Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide
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Title
Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide
Authors
Keywords
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Journal
PHYSICAL REVIEW LETTERS
Volume 126, Issue 15, Pages -
Publisher
American Physical Society (APS)
Online
2021-04-15
DOI
10.1103/physrevlett.126.156002
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- (2020) Yunxing Zuo et al. JOURNAL OF PHYSICAL CHEMISTRY A
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- Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
- (2019) Volker L. Deringer et al. ADVANCED MATERIALS
- Less is more: Sampling chemical space with active learning
- (2018) Justin S. Smith et al. JOURNAL OF CHEMICAL PHYSICS
- Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics
- (2018) Volker L. Deringer et al. Journal of Physical Chemistry Letters
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- (2018) Stefan Chmiela et al. Nature Communications
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- (2016) V. Botu et al. Journal of Physical Chemistry C
- FIT2D: a multi-purpose data reduction, analysis and visualization program
- (2016) A. P. Hammersley JOURNAL OF APPLIED CRYSTALLOGRAPHY
- Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
- (2015) A.P. Thompson et al. JOURNAL OF COMPUTATIONAL PHYSICS
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- (2014) G Broglia et al. MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING
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- (2012) Jörg Neuefeind et al. NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION B-BEAM INTERACTIONS WITH MATERIALS AND ATOMS
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