使用AgileFD进行自动相位图绘制及其在V-Mn-Nb氧化物系统中发现光吸收体的应用
Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System
摘要：
快速构建相图是组合材料科学的核心原则，加速材料发现的努力往往受到解释组合X射线衍射数据集的挑战的阻碍，我们通过开发AgileFD来解决这个问题，AgileFD是一种人工智能算法，可以从X射线衍射图案的组合库中快速绘制相位图。AgileFD通过卷积非负矩阵分解的新扩展对基于合金的峰位移进行建模，这不仅改善了组成相的识别，而且还将其浓度和晶格参数映射为组成的函数。通过将Gibbs相位规则引入算法，在无监督操作的情况下得到有物理意义的相位图，并通过注入系统的专家知识得到更精细的解。该算法是证明通过调查的V-Mn-Nb氧化物系统，其中分解的8个氧化物相，包括两个大量的合金化，提供了第一个相图为这个伪三元系统。该相图能够解释高通量带隙数据，从而发现新的太阳光吸收剂和基于合金的MnV 2 O 6直接允许带隙能量的调谐。AgileFD算法的开源系列可以在广泛的高通量工作流程中实施，以加速材料发现。
英文摘要：
Rapid construction of phase diagrams is a central tenet of combinatorial materials science with accelerated materials discovery efforts often hampered by challenges in interpreting combinatorial x-ray diffraction datasets, which we address by developing AgileFD, an artificial intelligence algorithm that enables rapid phase mapping from a combinatorial library of x-ray diffraction patterns. AgileFD models alloying-based peak shifting through a novel expansion of convolutional nonnegative matrix factorization, which not only improves the identification of constituent phases but also maps their concentration and lattice parameter as a function of composition. By incorporating Gibbs phase rule into the algorithm, physically meaningful phase maps are obtained with unsupervised operation, and more refined solutions are attained by injecting expert knowledge of the system. The algorithm is demonstrated through investigation of the V-Mn-Nb oxide system where decomposition of eight oxide phases, including two with substantial alloying, provides the first phase map for this pseudo-ternary system. This phase map enables interpretation of high-throughput band gap data, leading to the discovery of new solar light absorbers and the alloying-based tuning of the direct-allowed band-gap energy of MnV2O6. The open-source family of AgileFD algorithms can be implemented into a broad range of high throughput workflows to accelerate materials discovery.
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