Multitask learning on linear-scaling GFN1-xTB orbital charges cuts energy MAE by 46% and data needs by 5x versus energy-only MLIPs while outperforming DFT atomic charge augmentation.
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physics.chem-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
THEMol is a new large dataset of torsion, Hessian, energy, and multipole data from DFT for closed-shell organic molecules, organized into five subsets for use in molecular potential development.
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Multitask learning with semiempirical orbital charges enables sample-efficient MLIPs
Multitask learning on linear-scaling GFN1-xTB orbital charges cuts energy MAE by 46% and data needs by 5x versus energy-only MLIPs while outperforming DFT atomic charge augmentation.
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THEMol dataset: Torsion, Hessian, and Energy of Molecules
THEMol is a new large dataset of torsion, Hessian, energy, and multipole data from DFT for closed-shell organic molecules, organized into five subsets for use in molecular potential development.