focused AI approach trains the system on the structures and properties of molecules. In the language of AI, molecular structures are graphs. In molecules, chemical bonds connect atoms — just as edges connect nodes in graphs. Such AI systems fed with 5,000–10,000 data points can already beat conventional computational approaches to answering chemical questions 4 . The problem is that, in many cases, even 5,000 data points is far more than are currently available.

Artificial intelligence in structural biology is here to stay

The AlphaFold protein-structure-prediction tool 5 , arguably the most successful chemistry AI application, uses such a graph-representation approach. AlphaFold’s creators trained it on a formidable data set: the information in the Protein Data Bank, which was established in 1971 to collate the growing set of experimentally determined protein structures and currently contains more than 200,000 structures. AlphaFold provides an excellent example of the power AI systems can have when furnished with sufficient high-quality data.

So how can other AI systems create or access more and better chemistry data? One possible solution is to set up systems that pull data out of published research papers and existing databases, such as an algorithm created by researchers at the University of Cambridge, UK, that converts chemical names to structures 6 . This approach has accelerated progress in the use of AI in organic chemistry.

Another potential way to speed things up is to automate laboratory systems. Existing options include robotic materials-handling systems, which can be set up to make and measure compounds to test AI model outputs 7 , 8 . However, at present this capability is limited, because the systems can carry out only a relatively narrow range of chemical reactions compared with a human chemist.

AI developers can train their models using both real and simulated data. Researchers at the Massachusetts Institute of Technology in Cambridge have used this approach to create a graph-based model that can predict the optical properties of molecules, such as their colour 9 .

How AlphaFold can realize AI’s full potential in structural biology

There is another, particularly obvious solution: AI tools need open data. How people publish their papers must evolve to make data more accessible. This is one reason why Nature r equests that authors deposit their code and data in open repositories . It is also yet another reason to focus on data accessibility, above and beyond scientific crises surrounding the replication of results and high-profile retractions. Chemists are already addressing this issue with facilities such as the Open Reaction Database .

But even this might not be enough to allow AI tools to reach their full potential. The best possible training sets would also include data on negative outcomes, such as reaction conditions that don’t produce desired substances. And data need to be recorded in agreed and consistent formats, which they are not at present.

Chemistry applications require computer models to be better than the best human scientist. Only by taking steps to collect and share data will AI be able to meet expectations in chemistry and avoid becoming a case of hype over hope.

Nature 617 , 438 (2023)

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Virtual lab finds the right AI tool for each chemistry problem

Having the right tool for the job makes the job a lot easier, less expensive and faster. Chemical engineering researchers have now developed a virtual laboratory that can be used to determine the artificial intelligence (AI) tools best suited for addressing various chemical synthesis challenges in flow chemistry systems.

"Autonomous systems have tremendous potential for accelerating chemical R&D and manufacturing, but they are not in widespread use yet," says Milad Abolhasani, corresponding author of a paper on the work and an assistant professor of chemical engineering at North Carolina State University. "These systems face two kinds of challenges: finding or developing the right hardware for reliable, reproducible automated synthesis; and finding or developing the right 'brain,' or AI-guided decision-making algorithm, for efficiently determining the best way of synthesizing the desired material. My team focused on the hardware challenges with our Artificial Chemist project. The work we're publishing now is focused on addressing the autonomous decision-making challenges."

Abolhasani's work stems from his observation that: A) there are a lot of different AI tools available; B) it's not always clear which tool is the best fit for any given material synthesis problem; and C) whichever tool is selected, it will always need to be fine-tuned based on the chemistry problem.

"Recently, there has been increased interest in using off-the-shelf AI programs for modeling and optimization of chemical reactions," Abolhasani says. "But those off-the-shelf AI techniques are not one-size-fits-all -- they're not all equally good at solving whatever material synthesis problem you want to address.

"Ultimately, we want to find the best AI model architecture for determining the best material formulation that gives you the target properties you are looking for. Not just identifying the best material, but the best way of producing that material so that it has the best possible combination of characteristics. And the best AI model architecture is going to vary depending on the material and the complexity of the challenge."

So Abolhasani and his collaborators took an AI-driven approach to finding the best AI tool for each material synthesis problem.

"It would be impossible to do the millions of experiments necessary to determine which AI tools do the best job for addressing different kinds of material synthesis problems," Abolhasani says. "So, we wanted a model that simulates a real-world microfluidic experimental platform to effectively run those millions of experiments for us."

The researchers ran 1,000 experiments using their automated Artificial Chemist platform and used those experimental data points to train the virtual experimental platform.

For the work reported in the new paper, the virtual laboratory simulated more than 600,000 experiments, assessing more than 150 AI-guided decision-making strategies. If those experiments were run in the real world, even using automated systems and microscale volumes of material, the experiments would have taken 7.5 years of continuous robotic operation and 400 liters of reagents. Abolhasani's team did it in about a month.

