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Machine Learning in Molecular Sciences

  • Book
  • © 2023

Overview

Editors:
  1. Chen Qu

    1. Independent Researcher, Toronto, Canada

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    You can also search for this editor in PubMed   Google Scholar

  2. Hanchao Liu

    1. Google Inc, Mountain View, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Comprehensive survey of machine learning in molecular sciences
  • Perspectives on challenges and future of machine learning in chemistry
  • Features contributions from experts in the field

Part of the book series: Challenges and Advances in Computational Chemistry and Physics (COCH, volume 36)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-x
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  2. An Introduction to Machine Learning in Molecular Sciences

    • Hanchao Liu, Chen Qu
    Pages 1-19

  3. Graph Neural Networks for Molecules

    • Yuyang Wang, Zijie Li, Amir Barati Farimani
    Pages 21-66

  4. Voxelized Representations of Atomic Systems for Machine Learning Applications

    • Matthew C. Barry, Satish Kumar, Surya R. Kalidindi
    Pages 67-89

  5. Development of Exchange-Correlation Functionals Assisted by Machine Learning

    • Ryo Nagai, Ryosuke Akashi
    Pages 91-112

  6. Machine-Learning for Static and Dynamic Electronic Structure Theory

    • Lenz Fiedler, Karan Shah, Attila Cangi
    Pages 113-160

  7. 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
    Pages 161-201

  8. Machine Learning Applications in Chemical Kinetics and Thermochemistry

    • Lung-Yi Chen, Yi-Pei Li
    Pages 203-226

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

    • Dian-Zhao Lin, Guichun Fang, Kuangbiao Liao
    Pages 227-275

  10. Machine Learning for Protein Engineering

    • Kadina E. Johnston, Clara Fannjiang, Bruce J. Wittmann, Brian L. Hie, Kevin K. Yang, Zachary Wu
    Pages 277-311

  11. Back Matter

    Pages 313-317
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Keywords

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.

Editors and Affiliations

  • Independent Researcher, Toronto, Canada

    Chen Qu

  • Google Inc, Mountain View, USA

    Hanchao Liu

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.

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