1 citations · 1 across the 6 of their papers we have counts for
6 papers
Out-of-Distribution Inverse Design of Elastic Networks with Differentiable Graph Neural Network Molecular Dynamics
Sergey A. Shteingolts, Salman N. Salman, Ron Levie +1
Machine-learning-based inverse design can accelerate the discovery of materials with targeted properties, but conventional structure--property models often require large training d…
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…
Collective Variable-Guided Engineering of the Free-Energy Surface of a Small Peptide
Muralika Medaparambath, Alexander Zhilkin, Dan Mendels
Engineering the free-energy surfaces (FES) of proteins and peptides is central to controlling conformational ensembles and their responses to perturbations. However, predicting how…
Guiding Peptide Kinetics via Collective-Variable Tuning of Free-Energy Barriers
Alexander Zhilkin, Muralika Medaparambath, Dan Mendels
While recent advances in AI have transformed protein structure prediction, protein function is also strongly influenced by the thermodynamic and kinetic features encoded in its und…
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…
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…