Inferring phase diagrams from X-ray data with background signals using graph segmentation

References

• Ward C. Materials Genome Initiative for Global Competitiveness. In 23rd Advanced Aerospace Materials and Processes (AeroMat) Conference and Exposition; 2012. ASM.
   Google Scholar
• Denton AR, Ashcroft NW. Vegard’s law. Phys Rev A. 1991;43(6):3161. doi: 10.1103/PhysRevA.43.3161
   PubMed Web of Science ®Google Scholar
• Barr G, Dong W, Gilmore CJ. High-throughput powder diffraction. II. Applications of clustering methods and multivariate data analysis. J Appl Crystallogr. 2004;37(2):243–252. doi: 10.1107/S0021889804000391
   Web of Science ®Google Scholar
• Baumes LA, Collet P. Examination of genetic programming paradigm for high-throughput experimentation and heterogeneous catalysis. Comput Mater Sci. 2009;45(1):27–40. doi: 10.1016/j.commatsci.2008.03.051
   Web of Science ®Google Scholar
• Berry MW, Browne M, Langville AN, et al. Algorithms and applications for approximate nonnegative matrix factorization. Comput Stat Data Anal. 2007;52(1):155–173. doi: 10.1016/j.csda.2006.11.006
   Web of Science ®Google Scholar
• Cunningham GJ. Application of cluster analysis to high-throughput multiple data types. University of Glasgow; 2011.
   Google Scholar
• Ermon S, Bras RL, Suram SK, et al. Pattern decomposition with complex combinatorial constraints: application to materials discovery. arXiv preprint arXiv:1411.7441, 2014.
   Google Scholar
• Farrusseng D, Clerc F. Diversity management for efficient combinatorial optimization of materials. Appl Surf Sci. 2007;254(3):772–776. doi: 10.1016/j.apsusc.2007.04.091
   Web of Science ®Google Scholar
• Rodemerck U, Baerns M, Holena M, et al. Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials. Appl Surf Sci. 2004;223(1):168–174. doi: 10.1016/S0169-4332(03)00919-X
   Web of Science ®Google Scholar
• Hunt Jr WH. Materials informatics: growing from the bio world. JOM. 2006;58(7):88–88. doi: 10.1007/s11837-006-0150-5
   Web of Science ®Google Scholar
• Kullmann O. Theory and applications of Satisfiability Testing-SAT 2009: 12th International Conference, SAT 2009, Swansea, UK, 2009 June 30–July 3. Proceedings. Vol. 5584; 2009. Springer.
   Google Scholar
• Peurrung L, Ferris K, Osman TM. The materials informatics workshop: theory and application. JOM. 2007;59(3):50. doi: 10.1007/s11837-007-0039-y
   Web of Science ®Google Scholar
• Holena M, Cukic T, Rodemerck U, et al. Optimization of catalysts using specific, description-based genetic algorithms. J Chem Inf Model. 2008;48(2):274–282. doi: 10.1021/ci700218p
   PubMed Web of Science ®Google Scholar
• Suh C, Gorrie C, Perkins J, et al. Strategy for the maximum extraction of information generated from combinatorial experimentation of Co-doped ZnO thin films. Acta Mater. 2011;59(2):630–639. doi: 10.1016/j.actamat.2010.09.068
   Web of Science ®Google Scholar
• Valero S, Argente E, Botti V, et al. Doe framework for catalyst development based on soft computing techniques. Comput Chem Eng. 2009;33(1):225–238. doi: 10.1016/j.compchemeng.2008.08.012
   Web of Science ®Google Scholar
• Wolf D, Buyevskaya O, Baerns M. An evolutionary approach in the combinatorial selection and optimization of catalytic materials. Appl Catal A. 2000;200(1):63–77. doi: 10.1016/S0926-860X(00)00643-8
   Web of Science ®Google Scholar
• West D. Phase equilibria in iron ternary alloys. Br Corros J. 1989;24(1):16–17. doi: 10.1179/000705989798270531
   Google Scholar
• Kusne A, Keller D, Anderson A, et al. High-throughput determination of structural phase diagram and constituent phases using GRENDEL. Nanotechnology. 2015;26(44):444002. doi: 10.1088/0957-4484/26/44/444002
   PubMed Web of Science ®Google Scholar
• Long C, Bunker D, Li X, et al. Rapid identification of structural phases in combinatorial thin-film libraries using x-ray diffraction and non-negative matrix factorization. Rev Sci Instrum. 2009;80(10):103902. doi: 10.1063/1.3216809
   PubMed Web of Science ®Google Scholar
• Long C, Hattrick-Simpers J, Murakami M, et al. Rapid structural mapping of ternary metallic alloy systems using the combinatorial approach and cluster analysis. Rev Sci Instrum. 2007;78(7):072217. doi: 10.1063/1.2755487
   PubMed Web of Science ®Google Scholar
• LeBras R, Damoulas T, Gregoire JM, et al. Constraint reasoning and kernel clustering for pattern decomposition with scaling, in principles and practice of constraint programming – CP 2011. Springer; 2011. p. 508–522.
