125 citations · 136 across the 7 of their papers we have counts for
8 papers
Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics
Ziyue Zou, Dedi Wang, Pratyush Tiwary
Molecular dynamics simulations offer detailed insights into atomic motions but face timescale limitations. Enhanced sampling methods have addressed these challenges but even with m…
Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning based Information Bottleneck
Dedi Wang, Pratyush Tiwary
The weighted ensemble (WE) method stands out as a widely used segment-based sampling technique renowned for its rigorous treatment of kinetics. The WE framework typically involves…
An Information Bottleneck Approach for Markov Model Construction
Dedi Wang, Yunrui Qiu, Eric Beyerle +2
Markov state models (MSMs) are valuable for studying dynamics of protein conformational changes via statistical analysis of molecular dynamics (MD) simulations. In MSMs, the comple…
From latent dynamics to meaningful representations
Dedi Wang, Yihang Wang, Luke Evans +1
While representation learning has been central to the rise of machine learning and artificial intelligence, a key problem remains in making the learned representations meaningful.…
Accelerating all-atom simulations and gaining mechanistic understanding of biophysical systems through State Predictive Information Bottleneck
Shams Mehdi, Dedi Wang, Shashank Pant +1
An effective implementation of enhanced sampling algorithms for molecular dynamics simulations requires a priori knowledge of the approximate reaction coordinate describing the rel…
Influence of long range forces on the transition states and dynamics of NaCl ion-pair dissociation in water
Dedi Wang, Renjie Zhao, John D. Weeks +1
We study NaCl ion-pair dissociation in a dilute aqueous solution using computer simulations both for the full system with long range Coulomb interactions and for a well chosen refe…