Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training.

ABSTRACT: Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuO

SUBMITTER: Guo J 

PROVIDER: S-EPMC10845107 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature