Machine Learning Interatomic Potentials as Emerging Tools for Materials Science-Reference-Cited by-同舟云学术

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Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Published:2019-09-05 Issue:46 Volume:31 Page:1902765
ISSN:0935-9648
Container-title:Advanced Materials
language:en
Short-container-title:Adv. Mater.

Author:

Deringer Volker L.¹²^ORCID,Caro Miguel A.³,Csányi Gábor¹

Affiliation:

1. Department of EngineeringUniversity of Cambridge Cambridge CB2 1PZ UK

2. Department of ChemistryUniversity of Cambridge Cambridge CB2 1EW UK

3. Department of Electrical Engineering and Automation and Department of Applied PhysicsAalto University Espoo 02150 Finland

Funder

Isaac Newton Trust

Academy of Finland

Publisher

Wiley

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adma.201902765

Reference173 articles.

1. Origin of low sodium capacity in graphite and generally weak substrate binding of Na and Mg among alkali and alkaline earth metals

2. Niobium tungsten oxides for high-rate lithium-ion energy storage

3. Conceptual Inorganic Materials Discovery - A Road Map

4. Computational predictions of energy materials using density functional theory

5. Structure prediction drives materials discovery

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1. Long timescale molecular dynamics simulations of carboxylic acid-modified anatase TiO2(101)-water interfaces using ab-initio deep neural network potentials;Surface Science;2024-12

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3. A “bond-focused” local atomic environment representation for a high throughput solute interaction spectrum analysis;Acta Materialia;2024-10

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