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Accelerating the Training and Improving the Reliability of
  Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials
  through Active Learning

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning

18 September 2024
Kisung Kang
Thomas A. R. Purcell
Christian Carbogno
Matthias Scheffler
    AI4CE
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Papers citing "Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning"

3 / 3 papers shown
Title
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials
Albert J. W. Zhu
Simon L. Batzner
Albert Musaelian
Boris Kozinsky
37
47
0
17 Nov 2022
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast
  and Accurate Force Fields
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
Ilyes Batatia
D. P. Kovács
G. Simm
Christoph Ortner
Gábor Csányi
73
478
0
15 Jun 2022
DeePMD-kit: A deep learning package for many-body potential energy
  representation and molecular dynamics
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Han Wang
Linfeng Zhang
Jiequn Han
E. Weinan
AI4CE
58
1,238
0
11 Dec 2017
1