FT²DP: large atomic model fine-tuned machine learning potential for accelerating atomistic simulation of iron-based Fischer-Tropsch synthesis

Abstract

Density-functional theory (DFT)-based atomistic simulation methods have been essential in studying the structure-property relationships in heterogeneous catalysis. However, for complex catalytic processes, such as iron-based Fischer-Tropsch synthesis (FTS), the temporal or spatial scales involved are generally too large to perform DFT calculations. Recently, the development of machine learning potentials (MLPs) has demonstrated the capability for atomistic simulation on a large scale and long duration, and the rise of large atomic models (LAMs) is gaining much attention with unified descriptors incorporating a wide range of chemical knowledge and fine-tuning methodology for efficiently deploying the model to downstream tasks. In this work, we construct a MLP named fine-tuned Fischer-Tropsch deep potential (FT$$ ^2 $$DP) model, which is fine-tuned from upstream DPA-2 LAM on a downstream dataset focused on the iron-based FTS process. We further applied this model to investigate iron-based FTS in both surface reactions and reconstructions of edge sites combined with the double-to-single transition state optimization method and the local genetic algorithm. Our work demonstrated the capability and efficiency of our model for iron-based FTS simulations, while revealing the reaction mechanism on common active sites containing [Fe$$ _4 $$C] squares, and the abundant formation of [Fe$$ _4 $$C] squares on several reconstructed surfaces. These insights highlight the potential of utilizing LAM for atomistic simulation for iron-based FTS processes and other complex catalytic reactions.

Keywords

Large atomic model / machine learning potentials / fine-tuning / Fischer-Tropsch synthesis / transition state optimization / surface reconstruction