9 citations · 13 across the 10 of their papers we have counts for
10 papers
Exponentially Tilted Thermodynamic Maps (expTM): Predicting Phase Transitions Across Temperature, Pressure, and Chemical Potential
Suemin Lee, Ruiyu Wang, Lukas Herron +1
Predicting and characterizing phase transitions is crucial for understanding generic physical phenomena such as crystallization, protein folding and others. However, directly obser…
PLUMED Tutorials: a collaborative, community-driven learning ecosystem
Gareth A. Tribello, Massimiliano Bonomi, Giovanni Bussi +60
In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific p…
A survey of probabilistic generative frameworks for molecular simulations
Richard John, Lukas Herron, Pratyush Tiwary
Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking the…
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…
Simulating Crystallization in a Colloidal System Using State Predictive Information Bottleneck based Enhanced Sampling
Vanessa J. Meraz, Ziyue Zou, Pratyush Tiwary
We investigate crystal nucleation in supersaturated colloid suspensions using enhanced molecular dynamics simulations augmented with machine learning techniques. The simulations re…
Enhanced sampling of Crystal Nucleation with Graph Representation Learnt Variables
Ziyue Zou, Pratyush Tiwary
In this study, we present a graph neural network-based learning approach using an autoencoder setup to derive low-dimensional variables from features observed in experimental cryst…