Representing molecular and materials data for unsupervised machine learning-Reference-Cited by-同舟云学术

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Representing molecular and materials data for unsupervised machine learning

Published:2018-04-02 Issue:11 Volume:44 Page:905-920
ISSN:0892-7022
Container-title:Molecular Simulation
language:en
Short-container-title:Molecular Simulation

Author:

Swann E.¹,Sun B.¹,Cleland D. M.¹,Barnard A. S.¹

Affiliation:

1. Molecular and Materials Modelling, Data61 CSIRO , Docklands, Victoria, Australia.

Funder

National Computational Infrastructure

Publisher

Informa UK Limited

Subject

Condensed Matter Physics,General Materials Science,General Chemical Engineering,Modelling and Simulation,Information Systems,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/08927022.2018.1450982

Reference134 articles.

1. MoleculeNet: a benchmark for molecular machine learning

2. Machine learning in materials informatics: recent applications and prospects

3. Atomistic calculations and materials informatics: A review

4. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

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