First principles data-driven potentials for prediction of iron carbide clusters

Abstract

Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput simulations with large length and time scales. In this paper, we generate data from 177 k clusters and choose state-of-the-art machine learning models within physical chemistry to train the features of this data. The generated potential gives a very high prediction accuracy on the order of the structure stability and achieves better adaptability/tolerance to poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and the prediction of hydrogen adsorption sites on cluster surfaces. We achieve more stable adsorption locations of the hydrogen atom more rapidly compared with traditional quantum chemical calculations.