3 papers
physics.chem-ph2026
OrbGNN: A Wave function-based Machine Learning Interelectronic Representation
Brody Quebedeaux, Shahzad Akram, Markus Reiher +1
Machine learning interatomic potentials (MLIPs) have become emerging tools in molecular modeling and computational chemistry. By learning high-dimensional potential energy surfaces…
physics.chem-ph2026
Non-covalent Interactions at cm Accuracy: Data Efficient Physics-Informed Distillation for Machine Learning Interatomic Potentials
Yulin Shen, Shahzad Akram, Louis Primeau +4
Foundation models in atomistic machine learning encode interaction physics across diverse atomic environments, but whether that structure can be transferred when building specialis…
physics.chem-ph2026
Accurate Helium-Benzene Potential: from CCSD(T) to Gaussian Process Regression
Shahzad Akram, Sutirtha Paul, Collin Kovacs +3
The accurate modeling of non-covalent interactions between helium and graphitic materials is important for understanding quantum phenomena in reduced dimensions, with the helium-be…