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 मार्च 2018

Accepted

27 मार्च 2018

First published

25 अप्रैल 2018

Download Citation

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

Permissions

Request permissions

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

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Molecular Systems Design & Engineering

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

Abstract

Article information

Download Citation

Permissions

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

Social activity

Search articles by author

Spotlight

Advertisements