"We've effectively trained our virtual lab to choose the best AI tools for each material synthesis challenge," Abolhasani says. "And those tools become more efficient every time we use them, helping us resolve increasingly complex challenges in chemistry and chemical engineering. Ultimately, we think that these AI-driven tools will be able to operate quickly enough to adjust operations as needed in real time."

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Story Source:

Materials provided by North Carolina State University . Original written by Matt Shipman. Note: Content may be edited for style and length.

Journal Reference :

• Robert W. Epps, Amanda A. Volk, Kristofer G. Reyes, Milad Abolhasani. Accelerated AI development for autonomous materials synthesis in flow . Chemical Science , 2021; DOI: 10.1039/D0SC06463G

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AI for chemistry

A concise state of the art.

Introduction

Artificial Intelligence (AI) is being used more and more by chemists to perform various tasks. Originally, research in AI applied to chemistry has largely been fueled by the need to accelerate drug discovery and reduce its huge costs and the time to market for new drugs. So far, AI has made significant progess towards the acceleration of drug discovery R&D . However, the applications of AI in chemistry are not limited to drug discovery, as discussed in a recent review . In this article, we will provide a general picture of how AI can help chemists be faster and more creative in their research.

Molecule property prediction

When scientists design new molecules for a certain application, they have to synthesize them to check experimentally that they possess the right properties. If they don't, the scientists design new molecules (that can be analogs of the previously synthesized molecules, for example), and iterate until they obtain molecules that satisfy their requirements (properties, performance, price, toxicity, environmental impact, etc.). This iterative process takes a lot of time and money.

Being able to accurately predict the properties of hypothetical molecules would allow researchers to synthesize only the most promising ones and to avoid synthesizing and testing many molecules that don't possess the desired properties. Methods of prediction of molecule properties have been used for a long time, often under the name of Quantitative Structure-Activity Relationships (QSAR) or Quantitative Structure-Property Relationships (QSPR). These methods are usually based on physical laws or empirical relationships relating the structure of molecules (often indirectly, via a set of chosen descriptors) to their properties.

The prediction of molecular properties can also be done using machine learning algorithms. These algorithms have been used to predict properties such as bioactivity, toxicity, solubility, melting points, atomization energies, HOMO/LUMO molecular orbital energies and many other kinds of properties. They are neither based on physical laws nor on manually crafted empirical relationships: they are entirely data-driven. Basically, these AI algorithms are trained by feeding them many examples of molecules and their associated properties (supervised learning). Different regression or classification algorithms can be used, such as linear regression, Support Vector Machines, Random Forests or Neural Networks.

AI-based algorithms are particularely well-suited to problems for which the physical laws that determine the molecular properties to be predicted are not exactly known, or when empirical relationships would be too complicated to establish, eg. because of strong non-linearities or correlations between parameters. Interestingly, AI can be used in combination with other prediction methods such as physical equations or empirical relationships, in order to obtain predictions that are even more accurate. For example, AI can use the results of predictions made by physical or empirical relationships as input data, a technique called stacking .

Molecule design

Designing new molecules is one of the highest value-added tasks that chemists perform. They usually use their chemistry knowledge, their domain knowledge and their creativity to propose new molecular structures that are then tested either virtually ( in silico ) or experimentally in the relevant applications. There are two main limitations to this method. The first limitation is that human creativity is inherently biased: a chemist may prefer certain functional groups or molecular patterns (consciously or not), and might exclude molecules that he/she finds strange or does not like. The second limitation is that when large numbers of new molecules (hundred of thousands or even millions) are needed for virtual screening, the human mind is not capable of designing such large sets of molecules in a reasonable time.

Artificial Intelligence has emerged as a valuable complement to the human creativity for designing new molecules, as discussed in two reviews . It is less biased than human creativity (although biases in the training datasets induce biases in the set of generated molecules) and can generate huge numbers of molecules in short times.

Several deep learning algorithms have been used to design new molecules: variational autoencoders, adversarial autoencoders, recurrent neural networks and graph convolutional networks. These algorithms generate molecular structures either as SMILES strings or directly as graphs, a more recent technique. These deep learning algorithms need to be trained on large numbers of molecules, typically millions, to construct a statistical distribution of the molecules. Several datasets containing millions of molecules are freely available, such as the ZINC database , the QM9 dataset of the ChEMBL database and can be used as training datasets. When generating SMILES strings, it is necessary to check the validity of the generated strings in order to eliminate invalid SMILES. In addition, when using this kind of algorithms, one should verify that the generated molecules are effectively different from the molecules of the training dataset and measure the chemical diversity of the generated molecules (how different they are from known molecules).

