Integrating Physical Principles with Machine Learning for Predicting Field-Enhanced Catalysis.

ABSTRACT: Field-dipole interactions can tune the energetics of polarized species over catalyst nanoparticles (NPs) for sustainable technologies. This can boost the energy efficiency of desired reactions by several orders of magnitude compared with conventional heating. However, the local electric field accumulation over the NPs sharp points and field-dependent adsorption over NPs are not well studied, and the associated computational expense is immense. To address this challenge, we introduce an innovative approach that combines density functional theory (DFT) calculations, DFT-based CO vibrational Stark effects, and physics principles enhanced machine learning (ML). This approach enables precise mapping of local electric fields and integrates the physical principles of the first-order Taylor expans

SUBMITTER: Zhao R 

PROVIDER: S-EPMC11938032 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature