5 citations · 12 across the 7 of their papers we have counts for
9 papers
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…
Atomistic modeling of uranium monocarbide with a machine learning interatomic potential
Lorena Alzate-Vargas, Kashi N. Subedi, Roxanne M. Tutchton +3
Uranium monocarbide (UC) is an advanced ceramic fuel candidate due to its superior uranium density and thermal conductivity compared to traditional fuels. To accurately model UC at…
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…
Toward machine learning interatomic potentials for modeling uranium mononitride
Lorena Alzate-Vargas, Kashi N. Subedi, Nicholas Lubbers +4
Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine lear…
Machine learning potentials with Iterative Boltzmann Inversion: training to experiment
Sakib Matin, Alice Allen, Justin S. Smith +7
Methodologies for training machine learning potentials (MLPs) to quantum-mechanical simulation data have recently seen tremendous progress. Experimental data has a very different c…
Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning
Alice E. A. Allen, Nicholas Lubbers, Sakib Matin +4
The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditio…
Bayesian inference-driven model parameterization and model selection for 2CLJQ fluid models
Owen C. Madin, Simon Boothroyd, Richard A. Messerly +3
A high level of physical detail in a molecular model improves its ability to perform high accuracy simulations, but can also significantly affect its complexity and computational c…