Data-driven approach to parameterize SCAN+U for an accurate description of 3d transition metal oxide thermochemistry
Data-driven approach to parameterize SCAN+U for an accurate description of 3d transition metal oxide thermochemistry
复制标题
用于参数化 SCAN U 的数据驱动方法，以准确描述 3d 过渡金属氧化物热化学
DOI：
10.1103/physrevmaterials.6.035003
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发表时间：
2022
影响因子：
3.4
通讯作者：
Hybertsen, Mark S.
中科院分区：
文献类型：
--
作者：
Artrith, Nongnuch;Garrido Torres, José Antonio;Urban, Alexander;Hybertsen, Mark S.
半局域密度泛函理论（DFT）方法在计算过渡金属氧化物的相图时会出现很大的误差，这是由于对分子氧的错误描述以及在具有强局域电子轨道的材料中存在较大的自相互作用误差造成的。基于该方法的经验和半经验修正可以减少这些误差，但修正项的参数化和验证仍然是一个持续的挑战。我们开发了一种系统的方法来确定参数，并通过考虑跨一组过渡金属化合物的相互关联的热化学数据来统计评估结果。我们考虑三个相互关联的水平的校正项：（1）一个恒定的氧结合校正，（2）哈伯德校正，和（3）兼容性校正。参数化表示为一个统一的优化问题。我们证明了这种方法fortransition金属氧化物，考虑一组目标的二元和三元氧化物。从文献中获得了总共37个测量的形成能量，通过系统枚举从形成能量中获得的1710个独特反应的反应能量来增强数据集。为了确保可用数据的平衡数据集，使用聚类并适当加权，根据其相似性对反应进行分组。使用留一交叉验证（CV），统计模型验证的标准技术验证的参数化。我们将该方法应用于强约束和适当赋范（SCAN）密度泛函。基于CV分数，二元（三元）氧化物形成能的误差减少了40%（75%）至0.10（0.03）eV/atom。一个简化的校正方案，不涉及兼容性条款仍然实现了30%（25%）的误差减少。这里演示的方法和工具可以应用于其他类别的材料或参数化的校正，以优化其他目标物理特性的性能。
Semilocal density-functional theory (DFT) methods exhibit significant errors for the phase diagrams of transition-metal oxides that are caused by an incorrect description of molecular oxygen and the large self-interaction error in materials with strongly localized electronic orbitals. Empirical and semiempirical corrections based on themethod can reduce these errors, but the parameterization and validation of the correction terms remains an on-going challenge. We develop a systematic methodology to determine the parameters and to statistically assess the results by considering interlinked thermochemical data across a set of transition metal compounds. We consider three interconnected levels of correction terms: (1) a constant oxygen binding correction, (2) Hubbard-correction, and (3)compatibility correction. The parameterization is expressed as a unified optimization problem. We demonstrate this approach fortransition metal oxides, considering a target set of binary and ternary oxides. With a total of 37 measured formation enthalpies taken from the literature, the dataset is augmented by the reaction energies of 1710 unique reactions that were derived from the formation energies by systematic enumeration. To ensure a balanced dataset across the available data, the reactions were grouped by their similarity using clustering and suitably weighted. The parameterization is validated using leave-one-out cross validation (CV), a standard technique for the validation of statistical models. We apply the methodology to the strongly constrained and appropriately normed (SCAN) density functional. Based on the CV score, the error of binary (ternary) oxide formation energies is reduced by 40% (75%) to 0.10 (0.03) eV/atom. A simplified correction scheme that does not involvecompatibility terms still achieves an error reduction of 30% (25%). The method and tools demonstrated here can be applied to other classes of materials or to parameterize the corrections to optimizeperformance for other target physical properties.