3 papers
physics.chem-ph2026
Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators
S. A. Shteingolts, Salman N. Salman, Dan Mendels
Machine learning-based simulators offer the potential to model the dynamics of complex systems more efficiently than classical approaches, while retaining differentiability, a key…
physics.chem-ph2026
A Non Linear Spectral Graph Neural Network Simulator for More Stable and Accurate Rollouts
Salman N. Salman, Sergey A. Shteingolts, Ron Levie +1
Molecular dynamics (MD) simulations are a central tool in science and engineering enabling the study of dynamical behavior and the link between microscopic structure and macroscopi…
physics.chem-ph2025
Dynamical Data for More Efficient and Generalizable Learning: A Case Study in Disordered Elastic Networks
Salman N. Salman, Sergey A. Shteingolts, Ron Levie +1
Machine learning models often require large datasets and struggle to generalize beyond their training distribution. These limitations pose significant challenges in scientific and…