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Title: Automated phase mapping with AgileFD and its application to light absorber discovery in the V–Mn–Nb oxide system

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 MnV 2O 6. Lastly, the open-source family of AgileFD algorithms can be implemented into a broad range of high throughput workflowsmore » to accelerate materials discovery.« less
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  1. California Institute of Technology, Pasadena, CA (United States)
  2. Cornell Univ., Ithaca, NY (United States)
  3. Shanghai Jiao Tong Univ., Shanghai (China)
  4. Cornell Univ., Ithaca, NY (United States); Allen Institute for Artificial Intelligence, Seattle, WA (United States)
Publication Date:
Grant/Contract Number:
SC0004993; AC02-76SF00515
Published Article
Journal Name:
ACS Combinatorial Science
Additional Journal Information:
Journal Volume: 19; Journal Issue: 1; Journal ID: ISSN 2156-8952
American Chemical Society
Research Org:
California Institute of Technology, Pasadena, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
36 MATERIALS SCIENCE; band gap tuning; combinatorial science; high-throughput screening; machine learning; X-ray diffraction
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1339953