Machine Learning in Chemistry

Data-Driven Algorithms, Learning Systems, and Predictions

Edward O. Pyzer-Knapp, Teodoro Laino (Herausgeber)

Buch | Hardcover

140 Seiten

2020
Oxford University Press Inc (Verlag)
978-0-8412-3505-2 (ISBN)

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This volume serves as a basis for future development in the field of artificial intelligence and its application to cheminformatics, physical chemistry, and computational chemistry.

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.

Edward O. Pyzer Knapp is the lead for AI and Machine Learning for IBM Research in the United Kingdom. Teodoro Laino is a principal research staff member in the Department of Cognitive Computing and Industry Solutionat IBM Research Laboratory in Zurich, Switzerland.

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

Erscheinungsdatum	17.04.2020
Reihe/Serie	ACS Symposium Series
Verlagsort	New York
Sprache	englisch
Maße	183 x 262 mm
Gewicht	518 g
Themenwelt	Naturwissenschaften ► Chemie ► Physikalische Chemie
	Naturwissenschaften ► Chemie ► Technische Chemie
	Technik
ISBN-10	0-8412-3505-8 / 0841235058
ISBN-13	978-0-8412-3505-2 / 9780841235052
Zustand	Neuware