Machine learning and data science in materials design: a themed collection

Andrew Ferguson; Johannes Hachmann

doi:10.1039/C8ME90007H

Machine learning and data science in materials design: a themed collection

Andrew Ferguson

^abc and Johannes Hachmann

^def

Author affiliations

^a Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, 1304 West Green Street, Urbana, IL 61801, USA
E-mail: alf@illinois.edu
Fax: +1 217 333 2736
Tel: +1 217 300 2354

^b Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, 600 South Mathews Avenue, Urbana, IL 61801, USA

^c Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, IL 61801, USA

^d Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA
E-mail: hachmann@buffalo.edu

^e New York State Center of Excellence in Materials Informatics, Buffalo, NY 14203, USA

^f Computational and Data-Enabled Science and Engineering Graduate Program, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA

Abstract

Guest Editors Andrew Ferguson and Johannes Hachmann introduce this themed collection of papers showcasing the latest research leveraging data science and machine learning approaches to guide the understanding and design of hard, soft, and biological materials with tailored properties, function and behaviour.

This article is part of the themed collection: Machine Learning and Data Science in Materials Design

Article information

https://doi.org/10.1039/C8ME90007H

Article type

Editorial

Submitted

27 3 2018

Accepted

27 3 2018

First published

25 4 2018

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Mol. Syst. Des. Eng., 2018,3, 429-430

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Machine learning and data science in materials design: a themed collection

A. Ferguson and J. Hachmann, Mol. Syst. Des. Eng., 2018, 3, 429 DOI: 10.1039/C8ME90007H

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Molecular Systems Design & Engineering

Machine learning and data science in materials design: a themed collection

Abstract

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Machine learning and data science in materials design: a themed collection

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