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Machine learning in materials science / Keith T. Butler, Felipe Oviedo & Pieremanuele Canepa.

By: Butler, Keith T [author.].
Contributor(s): Oviedo, Felipe. Microsoft AI For Good and Massachusetts Institute of Technology [author.] | Canepa, Pieremanuele. National University of Singapore [author.] | American Chemical Society.
Material type: materialTypeLabelBookSeries: ACS in focus: Publisher: Washington, DC, USA : American Chemical Society, 2022Description: 1 online resource : illustrations (some color).Content type: text Media type: computer Carrier type: online resourceISBN: 9780841299467.Subject(s): Materials -- Data processing | Materials science -- Mathematical models | Machine learning -- Industrial applicationsDDC classification: 620.11 Online resources: Click here to access online
Contents:
Applying Machine Learning to Materials Science -- Building Trust in Machine Learning -- Machine Learning for Materials Simulations -- Analyzing Experimental Data -- Closed-Loop Optimization and Active Learning for Materials -- Discovering New Materials -- Coda.
Summary: " Machine Learning for Materials Science provides the fundamentals and useful insight into where Machine Learning (ML) will have the greatest impact for the materials science researcher. This digital primer provides example methods for ML applied to experiments and simulations, including the early stages of building an ML solution for a materials science problem, concentrating on where and how to get data and some of the considerations when choosing an approach. The authors demonstrate how to build more robust models, how to make sure that your colleagues trust the results, and how to use ML to accelerate or augment simulations, by introducing methods in which ML can be applied to analyze and process experimental data. They also cover how to build integrated closed-loop experiments where ML is used to plan the course of a materials optimization experiment and how ML can be utilized in the discovery of materials on computers."-- Provided by publisher.
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Includes bibliographical references and index.

Applying Machine Learning to Materials Science -- Building Trust in Machine Learning -- Machine Learning for Materials Simulations -- Analyzing Experimental Data -- Closed-Loop Optimization and Active Learning for Materials -- Discovering New Materials -- Coda.

" Machine Learning for Materials Science provides the fundamentals and useful insight into where Machine Learning (ML) will have the greatest impact for the materials science researcher. This digital primer provides example methods for ML applied to experiments and simulations, including the early stages of building an ML solution for a materials science problem, concentrating on where and how to get data and some of the considerations when choosing an approach. The authors demonstrate how to build more robust models, how to make sure that your colleagues trust the results, and how to use ML to accelerate or augment simulations, by introducing methods in which ML can be applied to analyze and process experimental data. They also cover how to build integrated closed-loop experiments where ML is used to plan the course of a materials optimization experiment and how ML can be utilized in the discovery of materials on computers."-- Provided by publisher.

American Chemical Society, ACS In Focus eBooks - 2022 Front Files.

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