   Google Scholar
• Barr G, Dong W, Gilmore CJ. PolySNAP: a computer program for analysing high-throughput powder diffraction data. J Appl Crystallogr. 2004;37(4):658–664. doi: 10.1107/S0021889804011173
   Web of Science ®Google Scholar
• Corma A, Díaz-Cabanas MJ, Moliner M, et al. Discovery of a new catalytically active and selective zeolite (ITQ-30) by high-throughput synthesis techniques. J Catal. 2006;241(2):312–318. doi: 10.1016/j.jcat.2006.04.036
   Web of Science ®Google Scholar
• Kusne AG, Gao T, Mehta A, et al. On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets. Sci Rep. 2014;4.
   PubMed Web of Science ®Google Scholar
• Bunn JK, Han S, Zhang Y, et al. Generalized machine learning technique for automatic phase attribution in time variant high-throughput experimental studies. J Mater Res. 2015;30(07):879–889. doi: 10.1557/jmr.2015.80
   Web of Science ®Google Scholar
• Bunn JK, Hu JJ, Hattrick-Simpers JR. Semi-Supervised approach to phase identification from combinatorial sample diffraction patterns. JOM. 2016;68(8):2116–2125. doi: 10.1007/s11837-016-2033-8
   Web of Science ®Google Scholar
• Xiong Z, He YY, Hattrick-Simpers JR, et al. Automated phase segmentation for large-scale X-ray diffraction data using a graph-based phase segmentation (GPhase) algorithm. ACS Comb Sci. 2017;19(3):137–144. doi: 10.1021/acscombsci.6b00121
   PubMed Web of Science ®Google Scholar
• Suram SK, Xue YX, Bai JW, et al. Automated phase mapping with AgileFD and its application to light absorber discovery in the V-Mn-Nb oxide system. ACS Comb Sci. 2017;19(1):37–46. doi: 10.1021/acscombsci.6b00153
   PubMed Web of Science ®Google Scholar
• Suram SK, Haber JA, Jin J, et al. Generating information-rich high-throughput experimental materials genomes using functional clustering via multitree genetic programming and information theory. ACS Comb Sci. 2015;17(4):224–233. doi: 10.1021/co5001579
   PubMed Web of Science ®Google Scholar
• Ilea DE, Whelan PF. Image segmentation based on the integration of colour-texture descriptors – a review. Pattern Recognit. 2011;44(10):2479–2501. doi: 10.1016/j.patcog.2011.03.005
   Web of Science ®Google Scholar
• Cignoni P, Montani C, Scopigno R. Dewall: a fast divide and conquer Delaunay triangulation algorithm in E d. Comput Aided Des. 1998;30(5):333–341. doi: 10.1016/S0010-4485(97)00082-1
   Web of Science ®Google Scholar
• Felzenszwalb PF, Huttenlocher DP. Efficient graph-based image segmentation. Int J Comput Vis. 2004;59(2):167–181. doi: 10.1023/B:VISI.0000022288.19776.77
   Web of Science ®Google Scholar