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Abdulrahman Aldossary

2 accepted papers

2026

Coupled Cluster con MoLe: Molecular Orbital Learning for Neural Wavefunctions

ICML 2026poster

Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled cluster (CC) theory is the most successful method for achieving accuracy beyond DFT and predicting properties that cl…

Cited by 0Source ↗Scholar ↗
2026

Derivative Informed Learning of Exchange-Correlation Functionals

ICML 2026poster

Machine-learned (ML) XC-functionals promise improved accuracy, but overfit to training energies and basis sets without proper regularization. We introduce Derivative Informed XC-Loss (DI-Loss), a loss that regularizes ML-XC training by supervising energy gradients on the Grassmannian of density matr…

Cited by 0Source ↗Scholar ↗