Abstract
Extended X-ray Absorption Fine Structure (EXAFS) experiments provide high-resolution, quantitative insights into the local atomic structure—even under extreme shock conditions. However, while the data is rich and high-fidelity, its inherent complexity and accompanying noise can make structural analysis challenging. In this talk, we introduce our novel approach for refining machine-learned interatomic potentials (MLIPs) for zinc under dynamic compression by leveraging experimental EXAFS data. MLIPs have transformed molecular dynamics simulations by systematically bridging the gap between electron-aware quantum mechanical models and large-scale, atomistic simulations. Our method employs the Hierarchically Interacting Particle Neural Network (HIPPYNN), which is initially trained on quantum mechanical data through an active learning framework (ALF) and then iteratively refined using both ambient and shock-compressed EXAFS spectra. We will demonstrate that the refined HIPNN MLIP model accurately captures zinc's structural responses over a broad range of pressures and temperatures and discuss the challenges of fitting multiple spectra simultaneously. Although the QM-trained MLIP initially struggled to predict zinc's targeted thermodynamic properties using MD simulation, our refinement procedure improves a diverse range of physical properties predicted by the model.
*This work at Los Alamos National Laboratory was supported by the Directed Research and Development and the U.S. DOE Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division under Triad National Security, LLC Contract No. 89233218CNA000001 (FWP: LANLE3F2). Computing resources were provided by the LANL Institutional Computing Program, supported by the U.S. DOE NNSA under the same contract. Portions of this work were performed at the High-Pressure Collaborative Access Team and the Dynamic Compression Sector at the Advanced Photon Source, Argonne National Laboratory, with support from DOE-NNSA's Office of Experimental Sciences and in collaboration with Washington State University, the Army Research Laboratory, and the University of Rochester.
