A fundamental objective of materials modeling is identifying atomic structures that align with experimental observables. Conventional approaches for disordered materials involve sampling from thermodynamic ensembles and hoping for an experimental match. This process is inefficient and offers no guarantee of success. We present a method based on modified molecular dynamics, that we call molecular augmented dynamics (MAD), which identifies structures that simultaneously match multiple experimental observables and exhibit low energies as described by a machine learning interatomic potential (MLP) trained from ab-initio data. We demonstrate its feasibility by finding representative structures of glassy carbon, nanoporous carbon, ta-C, a-C: D and a-COₓ that match their respective experimental observables -- X-ray diffraction, neutron diffraction, pair distribution function and X-ray photoelectron spectroscopy data -- using the same initial structure and underlying MLP. The method is general, accepting any experimental observable whose simulated counterpart can be cast as a function of differentiable atomic descriptors. This method enables a computational "microscope" into experimental structures.
Zarrouk et al. (Sat,) studied this question.