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

Documenti analoghi

• Carrier Mobility of Strongly Anharmonic Materials from First Principles
  di: Quan, Jingkai, et al.
  Pubblicazione: (2024)
• Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
  di: Birks, Fraser, et al.
  Pubblicazione: (2025)
• Hydrogen under Pressure as a Benchmark for Machine-Learning Interatomic Potentials
  di: Bischoff, Thomas, et al.
  Pubblicazione: (2024)
• Improving Reliability of Machine Learned Interatomic Potentials With Physics-Informed Pretraining
  di: Zheng, Qianyu, et al.
  Pubblicazione: (2026)
• Efficient Band Structure Unfolding with Atom-centered Orbitals: General Theory and Application
  di: Quan, Jingkai, et al.
  Pubblicazione: (2025)