4 papers
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…