59 citations · 145 across the 9 of their papers we have counts for
9 papers
Applied causality to infer protein dynamics and kinetics
Akashnathan Aranganathan, Eric R. Beyerle
The use of generative machine learning models, trained on the experimentally resolved structures deposited in the protein data bank, is an attractive approach to sampling conformat…
Inferring the Isotropic-nematic Phase Transition with Generative Machine Learning
Eric R. Beyerle, Pratyush Tiwary
Contemporary work implies generative machine learning models are capable of learning the phase behavior in condensed matter systems such as the Ising model. In this Letter, we util…
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…
Thermodynamically Optimized Machine-learned Reaction Coordinates for Hydrophobic Ligand Dissociation
Eric Beyerle, Pratyush Tiwary
Ligand unbinding is mediated by the free energy change, which has intertwined contributions from both energy and entropy. It is important but not easy to quantify their individual…
JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
Kamal Choudhary, Daniel Wines, Kangming Li +35
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…
Recent advances in describing and driving crystal nucleation using machine learning and artificial intelligence
Eric R. Beyerle, Ziyue Zou, Pratyush Tiwary
With the advent of faster computer processors and especially graphics processing units (GPUs) over the last few decades, the use of data-intensive machine learning (ML) and artific…
Driving and characterizing nucleation of urea and glycine polymorphs in water
Ziyue Zou, Eric Beyerle, Sun-Ting Tsai +1
Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases its mechanism remains unclear due to a lack of temporal or spatial res…
Quantifying Energetic and Entropic Pathways in Molecular Systems
E. R. Beyerle, Shams Mehdi, Pratyush Tiwary
When examining dynamics occurring at non-zero temperatures, both energy and entropy must be taken into account while describing activated barrier crossing events. Furthermore, good…
Identifying the leading dynamics of ubiquitin: a comparison between the tICA and the LE4PD slow fluctuations in amino acids' position
Eric Beyerle, Marina Guenza
Molecular Dynamics (MD) simulations of proteins implicitly contain the information connecting the atomistic molecular structure and proteins' biologically relevant motion, where la…