533 citations · 1.9k across the 55 of their papers we have counts for
60 papers
SPIBER: Reconstructing Free Energy Landscapes from Short, Unconverged Trajectories with Generative Flow Networks
Venkata Sai Sreyas Adury, Pratyush Tiwary
Molecular systems have many degrees of freedom, but their metastable behavior can often be described by a few collective variables. Identifying these variables and estimating free…
Expanding Protein Structure Prediction into Conformational State Space
Devlina Chakravarty, Justin J. Miller, Da Teng +8
Recent AI advances have enabled protein structure prediction at near-experimental accuracy, largely solving the problem of identifying a dominant conformation from sequence. Many p…
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…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
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…
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…
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…
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…
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…
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…
Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE
Xinyu Gu, Akashnathan Aranganathan, Pratyush Tiwary
Small molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo stru…
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…
Atomic scale insights into NaCl nucleation in nanoconfined environments
Ruiyu Wang, Pratyush Tiwary
In this work we examine the nucleation from NaCl aqueous solutions within nano-confined environments, employing enhanced sampling molecular dynamics simulations integrated with mac…
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…
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…
Exploring kinase DFG loop conformational stability with AlphaFold2-RAVE
Bodhi P. Vani, Akashnathan Aranganathan, Pratyush Tiwary
Kinases compose one of the largest fractions of the human proteome, and their misfunction is implicated in many diseases, in particular cancers. The ubiquitousness and structural s…
Is the Local Ion Density Sufficient to Drive NaCl Nucleation from the Melt and Aqueous Solution?
Ruiyu Wang, Shams Mehdi, Ziyue Zou +1
Even though nucleation is ubiquitous in different science and engineering problems, investigating nucleation is extremely difficult due to the complicated ranges of time and length…
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…
Quantifying the relevance of long-range forces for crystal nucleation in water
Renjie Zhao, Ziyue Zou, John D. Weeks +1
Understanding nucleation from aqueous solutions is of fundamental importance in a multitude of fields, ranging from materials science to biophysics. The complex solvent-mediated in…
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…
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…