Multitask learning with semiempirical orbital charges enables sample-efficient MLIPs

arXiv:2605.24073 · physics.chem-ph, cs.LG · Submitted 2026-05-22 · Read on arXiv

physics.chem-ph, cs.LG

Submitted: 2026-05-22

Updated: 2026-10-15

Terminology

• Sample Efficiency — 2× in this paper · explained in 7 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Density Functional Theory (DFT) — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Latent Space — 1× in this paper · explained in 7 episodes

Related papers

• Solvating machine-learning interatomic potentials for solution-phase chemistry
• MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?
• Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics
• Accelerated "on-the-fly" coupled-cluster path-integral molecular dynamics: Impact of nuclear quantum effects on an asymmetric proton
• Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration
• Variational Polaron Theory for Ground States of Strongly Coupled Light-Matter and Electron-Phonon Systems