Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability.

ABSTRACT: Machine learning has been successfully applied in recent years to screen materials for a variety of applications. However, despite recent advances, most screening-based machine learning approaches are limited in generality and transferability, requiring new models to be created from scratch for each new application. This is particularly apparent in catalysis, where there are many possible intermediates and transition states of interest in addition to a large number of potential catalytic materials. In this work, we developed a new machine learning framework that is built on chemical principles and allows the creation of general, interpretable, reusable models. Our new architecture uses latent variables to create a set of submodels that each take on a relatively simple learning task, leadin

SUBMITTER: Kayode GO 

PROVIDER: S-EPMC10807004 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature