4 papers
PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
Teddy Koker, Abhijeet Gangan, Mit Kotak +2
Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on ene…
Electronic structure prediction of medium and high entropy alloys across composition space
Shashank Pathrudkar, Stephanie Taylor, Abhishek Keripale +6
We propose machine learning (ML) models to predict the electron density -- the fundamental unknown of a material's ground state -- across the composition space of concentrated allo…
Optimization of Transferable Interatomic Potentials for Glasses toward Experimental Properties
Ruoxia Chen, Kai Yang, Morten M. Smedskjaer +3
The accuracy of molecular simulations is fundamentally limited by the interatomic potentials that govern atomic interactions. Traditional potential development, which relies heavil…
Integrated Experiment and Simulation Co-Design: A Key Infrastructure for Predictive Mesoscale Materials Modeling
Shailendra P. Joshi, Ashley Bucsek, Darren C. Pagan +10
The design of structural & functional materials for specialized applications is being fueled by rapid advancements in materials synthesis, characterization, manufacturing, with sop…