Training Machine learned Interatomic Potentials to EXAFS Data for Simulations Under Extreme Conditions

Machine Learned (ML) interatomic potentials are an extremely popular tool for running nearly quantum accurate molecular dynamics (MD) simulations at large scales. Traditionally, ML potentials are trained to energies and forces derived from quantum mechanical simulations. In this paradigm, experimental measurements are relegated to the role of testing data. While quantum mechanical data has proven successful for training ML potentials, there are cases where it is prohibitively expensive to obtain or where quantum models for a system are inaccurate and produce incorrect predictions. With the increasing prevalence of high-resolution, high repetition rate experiments occurring at modern laser and synchrotron facilities, the ability to train ML interatomic potentials directly to atomistic experimental probes is becoming increasingly important. Here we will describe a general-purpose training procedure for refining ML interatomic potentials on experimental data, only requiring a forward model for comparison to experiment. This procedure will first be demonstrated with processed radial distribution function data, as extracted from x-ray diffraction, to refine an aluminum ML interatomic potential. As a second example of this procedure, an ML potential for zinc, originally trained to DFT data, is refined by direct comparison to EXAFS (Extended X-ray Absorption Fine Structure) spectra taken at elevated temperatures and pressures. Both models are then tested against out of sample experimental data such as diffusion constants, elastic properties, and others. Finally, the potentials are used run large scale MD simulations to interpolate the experimental measurements for new pressure and temperature regimes.