期刊
ACS COMBINATORIAL SCIENCE
卷 19, 期 1, 页码 37-46出版社
AMER CHEMICAL SOC
DOI: 10.1021/acscombsci.6b00153
关键词
high-throughput screening; machine learning X-ray diffraction; combinatorial science; band gap tuning
资金
- DOE Energy Innovation Hub through the Office of Science of the U.S. Department of Energy [DE-SC0004993]
- NSF [CCF-1522054, CNS-0832782, CNS-1059284, IIS-1344201]
- ARO [W911-NF-14-1-0498]
- U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences [DE-AC02-76SF00515]
- Direct For Computer & Info Scie & Enginr [1522054, 1344201] Funding Source: National Science Foundation
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 data sets, 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 pseudoternary 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据