Jump to main content
Jump to site search
Access to RSC content Close the message box

Continue to access RSC content when you are not at your institution. Follow our step-by-step guide.



Machine Learning Driven New Material Discovery

Abstract

New materials can bring tremendous technology and application progress. But now the commonly used trierand-error method can’t meet the need today. Now, a newly proposed idea using machine learning to explore new materials is becoming popular. In this paper, we review this research paradigm of applying machine learning in material discovery, including data preprocessing, feature engineering, machine learning algorithms and cross-validation procedures. Furthermore, we propose to assist traditional DFT calculation with this idea for material discovery. Many experiments and literatures have shown the great effect and prospect of this idea. It’s now showing its potential and advantages in property prediction, material discovery, inverse design, corrosion detection and many other aspects in life.

Back to tab navigation

Article information


Submitted
13 May 2020
Accepted
22 Jun 2020
First published
22 Jun 2020

This article is Open Access

Nanoscale Adv., 2020, Accepted Manuscript
Article type
Review Article

Machine Learning Driven New Material Discovery

J. Cai, X. Chu, K. Xu, H. Li and J. Wei, Nanoscale Adv., 2020, Accepted Manuscript , DOI: 10.1039/D0NA00388C

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material and it is not used for commercial purposes.

Reproduced material should be attributed as follows:

  • For reproduction of material from NJC:
    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the Centre National de la Recherche Scientifique (CNRS) and the RSC.
  • For reproduction of material from PCCP:
    [Original citation] - Published by the PCCP Owner Societies.
  • For reproduction of material from PPS:
    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the European Society for Photobiology, the European Photochemistry Association, and RSC.
  • For reproduction of material from all other RSC journals:
    [Original citation] - Published by The Royal Society of Chemistry.

Information about reproducing material from RSC articles with different licences is available on our Permission Requests page.


Social activity

Search articles by author

Spotlight

Advertisements