Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO

arXiv:2403.00259v1 Announce Type: cross
Abstract: 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 …

abstract arxiv atom case challenge cond-mat.mtrl-sci cs.lg foundation foundation model gap machine machine learning materials matter overview perspective physics ray type x-ray