Tim Allen - Machine Learning in Chemistry: The Impact of Artificial Intelligence (Theoretical and Computational Chemistry Series): Volume 17

Machine Learning in Chemistry

The Impact of Artificial Intelligence

Theoretical and Computational Chemistry Series

Editor-in-chief:

Jonathan Hirst, University of Nottingham, Nottingham, UK

Advisory board:

Dongqing Wei, Shanghai Jiao Tong University, China

Jeremy Smith, Oakridge National Laboratory, USA

Titles in the series:

1: Knowledge-based Expert Systems in Chemistry: Not Counting on Computers

2: Non-Covalent Interactions: Theory and Experiment

3: Single-Ion Solvation: Experimental and Theoretical Approaches to Elusive Thermodynamic Quantities

4: Computational Nanoscience

5: Computational Quantum Chemistry: Molecular Structure and Properties in Silico

6: Reaction Rate Constant Computations: Theories and Applications

7: Theory of Molecular Collisions

8: In Silico Medicinal Chemistry: Computational Methods to Support Drug Design

9: Simulating Enzyme Reactivity: Computational Methods in Enzyme Catalysis

10: Computational Biophysics of Membrane Proteins

11: Cold Chemistry: Molecular Scattering and Reactivity Near Absolute Zero

12: Theoretical Chemistry for Electronic Excited States

13: Attosecond Molecular Dynamics

14: Self-organized Motion: Physicochemical Design based on Nonlinear Dynamics

15: Knowledge-based Expert Systems in Chemistry: Artificial Intelligence in Decision Making

16: London Dispersion Forces in Molecules, Solids, and Nano-structures: An Introduction to Physical Models and Computational Methods

17: Machine Learning in Chemistry: The Impact of Artificial Intelligence

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Machine Learning in Chemistry

The Impact of Artificial Intelligence

Edited by

Hugh M. Cartwright

Oxford University, UK

Email:

Theoretical and Computational Chemistry Series No. 17

Print ISBN: 978-1-78801-789-3

PDF ISBN: 978-1-83916-023-3

EPUB ISBN: 978-1-83916-024-0

Print ISSN: 2041-3181

Electronic ISSN: 2041-319X

A catalogue record for this book is available from the British Library

The Royal Society of Chemistry 2020

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Preface

Progress in science can be lumpy. Ground-breaking advances, such as the emergence of quantum theory, the development of spectroscopic techniques including IR and NMR, or the widespread adoption of computers in chemistry, catalyse research, and open new fields. Artificial intelligence (AI) is the latest significant advance.

Perhaps, this assessment of AI seems too generous. Can a computational technique really be transformational? After all, in every university chemistry department, there are computers and spectrometers, but in only some is AI used. However, there are signs that AI may have as great an impact in science as it promises to do in society more broadly; this book explores that impact.

We begin with an introduction to some of the core concepts in AI and machine learning, at a level suitable for those who are new to the field (Allen, Chapters 1 and 2). The tutorial that follows outlines some of the most widely used AI methods at the interface of machine learning and medicine (Lawrence, Chapter 3). Subsequent chapters deal with a wide range of topics, including fundamental studies in catalysis (Liu, Chapter 19) and prediction of the properties of materials (Winkler, Chapter 9; Jelfs, Chapter 12; Brgoch, Chapter 13); studies of synthesis design (Stukenbroeker, Chapter 6; Hirst, Chapter 7; Brgoch, Chapter 13); drug design (Hudson, Chapter 11; Speck-Planche, Chapter 16); industrial applications (Curteanu, Chapter 10; Clough, Chapter 14); theoretical areas of chemistry (Marquetand, Chapter 4; Mizoguchi, Chapter 17) and some more technical aspects of the use of AI (Staker, Chapter 15); autonomous chemistry (Stukenbroeker, Chapter 6; Simpson, Chapter 18); and chemical astronomy (Viti, Chapter 8). Several chapters (among them Stukenbroeker, Chapter 6; Hirst, Chapter 7; Brgoch, Chapter 13; Simpson, Chapter 18, and Shankar, Chapter 20) touch upon some of the difficulties that may complicate the use of AI in science and consider how one might circumvent them, while one further chapter (Cartwright, Chapter 5), which also discusses challenges and solutions when using AI in science, is aimed principally at newcomers to the field.

Chemistry research that takes advantage of AI is burgeoning: from a modest number of published papers in 2000, the publication rate had, by the end of 2019, risen 100-fold. Several factors have contributed to this growth:

• access to increasingly large volumes of data;
• a continuing rise in computer speed;
• improvements in the efficiency of AI software;
• the availability of innovative specialised chips, such as custom neural network chips;
• the re-purposing of graphics chips as fast surrogate CPUs for AI applications;
• the refinement of existing AI methods and the development of new techniques;