Cut-SOAP: A Machine Learning Descriptor for Rapid Screening of Molecular Adsorption Energetics.

ABSTRACT: Adsorption energy is a fundamental property in catalysis and chemical reaction studies; however, conventional quantum chemistry methods, such as density functional theory, provide high accuracy but are often computationally expensive or even impractical for screening large data sets or complex chemical systems. In this work, we proposed a machine learning (ML) pipeline that efficiently predicts relative energy interactions for molecular adsorption near the minimum molecule-cluster distance, at a fraction of the computational cost of quantum chemistry-based methods. Our approach begins by transforming the Fritz-Haber Institute ab initio materials simulation (FHI-aims) output data into feature arrays through a modified version of the Smooth Overlap of Atomic Positions (SOAP) descriptor, whic

SUBMITTER: Calderan FV 

PROVIDER: S-EPMC12902971 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature