7 papers
AquaGen: Scaling generative models to molecular dynamics precision on thousands of atoms
Emmanuel Bengio, Sanjeev Raja, Yui Tik Pang +5
We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution…
PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems
Divyam Goel, Nithin Chalapathi, Sanjeev Raja +1
Inverse problems in partial differential equations (PDEs) involve estimating the physical parameters of a system from observed spatiotemporal solution fields. Neural networks are w…
The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
Daniel S. Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith +20
Machine learning (ML) models hold the promise of transforming atomic simulations by delivering quantum chemical accuracy at a fraction of the computational cost. Realization of thi…
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
Sanjeev Raja, Martin Å Ãpka, Michael Psenka +3
Transition path sampling (TPS), which involves finding probable paths connecting two points on an energy landscape, remains a challenge due to the complexity of real-world atomisti…
Foundation Models for Atomistic Simulation of Chemistry and Materials
Eric C. -Y. Yuan, Yunsheng Liu, Junmin Chen +11
Given the power of large language and large vision models, it is of profound and fundamental interest to ask if a foundational model based on data and parameter scaling laws and pr…
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
Sanjeev Raja, Ishan Amin, Fabian Pedregosa +1
Machine learning force fields (MLFFs) are an attractive alternative to ab-initio methods for molecular dynamics (MD) simulations. However, they can produce unstable simulations, li…