3 papers
cs.LG2026
Hessian-informed machine learning interatomic potential towards bridging theory and experiments
Bangchen Yin, Jian Ouyang, Zhen Fan +7
Local curvature of potential energy surfaces is critical for predicting certain experimental observables of molecules and materials from first principles, yet it remains far beyond…
cs.LG2025
Ensemble Learning of Machine Learning Force Fields
Bangchen Yin, Yue Yin, Yuda W. Tang +1
Machine learning force fields (MLFFs) are a promising approach to balance the accuracy of quantum mechanics with the efficiency of classical potentials, yet selecting an optimal mo…
cs.LG2025
AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic Potential
Bangchen Yin, Jiaao Wang, Weitao Du +9
Molecular dynamics simulations demand an unprecedented combination of accuracy and scalability to tackle grand challenges in catalysis and materials design. To bridge this gap, we…