70 citations · 88 across the 7 of their papers we have counts for
4 papers · 1 filter
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 Šípka, 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…
Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
Ishan Amin, Sanjeev Raja, Aditi Krishnapriyan
The foundation model (FM) paradigm is transforming Machine Learning Force Fields (MLFFs), leveraging general-purpose representations and scalable training to perform a variety of c…