Machine Learning in Chemistry : the impact of artificial intelligence / edited by Hugh M. Cartwright.

• Computers as Scientists / Timothy E. H. Allen -- How Do Machines Learn? / Timothy E. H. Allen -- MedChemInformatics: An Introduction to Machine Learning for Drug Discovery / Matthew G. Roberts and Rae Lawrence -- Machine Learning for Nonadiabatic Molecular Dynamics / Julia Westermayr and Philipp Marquetand -- Machine Learning in Science : A Role for Mechanical Sympathy? / Hugh M. Cartwright -- A Prediction of Future States: AI-powered Chemical Innovation for Defense Applications / Tyler Stukenbroeker and Jonathan Clausen -- Machine Learning for Chemical Synthesis / Alexe L. Haywood, Joseph Redshaw, Thomas Gaertner, Adam Taylor, Andy M. Mason and Jonathan D. Hirst -- Constraining Chemical Networks in Astrochemistry / Serena Viti and Jonathan Holdship -- Machine Learning at the (Nano)materials-biology Interface / David A. Winkler -- Machine Learning Techniques Applied to a Complex Polymerization Process / Silvia Curteanu -- Machine Learning and Scoring Functions (SFs) for Molecular Drug Discovery: Prediction and Characterisation of Druggable Drugs and Targets / I. L. Hudson, S. Y. Leemaqz and A. D. Abell -- Artificial Intelligence Applied to the Prediction of Organic Materials / Steven Bennett, Andrew Tarzia, Martijn A. Zwijnenburg and Kim E. Jelfs -- A New Era of Inorganic Materials Discovery Powered by Data Science / Ya Zhuo, Aria Mansouri Tehrani and Jakoah Brgoch -- Machine Learning Applications in Chemical Engineering / Y. Yan, T. N. Borhani and P. T. Clough -- Representation Learning in Chemistry / Joshua Staker, Gabriel Marques and J. Dakka -- Demystifying Artificial Neural Networks as Generators of New Chemical Knowledge: Antimalarial Drug Discovery as a Case Study / Alejandro Speck-Planche and Valeria V. Kleandrova -- Machine Learning for Core-loss Spectrum / T. Mizoguchi and S. Kiyohara -- Autonomous Science: Big Data Tools for Small Data Problems in Chemistry / Andreas C. Geiger, Ziyi Cao, Zhengtian Song, James R. W. Ulcickas and Garth J. Simpson -- Machine Learning for Heterogeneous Catalysis: Global Neural Network Potential from Construction to Applications / Sicong Ma, Pei-Lin Kang, Cheng Shang and Zhi-Pan Liu -- A Few Guiding Principles for Practical Applications of Machine Learning to Chemistry and Materials / S. Shankar and R. N. Zare. 

• Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. 

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Maskininlärning  (sao)

Machine learning.  (LCSH)

Chemistry  -- Data processing. (LCSH)

Machine learning  (LCSH)

Chemistry  (LCSH)

Data processing  (LCSH)

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