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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"

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