Machine learning in chemistry : data-driven algorithms, learning systems, and predictions /

Bibliographic Details
Corporate Author:	American Chemical Society. Division of Computers in Chemistry (sponsoring body.)
Other Authors:	Pyzer-Knapp, Edward O. (Editor), Laino, Teodoro (Editor)
Format:	eBook
Language:	English
Published:	Washington, DC : American Chemical Society, 2019.
Series:	ACS symposium series ; 1326.
Subjects:	Machine learning. Chemisty > Computer programs. Chemistry > Computer simulation. Statistical hypothesis testing > Computer programs. Chemistry > Statistical methods > Computer programs. Chemistry > Molecular aspects > Statistical methods > Computer programs. Hardness > Forecasting > Computer programs. Neural networks (Computer science) Chemical reactions > Forecasting > Computer simulation. Drugs > Research > Computer simulation. Paradigm (Theory of knowledge) > Computer simulation. Learning by discovery > Computer simulation. Machine Learning. Chemistry > education. Statistics as Topic. Hardness. Neural Networks, Computer. Pharmaceutical Preparations > analysis. Computer Simulation. SCIENCE / Chemistry / Computational & Molecular Modeling. Electronic books. COMPUTERS / Data Science / Machine Learning.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Atomic-Scale Representation and Statistical Learning of Tensorial Properties / Grisafi, Andrea; Wilkins, David M.; Willatt, Michael J.; Ceriotti, Michele / http://dx.doi.org/10.1021/bk-2019-1326.ch001
Prediction of Mohs Hardness with Machine Learning Methods Using Compositional Features / Garnett, Joy C. / http://dx.doi.org/10.1021/bk-2019-1326.ch002
High-Dimensional Neural Network Potentials for Atomistic Simulations / Hellström, Matti, Software for Chemistry & Materials BV, De Boelelaan 1083, 1081HV Amsterdam, The Netherlands; Behler, Jörg, Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstrasse 6, 37077 Göttingen, Germany / http://dx.doi.org/10.1021/bk-2019-1326.ch003
Data-Driven Learning Systems for Chemical Reaction Prediction: An Analysis of Recent Approaches / Schwaller, Philippe, IBM Research – Zurich, Rueschlikon 8803, Switzerland, Department of Chemistry and Biochemistry, University of Berne, Berne 3012, Switzerland; Laino, Teodoro, IBM Research – Zurich, Rueschlikon 8803, Switzerland / http://dx.doi.org/10.1021/bk-2019-1326.ch004
Using Machine Learning To Inform Decisions in Drug Discovery: An Industry Perspective / Green, Darren V. S. / http://dx.doi.org/10.1021/bk-2019-1326.ch005
Cognitive Materials Discovery and Onset of the 5th Discovery Paradigm / Zubarev, Dmitry Y.; Pitera, Jed W. / http://dx.doi.org/10.1021/bk-2019-1326.ch006
Editors’ Biographies / http://dx.doi.org/10.1021/bk-2019-1326.ot001