First principles data-driven potentials for prediction of iron carbide clusters - Diao, Enhu; He, Yurong; Liu, Xuhong; Tong, Qiang; Yang, Tao; Liu, Xiaotong; Lewis, James P. (Frontiers in Quantum Science and Technology, 2023)
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 using density functional theory. The computational cost is prohibitive for high-throughput simulations large length- and time- scales. In this paper, we generate data from 177k clusters and choose state-of-the-art machine learning models within physical chemistry to train 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 on poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and prediction of hydrogen adsorption sites on cluster surfaces. We achieve a more stable adsorption locations of the hydrogen atom more rapidly compared with traditional quantum chemical calculations.