6 papers
Expanding Protein Structure Prediction into Conformational State Space
Devlina Chakravarty, Justin J. Miller, Da Teng +9
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…
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…
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…
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…