16 citations · 17 across the 6 of their papers we have counts for
5 papers · 1 filter
Democratizing Atomistic Simulation Workflows for the AI Era with the Quantum Accelerator
Andrew S. Rosen, Naisargi Goyal, Brad Ayers +25
We present the Quantum Accelerator (QuAcc), an open-source workflow library for atomistic simulations with an emphasis on quantum-mechanical calculations. QuAcc provides predefined…
Machine-Learned Leftmost Hessian Eigenvectors for Robust Transition State Finding
Guanchen Wu, Chung-Yueh Yuan, Kareem Hegazy +2
The reliable determination of transition states (TSs) benefits from second-order information for robust convergence and validation, but the computational expense of Hessians prohib…
Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential
Nitesh Kumar, Jianwei Lai, Casey S. Mezerkor +5
Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse datasets are revolutionizing computational chemistry, enabling molecular dynamics simulations o…
Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials
Alex Morehead, Miruna Cretu, Antonia Panescu +14
General-purpose 3D modeling in chemistry encompasses molecules and materials, requiring both generative and predictive capabilities. However, most existing AI approaches are optimi…
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…