Machine Learning in Molecular Sciences

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About this book

Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences.

Keywords

• Machine Learning
• Molecular Sciences
• Deep Learning
• Artificial Intelligence
• Graph Neural Networks
• Voxelized Representations
• Electronic Structure Theory
• Density Functional Theory
• Potential Energy Surface
• Chemical Kinetics
• Organic Synthesis
• Protein Engineering

Table of contents (9 chapters)

Front Matter

An Introduction to Machine Learning in Molecular Sciences

• Hanchao Liu, Chen Qu

Graph Neural Networks for Molecules

• Yuyang Wang, Zijie Li, Amir Barati Farimani

Voxelized Representations of Atomic Systems for Machine Learning Applications

• Matthew C. Barry, Satish Kumar, Surya R. Kalidindi

Development of Exchange-Correlation Functionals Assisted by Machine Learning

• Ryo Nagai, Ryosuke Akashi

Machine-Learning for Static and Dynamic Electronic Structure Theory

• Lenz Fiedler, Karan Shah, Attila Cangi

Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-Dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions

• Jun Li, Yang Liu

Machine Learning Applications in Chemical Kinetics and Thermochemistry

• Lung-Yi Chen, Yi-Pei Li

Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis

• Dian-Zhao Lin, Guichun Fang, Kuangbiao Liao

Machine Learning for Protein Engineering

• Kadina E. Johnston, Clara Fannjiang, Bruce J. Wittmann, Brian L. Hie, Kevin K. Yang, Zachary Wu

Back Matter

Editors and Affiliations

Independent Researcher, Toronto, Canada

Google Inc, Mountain View, USA

About the editors

Chen Qu is currently a research associate of National Institute of Standards and Technology. His current research focuses on applying machine learning methods to predict important chemical properties such as gas chromatography retention indices and mass spectra. He received his Ph.D. at Emory University, where he conducted research primarily on machine learning potential energy surfaces, under the guidance of Prof. Joel Bowman.

Hanchao Liu is currently a machine learning engineer at Google. His work focuses on building large-scale machine learning infrastructures and platforms. Dr. Liu received his Ph.D. in computational chemistry at Emory University under the tutelage of Prof. Joel Bowman, where he applied computational and machine learning methods to study the vibrational dynamics and spectra of various forms of water.

Bibliographic Information

• Book Title : Machine Learning in Molecular Sciences
• Editors : Chen Qu, Hanchao Liu
• Series Title : Challenges and Advances in Computational Chemistry and Physics
• DOI : https://doi.org/10.1007/978-3-031-37196-7
• Publisher : Springer Cham
• eBook Packages : Chemistry and Materials Science , Chemistry and Material Science (R0)
• Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
• Hardcover ISBN : 978-3-031-37195-0 Published: 02 October 2023
• Softcover ISBN : 978-3-031-37198-1 Due: 15 October 2024
• eBook ISBN : 978-3-031-37196-7 Published: 01 October 2023
• Series ISSN : 2542-4491
• Series E-ISSN : 2542-4483
• Edition Number : 1
• Number of Pages : X, 317
• Number of Illustrations : 6 b/w illustrations, 74 illustrations in colour
• Topics : Machine Learning , Artificial Intelligence , Life Sciences, general , Theoretical and Computational Chemistry , Computer Applications in Chemistry , Bioinformatics