Automated Pynta-Based Curriculum for ML-Accelerated Calculation of Transition States.

ABSTRACT: Microkinetic models (MKMs) are widely used within the computational heterogeneous catalysis community to investigate complex reaction mechanisms, to rationalize experimental trends, and to accelerate the rational design of novel catalysts. However, constructing these models requires computationally expensive and manually tedious density functional theory (DFT) calculations for identifying transition states for each elementary reaction within the MKM. To address these challenges, we demonstrate a novel protocol that uses the open-source kinetics workflow tool Pynta to automate the iterative training of a reactive machine learning potential (rMLP). Specifically, using the silver-catalyzed partial oxidation of methanol as a prototypical example, we first demonstrate our workflow by training a

SUBMITTER: Price T 

PROVIDER: S-EPMC12035851 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature