Entropy-based active learning of graph neural network surrogate models for materials properties-Reference-Cited by-同舟云学术

Entropy-based active learning of graph neural network surrogate models for materials properties

Published:2021-11-07 Issue:17 Volume:155 Page:174116
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Allotey Johannes¹^ORCID,Butler Keith T.²^ORCID,Thiyagalingam Jeyan²^ORCID

Affiliation:

1. School of Physics, University of Bristol, Bristol BS8 1TL, United Kingdom

2. Scientific Machine Learning Research Group, Scientific Computing Department, Rutherford Appleton Laboratory, Science and Technology Facilities Council, Didcot OX11 0DQ, United Kingdom

Funder

Engineering and Physical Sciences Research Council

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0065694

Reference54 articles.

1. Machine learning for molecular and materials science

2. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

3. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

4. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery

5. Transfer learning for materials informatics using crystal graph convolutional neural network

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