70 citations · 100 across the 13 of their papers we have counts for
16 papers
Learning the Shared Latent Structure of Cryo-EM Inverse Problems
Nithin Chalapathi, Theo Sternlieb, Ethan Han +4
Understanding biomolecular function requires characterizing three-dimensional structure and conformational variability, for which cryogenic electron microscopy (cryo-EM) is a power…
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
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…
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…
Transformers Discover Molecular Structure Without Graph Priors
Tobias Kreiman, Yutong Bai, Fadi Atieh +3
Computational simulations play a central role in scientific discovery, and machine learning (ML) has emerged as a promising alternative to traditional physics-based modeling. Howev…
MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform
Yuan Chiang, Tobias Kreiman, Christine Zhang +11
Machine learning interatomic potentials (MLIPs) have revolutionized molecular and materials modeling, but existing benchmarks suffer from data leakage, limited transferability, and…
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…
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…
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…
Deep Speech Synthesis from Multimodal Articulatory Representations
Peter Wu, Bohan Yu, Kevin Scheck +6
The amount of articulatory data available for training deep learning models is much less compared to acoustic speech data. In order to improve articulatory-to-acoustic synthesis pe…
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
Eric Qu, Aditi S. Krishnapriyan
Scaling has been critical in improving model performance and generalization in machine learning. It involves how a model's performance changes with increases in model size or input…
Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
Hongwei Jin, Prasanna Balaprakash, Allen Zou +5
The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts.…
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…
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan
Developing equivariant neural networks for the E(3) group plays an important role in modeling 3D data across real-world applications. Enforcing this equivariance primarily involves…