Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials

Documenti analoghi

• No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials
  di: Houston, Paul L., et al.
  Pubblicazione: (2024)
• The quantum nature of ubiquitous vibrational features revealed for ethylene glycol
  di: Nandi, Apurba, et al.
  Pubblicazione: (2025)
• A $Δ$-Machine Learning Approach for Force Fields, Illustrated by a CCSD(T) 4-body Correction to the MB-pol Water Potential
  di: Qu, Chen, et al.
  Pubblicazione: (2022)
• Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods
  di: Houston, Paul L., et al.
  Pubblicazione: (2021)
• $Δ$-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol
  di: Nandi, Apurba, et al.
  Pubblicazione: (2024)