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

Edward O. Pyzer-Knapp, Teodoro Laino, Andrea Grisafi, David M. Wilkins, Michael J. Willatt, Michele Ceriotti, Joy C. Garnett, Matti Hellström, Jörg Behler, Philippe Schwaller, Teodoro Laino, Darren V. S. Green, Dmitry Y. Zubarev, Jed W. Pitera, Edward O. Pyzer-Knapp, Teodoro Laino, Edward O. Pyzer-Knapp, Teodoro Laino

• Publisher: American Chemical Society
• Year: 2019
• eBook ISBN:
  9780841235045
• Print ISBN:
  9780841235045

Table of contents

• Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions
• ACS Symposium Series1326
• Foreword
• Preface
• Atomic-Scale Representation and Statistical Learning of Tensorial Properties
• Prediction of Mohs Hardness with Machine Learning Methods Using Compositional Features
• High-Dimensional Neural Network Potentials for Atomistic Simulations
• Data-Driven Learning Systems for Chemical Reaction Prediction: An Analysis of Recent Approaches
• Using Machine Learning To Inform Decisions in Drug Discovery: An Industry Perspective
• Cognitive Materials Discovery and Onset of the 5th Discovery Paradigm
• Editors’ Biographies
• Indexes
• Author Index
• Subject Index
• Preface
• Atomic-Scale Representation and Statistical Learning of Tensorial Properties
• Prediction of Mohs Hardness with Machine Learning Methods Using Compositional Features
• High-Dimensional Neural Network Potentials for Atomistic Simulations
• Data-Driven Learning Systems for Chemical Reaction Prediction: An Analysis of Recent Approaches
• Using Machine Learning To Inform Decisions in Drug Discovery: An Industry Perspective
• Cognitive Materials Discovery and Onset of the 5th Discovery Paradigm
• Editors’ Biographies
• Indexes
• Author Index
• Subject Index