69 citations · 72 across the 3 of their papers we have counts for
8 papers · 1 filter
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
Ryan Liu, Eric Qu, Tobias Kreiman +2
Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in…
A recipe for scalable attention-based MLIPs: unlocking long-range accuracy with all-to-all node attention
Eric Qu, Brandon M. Wood, Aditi S. Krishnapriyan +1
Machine-learning interatomic potentials (MLIPs) have advanced rapidly, with many top models relying on strong physics-based inductive biases. However, as models scale to larger sys…
Transformers Discover Molecular Structure Without Graph Priors
Tobias Kreiman, Yutong Bai, Fadi Atieh +3
Graph Neural Networks (GNNs) are the dominant architecture for molecular machine learning, particularly for molecular property prediction and machine learning interatomic potential…
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…
Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields
Tobias Kreiman, Aditi S. Krishnapriyan
Machine Learning Force Fields (MLFFs) are a promising alternative to expensive ab initio quantum mechanical molecular simulations. Given the diversity of chemical spaces that are o…
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…