描述符对高熵纳米合金能带中心预测的物理意义

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Yusuke Nanba, Michihisa Koyama
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引用次数: 0

摘要

d轨道的带中心(d带中心)被广泛地用作分析材料性质的有效描述符。然而,在高熵纳米合金中,不同的原子环境对系统地探索所有可能的组合提出了挑战。由于计算资源的限制,生成足够数量的样本是不可行的。因此,d波段中心应被视为机器学习模型中的响应变量。我们计算了单个原子的d带中心,并应用监督学习技术来确定影响其行为的关键因素。虽然确定了几个因素,但它们在预测d波段中心方面的物理意义仍不清楚。为了解决这个问题,我们结合了各种原子间距离术语作为描述符,以及基于元素的配位数(ECN)。所得模型与slater型轨道的重叠积分非常接近,且ECN的回归系数对有效主量子数和核电荷都很敏感。了解这些描述符的物理意义对于改进性能预测和促进新材料的数据收集至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Physical Significance of Descriptors to Predict the Band Center of High-Entropy Nanoalloys

Physical Significance of Descriptors to Predict the Band Center of High-Entropy Nanoalloys

Physical Significance of Descriptors to Predict the Band Center of High-Entropy Nanoalloys

The band center of d orbitals (d-band center) has been widely used as an effective descriptor for analyzing material properties. However, in high-entropy nanoalloys, the diverse atomic environments present challenges in systematically exploring all possible combinations. Due to computational resource limitations, generating a sufficient number of samples is infeasible. Consequently, the d-band center should be treated as a response variable in machine-learning models. We calculated the d-band center for individual atoms and applied supervised learning techniques to identify key factors influencing its behavior. While several factors were identified, their physical significance in predicting d-band centers remained unclear. To address this issue, we incorporated various interatomic distance terms as descriptors, along with element-based coordination numbers (ECN). The resulting model closely resembled the overlap integral of the Slater-type orbital, and the regression coefficients of the ECN exhibited sensitivity to the effective principal quantum number and nuclear charge. Understanding the physical significance of these descriptors is crucial for improving property predictions and facilitating data collection on novel materials.

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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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