Unified graph neural network force-field for the periodic table: solid state applications-Reference-Cited by-同舟云学术

Unified graph neural network force-field for the periodic table: solid state applications

Author:

Choudhary Kamal¹²^ORCID,DeCost Brian³^ORCID,Major Lily⁴⁵^ORCID,Butler Keith⁵^ORCID,Thiyagalingam Jeyan⁵^ORCID,Tavazza Francesca³^ORCID

Affiliation:

1. Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, 20899, MD, USA

2. Theiss Research, La Jolla, 92037, CA, USA

3. Materials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, 20899, MD, USA

4. Department of Computer Science, Aberystwyth University, SY23 3DB, UK

5. Scientific Computing Department, Rutherford Appleton Laboratory, Science and Technology Facilities Council, Harwell Campus, Didcot, OX11 0QX, UK

Abstract

Classical force fields (FFs) based on machine learning (ML) methods show great potential for large scale simulations of solids.

Funder

Air Force Research Laboratory

Engineering and Physical Sciences Research Council

UK Research and Innovation

Publisher

Royal Society of Chemistry (RSC)

Link

Reference67 articles.

1. S. B.Ogale , Thin films and heterostructures for oxide electronics , Springer Science & Business Media , 2006

5. D. J.Srolovitz and V.Vitek , Atomistic Simulation of Materials: Beyond Pair Potentials , Springer Science & Business Media , 2012

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