Interpreting surface adsorption with band data: a machine learning perspective on quantum orientation

IF 1.6 4区 物理与天体物理 Q3 PHYSICS, CONDENSED MATTER
Jiahao Wei, Xinxu Zhang, Guo Li, Jiamin Liu, Changlong Liu, Yonghui Li
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Abstract

A band-based approach with quantum mechanical data is successful in bridging quantum data and practical applications. Quantum mechanical intuitions of a band-based model (e.g. the d-band model) can be related to the adsorption states being reshaped by a band in an atom-like pattern. However, the incompleteness hinder the extension of the band-based model for broader and more accurate applications. Herein, in this work, a systematical extension of the band-based model is introduced with a complete decomposition of bonding/anti-bonding/non-bonding in a consistent and parameter-free way. The Bonding Decomposition with limited aid from machine learning algorithms, possesses enough explainability power which allow people to evaluate hybridization, repulsive orthogonalization and nonbonding occupations. With the new approach, the adsorption energy can be related to the band-center-like single indicator, or be better related to up to 5 better indicators including a high occupation center, a high occupation width, a nonbonding component in high occupation, a bonding indicator and an anti-bonding indicator in low occupation. As the 5 indicators cover the essence of the band center evaluations, the adsorption energies can simply be modeled with a linear model which split “cans” from “cannots” out of the pure Bonding Decomposition. The Bonding Decomposition is applied to metal d-band and perovskite p-band data. After the normally distributed residuals are considered as “random errors”, the Bonding Decomposition may be good for medium adhesion strength. The algorithm displaces why the band center for metal d-band data is already an effective approach and why perovskite p-band data must be handled with the nonbonding contributions considered.

用波段数据解释表面吸附:量子取向的机器学习视角
一种基于频带的量子力学数据处理方法成功地将量子数据与实际应用连接起来。基于能带模型(例如d能带模型)的量子力学直觉可以与吸附态被带以原子状模式重塑有关。然而,这种不完备性阻碍了基于波段的模型向更广泛、更精确应用的扩展。在本文中,引入了基于带的模型的系统扩展,以一致和无参数的方式完全分解成键/反键/非键。在有限的机器学习算法的帮助下,键分解具有足够的可解释性,可以让人们评估杂交,排斥正交化和非键职业。利用新方法,吸附能可以与带状中心型单一指标相关,或与高职业中心、高职业宽度、高职业非键组分、低职业成键指标和反键指标等多达5个较好的指标相关。由于这5个指标涵盖了能带中心评价的本质,所以吸附能可以简单地用线性模型来建模,从纯粹的键合分解中分离出“能”和“不能”。结合分解应用于金属d波段和钙钛矿p波段数据。将正态分布残差视为“随机误差”后,粘结分解可能有利于中等粘结强度。该算法取代了为什么金属d波段数据的带中心已经是一种有效的方法,以及为什么钙钛矿p波段数据必须考虑非键贡献来处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
The European Physical Journal B
The European Physical Journal B 物理-物理:凝聚态物理
CiteScore
2.80
自引率
6.20%
发文量
184
审稿时长
5.1 months
期刊介绍: Solid State and Materials; Mesoscopic and Nanoscale Systems; Computational Methods; Statistical and Nonlinear Physics
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