Machine learning assisted reverse Monte Carlo modeling for neutron total scattering data

In the area of atomic-level structure modeling, there are two well known parallel problems. The theory driven modeling usually cannot fully account for the disorder of practical system and therefore may fail to reproduce the complete picture of structure model as observed experimentally. The data driven approach tries to derive the strucrual model from the experimental data in a reverse manner (i.e., data to model) and therefore natually is able to catch features observed experimentally. But quite often it lacks the accurate coverage of energetic landscape from the theoretical perspective. In this contribution, we aim at bringing in a novel approach combining the theoretical and experimental considerations. To realize this, the LAMMPS module for energy calculation is implemented into the reverse Monte Carlo routine (here, the RMCProfile package was used) for modeling total scattering data. Through such a combined approach, atomic positions would be adjusted according to the agreement with experimental scattering data and the energy landscape simultanesouly. Specifically concerning the energy calculation, the Gaussian processing based machine learning routine for potential field construction is employed here. Such an approach, at the same time providing density functional theory level of accurary, gurantees a reasonably short computational time which is required for the metropolis algorithm for structure modeling. The LAMMPS implemented RMCProfile package for conducting the combined modeling is generally applicable to utilize neutron and X-ray total scattering data, X-ray absorption spectroscopy data, elecrton scattering data, etc. for structure modeling to provide insights into structure-property link of general consensed matter systems.