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Full Description
Integrate AI into chemical research through structured tutorials and exercises
Chemists adopting AI methods need discipline-specific training beyond generic introductions. Artificial Intelligence in Chemistry and Chemical Engineering: From Basics to Practical Exercises provides a step-by-step tutorial guiding researchers through AI, automation, data science, and cheminformatics. Progressing from foundational concepts to advanced applications, hands-on exercises and real-world case studies enable direct application to ongoing research programs.
Coverage spans AI reaction prediction models, automated high-throughput synthesis platforms, and chemical reaction big data systems. The book addresses ethical implications and regulatory considerations for AI deployment in chemistry.
Readers will also find:
Foundational machine learning concepts tailored specifically for practitioners working in chemistry and chemical engineering research disciplines
Detailed tutorials on building and utilizing chemical reaction big data systems for accelerating discovery and optimizing workflows
Real-world case studies demonstrating how AI-driven approaches solve specific challenges in organic synthesis and molecular science
Discussion of potential misuse scenarios and regulatory frameworks to navigate responsible AI integration in laboratory settings
Practical guidance on constructing next-generation automated high-throughput synthesis platforms for efficient experimental design and execution
Designed for organic, physical, theoretical, medicinal, analytical, pharmaceutical, and environmental chemists, as well as materials scientists, chemical engineers, and computer scientists, this book delivers the structured training required to apply AI methods directly to chemical research and industrial practice.
Contents
Preface xxiii
1 Introduction to AI in Chemistry: Landscape, Motivation, and Scientific Workflow 1
Lin Tan
1.1 Foundations of Artificial Intelligence: From Ideas to Infrastructure 1
1.2 Why Chemistry Needs AI: Scale, Data, and Decisions 8
1.3 The AI-Chemistry Landscape: What AI Enables Across Chemical Subdisciplines 10
1.4 From Prediction to Action: Closed-Loop Chemistry and Human-AI Collaboration 15
1.5 Trends, Frontiers, and Outlook: Toward Intelligent Chemical Science 18
2 Fundamentals of AI and Machine Learning 21
Chonghuan Zhang
2.1 Introduction to AI and Machine Learning 21
2.2 Neural Networks and Deep Learning 41
2.3 Large Language Models in Chemistry 63
2.4 Practical Applications in Chemistry 79
2.5 Integration with Automation and Robotics 87
2.6 Ethical and Regulatory Considerations 90
3 Data Science for Chemists 97
Chonghuan Zhang
3.1 Introduction to Data Science 97
3.2 Chemical Databases and Resources 104
3.3 Advanced Data Science Techniques 115
3.4 Data Management and Ethics 123
4 Cheminformatics 131
Chonghuan Zhang
4.1 Overview of Cheminformatics 131
4.2 Molecular Representations and Descriptors 134
4.3 Feature Selection and Extraction 150
4.4 Examples and Case Studies 162
5 Integrating Automation Tools in Chemical Research 173
Kai Xue
5.1 Automation Hardware and Software 173
5.2 Practical Integration 181
5.3 Future of Automation in Chemistry 186
6 Foundations of Predictive Modeling in Chemistry 193
Chonghuan Zhang
6.1 Introduction to Predictive Modeling 193
6.2 Theoretical Foundations and Model Mapping 194
6.3 Data Preparation and Preprocessing 204
6.4 Model Building and Validation 216
6.5 Advanced Techniques and Tools 230
6.6 Tools and Software for Predictive Modeling 235
6.7 Future Directions and Challenges 243
7 AI in Chemical Synthesis, Process Optimization, and Automation 249
Chonghuan Zhang
7.1 Reaction Prediction 249
7.2 Process Optimization 260
7.3 Case Studies and Real-World Applications 268
7.4 Future Directions and Challenges 284
8 Molecular Modeling and Design 289
Chonghuan Zhang
8.1 Basics and Importance 289
8.2 Techniques 290
8.3 Machine Learning in Molecular Modeling 293
8.4 Applications in Drug Discovery 294
8.5 Molecular Docking 296
8.6 De Novo Design 297
8.7 Conclusions 299
9 Materials Discovery and Design 301
Chonghuan Zhang
9.1 Applications of Artificial Intelligence in Materials Science 301
9.2 Successful Examples 314
10 Analytical Chemistry 329
Qianghua Lin
10.1 AI for Data Interpretation 329
10.2 Spectroscopy and Chromatography 334
10.3 Challenges and Future Perspectives 357
10.4 Conclusions 358
11 Environmental Chemistry 367
Qianghua Lin
11.1 Pollution Monitoring 367
11.2 Green Chemistry 377
11.3 Conclusion 381
12 AI in Chemical Engineering 385
Chonghuan Zhang
12.1 Introduction to AI in Chemical Engineering 385
12.2 Process Design and Optimization: From Laboratory to Production Facility 389
12.3 Control Systems and Automation: From Feedback to Foresight 397
12.4 Predictive Maintenance and Intelligent Scheduling 405
12.5 AI in Supply Chain Management 407
12.6 Sustainability and Environmental Impact 411
12.7 Case Review and Real-World Applications 414
12.8 Future Directions and Challenges 415
13 AI in Chemical Education and Training 421
Chan Zhu
13.1 AI for Personalized Learning 421
13.2 AI in Intelligent Tutoring and Experimental Instruction 422
13.3 AI for Automated Assessment and Learning Feedback 422
13.4 AI in Collaborative Learning and Research Training 423
13.5 AI in Curriculum Development and Educational Resource Optimization 424
13.6 Summary and Future Perspectives 424
14 Practical Exercises and Projects 427
Chonghuan Zhang
14.1 The Foundation of Chemical Data Analysis: From Cleaning to Visualization 427
14.2 Application of Machine Learning Algorithms 434
14.3 Reaction Condition Optimization: From Prediction to Experimental Guidance 442
14.4 Application of Language Models in Organic Synthesis: Substrate Prediction Practice 452
14.5 Case Study: Predicting Amide Coupling Yields Using Intermediate Knowledge 458
14.6 Lessons Learned, Best Practices, and Continuous Development 462
15 Ethical Considerations and Best Practices in AI for Chemistry 467
Chonghuan Zhang
15.1 Ethical Guidelines for AI in Chemistry 467
15.2 Data Privacy and Security 472
15.3 Mitigating Malicious Use of AI in Chemistry 474
15.4 Best Practices for Ethical AI in Chemistry 475
15.5 Responsible Use and Ethical Governance of AI in Chemistry 477
15.6 AI Applications in Chemical Safety 480
15.7 Regulatory Considerations and Compliance System 484
15.8 Case Studies and Practical Applications 486
15.9 Future Directions and Challenges 490
References 492
Index 497
