Transferability of neural network potentials for varying stoichiometry: Phonons and thermal conductivity of MnxGey compounds-Reference-Cited by-同舟云学术

Transferability of neural network potentials for varying stoichiometry: Phonons and thermal conductivity of MnxGey compounds

Published:2020-06-28 Issue:24 Volume:127 Page:244901
ISSN:0021-8979
Container-title:Journal of Applied Physics
language:en
Short-container-title:Journal of Applied Physics

Author:

Mangold Claudia¹,Chen Shunda²^ORCID,Barbalinardo Giuseppe²^ORCID,Behler Jörg³^ORCID,Pochet Pascal⁴^ORCID,Termentzidis Konstantinos⁵^ORCID,Han Yang⁶^ORCID,Chaput Laurent⁶,Lacroix David⁶^ORCID,Donadio Davide²^ORCID

Affiliation:

1. Max-Planck-Institut für Polymerforschung, Ackermannweg 10, 55128 Mainz, Germany

2. Department of Chemistry, University of California Davis, One Shields Ave., Davis, 95616 California, USA

3. Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstr. 6, 37077 Göttingen, Germany

4. Department of Physics, IRIG, Univ. Grenoble Alpes and CEA, F-38000 Grenoble, France

5. Universit Claude Bernard Lyon 1, CNRS, INSA-Lyon, CETHIL UMR5008, F-69621, Villeurbanne, France

6. Université de Lorraine, CNRS, LEMTA, Nancy F-54500, France

Funder

Deutsche Forschungsgemeinschaft

Agence Nationale de la Recherche

European Commission

Publisher

AIP Publishing

Subject

General Physics and Astronomy

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0009550

Reference81 articles.

1. Perspective: Machine learning potentials for atomistic simulations

2. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

3. Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

4. Silicon Liquid Structure and Crystal Nucleation from Ab Initio Deep Metadynamics

5. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

Cited by 32 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Benchmarking machine learning interatomic potentials via phonon anharmonicity;Machine Learning: Science and Technology;2024-08-14

2. Thermal conductivity of Li3PS4 solid electrolytes with ab initio accuracy;Physical Review Materials;2024-06-12

3. Thermal conductivity reduction due to phonon geometrical scattering in nano-engineered epitaxial germanium;Applied Physics Letters;2024-04-29

4. Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials;Journal of Applied Physics;2024-04-24

5. Tuning the lattice thermal conductivity of Janus SnSSe by interlayer twisting: a machine-learning-based study;New Journal of Physics;2024-04-01