872 citations · 1.1k across the 13 of their papers we have counts for
4 papers · 1 filter
Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
Alice E. A. Allen, Rui Li, Sakib Matin +8
Accurately modeling chemical reactions at the atomistic level requires high-level electronic structure theory due to the presence of unpaired electrons and the need to properly des…
Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
Shriya Gumber, Lorena Alzate-Vargas, Benjamin T. Nebgen +4
Machine learning interatomic potentials (MLIPs) are inherently limited by the accuracy of the training data, usually consisting of energies and forces obtained from quantum mechani…
Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you need
Mitchell Messerly, Sakib Matin, Alice E. A. Allen +5
The promise of machine learning interatomic potentials (MLIPs) has led to an abundance of public quantum mechanical (QM) training datasets. The quality of an MLIP is directly limit…
Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
Sakib Matin, Alice E. A. Allen, Emily Shinkle +9
Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures tr…