Machine learning in chemistry : data-driven algorithms, learning systems, and predictions
Author(s)
Bibliographic Information
Machine learning in chemistry : data-driven algorithms, learning systems, and predictions
（ACS symposium series, 1326）
American Chemical Society, c2019
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Includes bibliographical references and indexes
Description and Table of Contents
Description
Artificial intelligence, and especially its application to chemistry, is an exciting and rapidly expanding area of research. This volume presents groundbreaking work in this field to facilitate researcher engagement and to serve as a solid base from which new researchers can break into this exciting and rapidly transforming field. This interdisciplinary volume will be a valuable tool for those working in cheminformatics, physical chemistry, and computational
chemistry.
Table of Contents
Preface
Chapter 1. Atomic-Scale Representation and Statistical Learning of Tensorial Properties, Andrea Grisafi, David M. Wilkins, Michael J. Willatt, and Michele Ceriotti
Chapter 2. Prediction of Mohs Hardness with Machine Learning Methods Using Compositional Features, Joy C. Garnett
Chapter 3. High-Dimensional Neural Network Potentials for Atomistic Simulations, Matti Hellstrom and Jorg Behler
Chapter 4. Data-Driven Learning Systems for Chemical Reaction Prediction: An Analysis of Recent Approaches, Philippe Schwaller and Teodoro Laino
Chapter 5. Using Machine Learning To Inform Decisions in Drug Discovery: An Industry Perspective, Darren V. S. Green
Chapter 6. Cognitive Materials Discovery and Onset of the 5th Discovery Paradigm, Dmitry Y. Zubarev and Jed W. Pitera
Editors' Biographies
Author Index
Subject Index
by "Nielsen BookData"
