Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials

• URL: http://arxiv.org/abs/2404.08413v2
• Date: Sun, 06 Oct 2024 10:28:27 GMT
• Title: Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials
• Authors: Tomoya Shiota, Kenji Ishihara, Wataru Mizukami,
• Abstract summary: We propose a rapid method for predicting HEA properties using data from monometallic systems. We developed high-precision models by employing both classical and quantum machine learning. The proposed approach accelerates the exploration of the vast HEA chemical space, facilitating the design of novel catalysts.
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• Abstract: Computational screening is indispensable for the efficient design of high-entropy alloys (HEAs), which hold considerable potential for catalytic applications. However, the chemical space of HEAs is exponentially vast with respect to the number of constituent elements, making even machine learning-based screening calculations time-intensive. To address this challenge, we propose a rapid method for predicting HEA properties using data from monometallic systems (or few-component alloys). Central to our approach is the newly introduced local surface energy (LSE) descriptor, which captures local surface reactivity at atomic resolution. By applying linear regression, we successfully screened adsorption energies of molecules on HEAs based on LSEs derived from monometallic systems. Furthermore, we developed high-precision models by employing both classical and quantum machine learning. Our method enabled CO adsorption-energy calculations for 1000 quinary nanoparticles, comprising 201 atoms each, within a few days, considerably faster than density functional theory, which would require hundreds of years or neural network potentials, which would have taken hundreds of days. The proposed approach accelerates the exploration of the vast HEA chemical space, facilitating the design of novel catalysts.

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