← authors · overview

• Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
  2024 · 11 citations
• $Δ$-Machine Learning for Potential Energy Surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) Level of Theory
  2020
• Breaking the Coupled Cluster Barrier for Machine Learned Potentials of Large Molecules: The Case of 15-atom Acetylacetone
  2021
• 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
  2021
• Quantum calculations on a new CCSD(T) machine-learned PES reveal the leaky nature of gas-phase $trans$ and $gauche$ ethanol conformers
  2022
• FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization
  2024