699 citations · 699 across the 6 of their papers we have counts for
7 papers
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…
Autonomous Transition State Search with Soft Actor-Critic Reinforcement Learning
Utham Suresh, Konstantinos D. Vogiatzis
Transition state (TS) search is a crucial step in understanding chemical reactivity and mechanisms, yet conventional algorithms remain computationally intensive and heavily reliant…
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…
DDCCNet: Physics-enhanced Multitask Neural Networks for Data-driven Coupled-cluster
P. D. Varuna S. Pathirage, Konstantinos D. Vogiatzis
We present the data-driven coupled-cluster deep network (DDCCNet), a family of multitask, physics-enhanced deep learning architectures designed to predict coupled-cluster singles a…
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…
Data-driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory
Grier M. Jones, Run. R. Li, A. Eugene DePrince +1
The exponential computational cost of describing strongly correlated electrons can be mitigated by adopting a reduced density-matrix (RDM)-based description of the electronic struc…