ML4MS Machine Learning Modalities for Material Science

A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.