3 papers
physics.chem-ph2025
Evaluating Multiconfigurational Trials for Accurate Phaseless Auxiliary-Field Quantum Monte Carlo on 3d Transition Metal Complexes
Hung T. Vuong, Ankit Mahajan, John L. Weber +3
In this study, we evaluate multi-configurational trial wave function protocols for phaseless auxiliary field quantum Monte Carlo (ph-AFQMC) on transition metal containing systems.…
physics.chem-ph2025
Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties
John L. Weber, Rishabh D. Guha, Garvit Agarwal +10
Machine learning force fields (MLFFs) have emerged as a sophisticated tool for cost-efficient atomistic simulations approaching DFT accuracy, with recent message passing MLFFs able…
cond-mat.dis-nn2024
Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation
In Won Yeu, Annika Stuke, Jon L. pez-Zorrilla +5
Artificial neural network (ANN) potentials enable highly accurate atomistic simulations of complex materials at unprecedented scales. Despite their promise, training ANN potentials…