Iron carbide clusters are studied extensively in many catalysis fields by quantum chemistry methods. The high cost of structural energy calculation has always been the bottleneck of its development. First principles based on density functional theory (DFT) have high calculation accuracy and sound portability. Still, its computational cost is so high that it is difficult to carry out large-scale, long-term, and high-throughput simulations. A data driven potential is crucial for further accelerating the screening or prediction process in the high through-put calculation. In this paper, we generate 177k clusters data and choose some state-of-the-art machine learning models in physical chemistry to train them. 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 realize the prediction of the adsorption position of hydrogen atoms on the cluster surface. We can get a more stable adsorption position of the hydrogen atom in a shorter time, compared with traditional quantum chemical calculators.
A scheduling model was developed to optimize the maximum completion time, total machine load, and maximum machine load for the fuzzy flexible job shop problem with uncertain processing times. To solve this problem, a multi-strategy dynamic evolution-based improved multi-objective evolutionary algorithm based on decomposition(IMOEA/D) was proposed. In order to enhance the quality of the non-dominated solution set and improve the algorithm efficiency. The algorithm firstly employs a strategy based on minimum processing time and workload, along with a non-dominated solution prioritization mechanism to generate the initial population. Secondly, three evolutionary strategies are incorporated, and their probabilities are dynamically adjusted with the increase of evolution generations. Finally, a variable neighborhood search method is introduced to improve the search performance of the algorithm. The effectiveness of the proposed algorithm was demonstrated through experimental validation.
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.
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