Explainable AI models for Materials Science with the SISSO Approach
Join us for this webinar on the SISSO approach for explainable artificial intelligence in materials science. Thomas Purcell will showcase recent methodological developments that have expanded the capabilities of SISSO, including improved feature representation through binary expression trees and enhanced solver algorithms for both regression and classification problems. Lucas Foppa will demonstrate hierarchical SISSO applications in heterogeneous catalysis, illustrating how the method identifies the "materials genes" of catalytic systems and enables the development of design rules for improved catalysts.
Read more
