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Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.
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Determination of the phonon dispersion of zinc blende (3c) silicon carbide by inelastic x-ray scattering
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Ab initio molecular dynamics simulation of a pressure induced zinc blende to rocksalt phase transition in sic
HY Xiao, Fei Gao, Xiaotao T Zu, and William J Weber · 2009
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Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning
Chris M Handley, Glenn I Hawe, Douglas B Kell, and Paul LA Popelier · 2009
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An environment-dependent interatomic potential for silicon carbide: calculation of bulk properties, high-pressure phases, point and extended defects, and amorphous structures
G Lucas, M Bertolus, and L Pizzagalli · 2009
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Visualization and analysis of atomistic simulation data with ovito–the open visualization tool
Alexander Stukowski · 2009
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Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
Albert P Bartók, Mike C Payne, Risi Kondor, and Gábor Csányi · 2010
Si/c/h reaxff reactive potential for silicon surfaces grafted with organic molecules
Federico A Soria, Weiwei Zhang, Patricia A Paredes-Olivera, Adri CT Van Duin, and Eduardo M Patrito · 2018
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Graph networks as a universal machine learning framework for molecules and crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong · 2019
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Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on the fly with bayesian inference
Ryosuke Jinnouchi, Jonathan Lahnsteiner, Ferenc Karsai, Georg Kresse, and Menno Bokdam · 2019
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On-the-fly machine learning force field generation: Application to melting points
Ryosuke Jinnouchi, Ferenc Karsai, and Georg Kresse · 2019
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Atomic cluster expansion for accurate and transferable interatomic potentials
Ralf Drautz · 2019
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Snap: Automated generation of quantum-accurate interatomic potentials
Aidan Patrick Thompson, Laura Painton Swiler, Christian Robert Trott, Stephen Martin Foiles, and Garritt J. Tucker · 2014
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Governing factors for the formation of 4h or 6h-SiC polytype during SiC crystal growth: An atomistic computational approach
Kyung-Han Kang, Taihee Eun, Myong-Chul Jun, and Byeong-Joo Lee · 2014
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Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces
Zhenwei Li, James R Kermode, and Alessandro De Vita · 2015
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First principle investigation of phase transition and thermodynamic properties of sic
WH Lee and XH Yao · 2015
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First principles phonon calculations in materials science
A Togo and I Tanaka · 2015
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Distributions of phonon lifetimes in brillouin zones
Atsushi Togo, Laurent Chaput, and Isao Tanaka · 2015
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In situ observation of a phase transition in silicon carbide under shock compression using pulsed x-ray diffraction
SJ Tracy, RF Smith, JK Wicks, DE Fratanduono, AE Gleason, CA Bolme, VB Prakapenka, S Speziale, Karen Appel, A Fernandez-Pañella, et al · 2019
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A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
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On-the-fly active learning of interpretable bayesian force fields for atomistic rare events
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Building nonparametric n-body force fields using gaussian process regression
Aldo Glielmo, Claudio Zeni, Ádám Fekete, and Alessandro De Vita · 2020
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First principle study of structural, electronic and vibrational properties of 3c-sic
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Phase transitions and elastic anisotropies of sic polymorphs under high pressure
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The study of the optical phonon frequency of 3c-sic by molecular dynamics simulations with deep neural network potential
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Baoqin Fu, Yandong Sun, Linfeng Zhang, Han Wang, and Ben Xu · 2021
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E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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Structure and density of silicon carbide to 1.5 tpa and implications for extrasolar planets
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Effects of thermal, elastic, and surface properties on the stability of sic polytypes
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Learning local equivariant representations for large-scale atomistic dynamics
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