20 citations · 138 across the 26 of their papers we have counts for
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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
General Symmetry-Based Potential Energy Surface Grid Reduction in Normal Coordinates
Can Liao, Markus Reiher
The construction of a grid-based potential energy surface (PES) can be prohibitively expensive as the number of grid points grows exponentially with molecular size. Molecular symme…
quant-ph2026
Utility-scale quantum computational chemistry
Davide Castaldo, Markus Reiher
Chemistry and materials science are widely regarded as potential killer application fields for quantum hardware. While the dream of unlocking unprecedented simulation capabilities…