After the deep learning algorithm has been trained, it can generate large numbers of new molecules by sampling the learned statistical distribution, generally near molecules of interest (eg. molecules that are known to possess desired properties). Then, the designed molecules can either be synthesized or used for virtual screening, (eg. with docking software or AI-based software) to predict their properties and select only the more interesting ones for further development. Alternatively, if instead of obtaining large numbers of molecules one prefers to obtain small numbers of more precisely designed molecules, techniques such as Bayesian optimization can be used to iteratively search for molecules that satisfy some property requirements.

To have a better idea about how these molecular design algorithms work and can be used, read our two blog posts on that subject.

Retrosynthesis

Imagine that you have identified a promising molecule and you want to synthesize it. You will start by performing a retrosynthesis: going backwards from the target molecule to commercially available starting materials in one or several reaction steps. This time-consuming and uncertain retrosynthesis process is most of the time done manually by chemists. There have been many attempts at using computer programs for doing retrosynthesis in the last decades, but these computer-assisted approaches have yielded retrosyntheses of lower quality than the ones that are done by expert chemists.

However, recently, several high-performance machine learning-based algorithms have been developed to assist chemists doing retrosynthesis. These complex deep learning algorithms have been trained on very large numbers of organic reactions, typically millions or tens of millions. The user inputs the molecule that he/she wants to synthesize into the program, and the machine learning algorithm generates a retrosynthesis tree: each node of the tree is a synthesis intermediate and each leaf of the tree is a commercially available starting material.

One of these newly developed machine learning algorithms has been proven to be as performant in making retrosyntheses as experienced chemists, as evaluated with a double-blind comparison of AI-generated retrosyntheses with human-generated retrosyntheses. This algorithm uses a combination of Monte Carlo tree search and deep learning algorithms.

There are limitations to the current machine learning-based retrosynthesis algorithms. First, they work well for rather simple molecules, but not very well for natural products, due to the lower number of natural product syntheses in the literature that is available to train machine learning models. Second, since the training datasets are based on literature reactions and the literature contains much unreliable data, one can question the validity of the proposed retrosyntheses. Third, these algorithms are not yet capable of reliably predicting the sterochemistry of reaction products. Overall, these AI-based retrosynthesis tool are already very valuable for organic chemists, as they provide good starting points, but their results should be checked by experienced chemists before starting to perform syntheses in the laboratory.

Reaction outcome prediction

When you have identified the molecule that you want to synthesize and done its retrosynthesis (eg. by using an AI-based retrosynthesis program, as described in the part above), you may want to validate that each one of the synthesis steps is actually feasible.

To do that, you could use AI. Machine learning programs have been trained with databases of millions of organic reactions to predict reaction outcomes. These programs are based on graph-convolutional neural networks or recurrent neural networks. They work the following way: the reactants are given to the program, and the program predicts the main reaction product and several other possible products, which are often byproducts of the reaction. One limitation of the current programs is that they do not take into account the process conditions, including temperature, for their predictions.

If you want to learn more about reaction outcome prediction, you can read our blog post on that subject .

Reaction conditions prediction

Once you have identified a reaction that you want to run, you still have to find suitable reaction conditions (solvent(s), catalyst, reagent(s), temperature, concentrations, reaction time, purification method). You can make a first guess by using your experience and intuition, and you can also search in the literature for similar reactions and use the same conditions for your first synthesis attempts in the laboratory. But first guesses often don't yield the expected results, and finding the conditions that give a high product yield can take dozens or hundreds of experiments.

In a recent blog post , we have analyzed a publication that describes a machine learning-based program that was trained to predict reaction conditions for any organic reaction. This program does a pretty good job predicting solvent(s), catalyst, reagent(s) and the temperature. One small limitation of that particular program is that it doesn't predict concentrations and reaction times.

Chemical reaction optimization

Once you have determined what molecule you want to synthesize and which reaction steps you want to use to synthesize it, the next step is to go in the laboratory and experimentally search for synthesis conditions. For example, you want to know the temperature, concentrations, quantity of catalyst and reaction time that will lead to the desired molecule in the highest possible yield. This serach for optimal reaction conditions can be very long and require many synthesis attempts, especially if there are many different parameters to control and that you change only one of them at each experiment ("one variable at a time" method).

This reaction optimization process can be automatized, as we have discussed in a blog post . In the article in question, the authors have used a recurrent neural network to iteratively search for the best reaction conditions for chemical reactions and have shown that this deep learning model was quicker at finding optimal solutions than existing optimization algorithms or "one variable at a time" optimization. Let's note that AI-based methods have the advantage to change several parameters at the same time in order to quickly find an optimal solution.

Apart from yield, many other targets can be chosen: selectivity, productivity, environmental/energetic efficiency, the quantity of some byproduct, etc. Depending on your needs, you can choose to maximize their value, to minimize them or to aim for some specific value.

