3 papers
cs.LG2026
Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning
Chen Wang, Siyu Hu, Guangming Tan +1
SO(3) equivariant graph neural networks have become the dominant paradigm for atomistic foundation models, achieving high accuracy and data efficiency by building rotational symmet…
cs.LG2025
Exploring Landscapes for Better Minima along Valleys
Tong Zhao, Jiacheng Li, Yuanchang Zhou +2
Finding lower and better-generalizing minima is crucial for deep learning. However, most existing optimizers stop searching the parameter space once they reach a local minimum. Giv…
physics.comp-ph2024
ALKPU: an active learning method for the DeePMD model with Kalman filter
Haibo Li, Xingxing Wu, Liping Liu +4
Neural network force field models such as DeePMD have enabled highly efficient large-scale molecular dynamics simulations with ab initio accuracy. However, building such models hea…