Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions

Title, Copyright, Foreword

Preface

• Edward O. Pyzer-Knapp
• Teodoro Laino

Atomic-Scale Representation and Statistical Learning of Tensorial Properties

• Andrea Grisafi
• David M. Wilkins
• Michael J. Willatt
• Michele Ceriotti

Prediction of Mohs Hardness with Machine Learning Methods Using Compositional Features

• Joy C. Garnett

High-Dimensional Neural Network Potentials for Atomistic Simulations

• Matti Hellström
• Jörg Behler

Data-Driven Learning Systems for Chemical Reaction Prediction: An Analysis of Recent Approaches

• Philippe Schwaller
• Teodoro Laino

Using Machine Learning To Inform Decisions in Drug Discovery: An Industry Perspective

• Darren V. S. Green

Cognitive Materials Discovery and Onset of the 5th Discovery Paradigm

• Dmitry Y. Zubarev
• Jed W. Pitera

Editors’ Biographies

Subject Index