A molecular dynamics postprocessing tool for analyzing the structure and dynamics of materials

Machine learning and computing advancements have made it possible to carry out simulations over longer lengths and timescales. This has opened up new opportunities for understanding materials in different thermodynamic conditions. These large-scale simulations help analyze experimental measurements such as inelastic scattering and study diffusion in solid electrolytes for potential use in future batteries. However, analyzing these large datasets presents challenges in extracting useful thermodynamic and transport properties. To address these challenges, the Fortran-based code MDLAB has been developed. This code processes large-scale molecular dynamics simulation trajectories from various software and computes important quantities like mean squared displacements, phonon spectra, pair-distribution functions, simulated neutron/X-ray spectra and more. This comprehensive approach allows for a deeper understanding of material behavior, ultimately enhancing our overall grasp of condensed matter physics.