Machine learning-based tools are now capable of helping scientists design new molecules and synthesize them. In many cases, these tools reach impressive performance and boost chemists' productivity. We can expect that researchers will improve them a lot in the next few years and that these tools will become essential for chemists.

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Solve any chemistry problem ASAP with Chem AI, a step-by-step chemistry solver app! Download now for FREE. Got a tricky chemistry question? Need instant step-by-step explanations? Chem AI is here to save you. We got you covered for multiple choice, word, diagram, math, or any type of chemistry problem. Just upload or take a photo of your chemistry problems and Chem AI instantly recognizes the problem and helps you solve it with a thorough explanation. KEY FEATURES - Upload or take a photo of a handwritten or digital chemistry problem. - Copy and share the detailed solutions. - Topics cover all types of chemistry, from high school to doctorate research. - Math solutions are provided with detailed steps. - We provide a free trial to test out the app. By using Chem AI, you accept our Terms of Use and Privacy Policy. Terms of use: https://cuffmediagroup.com/terms-conditions Privacy Policy: https://cuffmediagroup.com/privacy-policy

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[Question] MLC-LLM (nightly-built) cannot be used because it conflicts with the numpy.How to solve it? #2063

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Is Your AI-First Strategy Causing More Problems Than It’s Solving?

• Oguz A. Acar

Consider a more balanced and thoughtful approach to AI transformation.

The problem with an AI-first strategy lies not within the “AI” but with the notion that it should come “first” aspect. An AI-first approach can be myopic, potentially leading us to overlook the true purpose of technology: to serve and enhance human endeavors. Instead, the author recommends following 3Ps during an AI transformation: problem-centric, people-first, and principle-driven.

From technology giants like Google to major management consultants like McKinsey , a rapidly growing number of companies preach an “AI-first” strategy. In essence, this means considering AI as the ultimate strategic priority , one that precedes other alternative directions. At first glance, this strategy seems logical, perhaps even inevitable. The figures speak for themselves: the sheer volume of investment flowing into AI technologies shows the confidence levels in an increasingly AI-driven future.

• Oguz A. Acar is a Chair in Marketing at King’s Business School, King’s College London.

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Bill Gates says AI won't magically solve problems that humans aren't already good at

• Bill Gates is one of AI's most enthusiastic proponents  but is wary of the technology's limitations.
• While AI is remarkably effective at some tasks, its weaknesses are becoming clearer.
• Gates said the key to current AI models is to have "data that embodies the expertise."

Bill Gates has been thinking about AI since long before it existed.

The Microsoft cofounder is now one of the leading advocates for the technology and is rolling up his sleeves to join meetings with his former company to advise them on product development.

Yet, for all the promise and success of the past year, there is also a growing awareness of AI's limitations .

In an interview with Dax Shepard on the Armchair Expert podcast, Gates said he's experiencing those strengths and weaknesses firsthand: "I'm using it all the time and saying, 'OK, no, it's not good enough for this — but wow, it is good enough for that.'"

AI has proven to be a game changer in several areas, including pharmaceuticals and agriculture, that Gates and his eponymous foundation are particularly concerned with.

But he's less impressed by AI's sudoku abilities — he said it struggles with recursive reasoning — and is still trying to figure out how to integrate the tools into his own work process.

"If it's a problem that humans are not good at dealing with, then present techniques don't create some novel approach," Gates said.

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In other words, despite appearances, current AI models aren't magic — they're just a lot faster at performing well-documented tasks that humans do more slowly.

The key is to have "data that embodies the expertise."

Experts know with a high degree of detail how nitrogen levels affect crop yields and are extremely good at measuring those. AI is a phenomenal help with improving forecasting models there.

By contrast, questions such as human happiness or relationship satisfaction are extremely difficult to quantify and predict, and humans have struggled with these questions with highly variable degrees of success throughout history. AI won't somehow solve those for us on its own.

"I actually think in mental counseling AIs will play a role, but boy, we're going to have to be very careful about that," Gates said. "That's going to require a lot of work that has not been done yet."

Another surprising area where Gates said current AI models stumble is complex math.

"It doesn't know to check its answers," he said. "A sudoku puzzle, you have to do a lot of recursive reasoning and it doesn't know to take extra time."

In a remarkably human way, the AI will sometimes say it "mistyped" rather than admit it was wrong, Gates added. "It's so apologetic, and it says it'll try again, but of course it gets it wrong again."

Ever the optimist, Gates said these limitations aren't a reason to put the AI genie back in the bottle. The potential upside for humanity is simply too great, even if it might take more work to get there.

While he respects individuals' and nations' right to disagree , Gates doesn't foresee a future in which the world collectively agrees to pause or roll back the development of this technology.

"Maybe we'll have a world where parts of the world have chosen to use AI in full, and other people are more like, 'Get your buggy out, baby,'" he said.

Watch: Microsoft CEO unravels ChatGPT, ethical AI, and going bust

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