activity
20182022
most citedMachine learning approaches for analyzing and enhancing molecular dynamics simulations

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

eess.SY2022

Predicted Trajectory Guidance Control Framework of Teleoperated Ground Vehicles Compensating for Delays

Qiang Zhang, Zhouli Xu, Yihang Wang +3

Maneuverability and drivability of the teleoperated ground vehicle could be seriously degraded by large communication delays if the delays are not properly compensated. This paper…

cond-mat.stat-mech2020

Understanding the role of predictive time delay and biased propagator in RAVE

Yihang Wang, Pratyush Tiwary

In this work, we revisit our recent iterative machine learning (ML) -- molecular dynamics (MD) technique "Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)" (Ri…

physics.comp-ph20191 cited

Machine learning approaches for analyzing and enhancing molecular dynamics simulations

Yihang Wang, Joao Marcelo Lamim Ribeiro, Pratyush Tiwary

Molecular dynamics (MD) has become a powerful tool for studying biophysical systems, due to increasing computational power and availability of software. Although MD has made many c…

physics.bio-ph2018

Ligand dissociation mechanisms from all-atom simulations: Are we there yet?

Joao Marcelo Lamim Ribeiro, Sun-Ting Tsai, Debabrata Pramanik +2

Large parallel gains in the development of both computational resources as well as sampling methods have now made it possible to simulate dissociation events in ligand-protein comp…

physics.chem-ph2018

Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE)

Joao Marcelo Lamim Ribeiro, Pablo Bravo Collado, Yihang Wang +1

Here we propose the Reweighted Autoencoded Variational Bayes for Enhanced Sampling (RAVE) method, a new iterative scheme that uses the deep learning framework of variational autoen…