56 citations · 80 across the 9 of their papers we have counts for
10 papers
Combined Representation and Generation with Diffusive State Predictive Information Bottleneck
Richard John, Yunrui Qiu, Lukas Herron +1
Generative modeling becomes increasingly data-intensive in high-dimensional spaces. In molecular science, where data collection is expensive and important events are rare, compress…
Latent Thermodynamic Flows: Unified Representation Learning and Generative Modeling of Temperature-Dependent Behaviors from Limited Data
Yunrui Qiu, Richard John, Lukas Herron +1
Accurate characterization of the equilibrium distributions of complex molecular systems and their dependence on environmental factors such as temperature is essential for understan…
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…
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…
Generative artificial intelligence for computational chemistry: a roadmap to predicting emergent phenomena
Pratyush Tiwary, Lukas Herron, Richard John +3
The recent surge in Generative Artificial Intelligence (AI) has introduced exciting possibilities for computational chemistry. Generative AI methods have made significant progress…
Inferring phase transitions and critical exponents from limited observations with Thermodynamic Maps
Lukas Herron, Kinjal Mondal, John S. Schneekloth +1
Phase transitions are ubiquitous across life, yet hard to quantify and describe accurately. In this work, we develop an approach for characterizing generic attributes of phase tran…
Enhanced Sampling with Machine Learning: A Review
Shams Mehdi, Zachary Smith, Lukas Herron +2
Molecular dynamics (MD) enables the study of physical systems with excellent spatiotemporal resolution but suffers from severe time-scale limitations. To address this, enhanced sam…
Robust retrieval of dynamic sequences through interaction modulation
Lukas Herron, Pablo Sartori, BingKan Xue
Many biological systems dynamically rearrange their components through a sequence of configurations in order to perform their functions. Such dynamic processes have been studied us…
From data to noise to data: mixing physics across temperatures with generative artificial intelligence
Yihang Wang, Lukas Herron, Pratyush Tiwary
Using simulations or experiments performed at some set of temperatures to learn about the physics or chemistry at some other arbitrary temperature is a problem of immense practical…
Thermal statistics of small magnets
Lukas Herron, Purushottam Dixit
While the canonical ensemble has been tremendously successful in capturing thermal statistics of macroscopic systems, deviations from canonical behavior exhibited by small systems…