Deciphering diffuse scattering with machine learning and the equivariant
  foundation model: The case of molten FeO
Ganesh Sivaraman Chris J. Benmore
Bridging the gap between diffuse x-ray or neutron scattering measurements and
predicted structures derived from atom-atom pair potentials in disordered
materials, has been a longstanding challenge in condensed matter physics. This
perspective gives a brief overview of the traditional approaches employed over
the past several decades. Namely, the use of approximate interatomic pair
potentials that relate 3-dimensional structural models to the measured
structure factor and its associated pair distribution function. The use of
machine learned interatomic potentials has grown in the past few years, and has
been particularly successful in the cases of ionic and oxide systems. Recent
advances in large scale sampling, along with a direct integration of scattering
measurements into the model development, has provided improved agreement
between experiments and large-scale models calculated with quantum mechanical
accuracy. However, details of local polyhedral bonding and connectivity in
meta-stable disordered systems still require improvement. Here we leverage
MACE-MP-0; a newly introduced equivariant foundation model and validate the
results against high-quality experimental scattering data for the case of
molten iron(II) oxide (FeO). These preliminary results suggest that the
emerging foundation model has the potential to surpass the traditional
limitations of classical interatomic potentials.