A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers-Reference-Cited by-同舟云学术

A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers

Published:2015-06-06 Issue:16 Volume:115 Page:1129-1139
ISSN:0020-7608
Container-title:International Journal of Quantum Chemistry
language:en
Short-container-title:Int. J. Quantum Chem.

Author:

Caccin Marco¹,Li Zhenwei¹²,Kermode James R.¹³,De Vita Alessandro¹⁴

Affiliation:

1. Department of Physics; King's College London; Strand London WC2R 2LS United Kingdom

2. Department of Chemistry; University of Basel; Klingelbergstrasse 80 CH-4056 Basel Switzerland

3. Warwick Centre for Predictive Modelling, School of Engineering; University of Warwick; Coventry CV4 7AL United Kingdom

4. CENMAT-UTS; Via Alfonso Valerio 2 34127 Trieste Italy

Funder

The Engineering and Physical Sciences Research Council

Argonne National Laboratory through a collaboration with the Thomas Young Centre and by the Rio Tinto Centre for Advanced Mineral Recovery based at Imperial College London

Publisher

Wiley

Subject

Physical and Theoretical Chemistry,Condensed Matter Physics,Atomic and Molecular Physics, and Optics

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/qua.24952/fullpdf

Reference42 articles.

1. Multiscale Modelling of Materials Chemomechanics: Brittle Fracture of Oxides and Semiconductors

2. Stress-corrosion mechanisms in silicate glasses

3. Low-speed fracture instabilities in a brittle crystal

4. Atomic mechanism and prediction of hydrogen embrittlement in iron

5. Hybrid atomistic simulation methods for materials systems

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