Rapid structural mapping of ternary metallic alloy systems using the combinatorial approach and cluster analysis

Abstract

Single-crystal Pb(Zr x Ti 1−x ) O 3 thin films prepared by metal-organic chemical vapor deposition: Systematic compositional variation of electronic and optical properties J. Appl. Phys. 81, 2349 (1997) We are developing a procedure for the quick identification of structural phases in thin film composition spread experiments which map large fractions of compositional phase diagrams of ternary metallic alloy systems. An in-house scanning x-ray microdiffractometer is used to obtain x-ray spectra from 273 different compositions on a single composition spread library. A cluster analysis software is then used to sort the spectra into groups in order to rapidly discover the distribution of phases on the ternary diagram. The most representative pattern of each group is then compared to a database of known structures to identify known phases. Using this method, the arduous analysis and classification of hundreds of spectra is reduced to a much shorter analysis of only a few spectra.

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

“…The benefits of machine learning for accelerated materials data analysis have already been realized, with numerous studies showing the great potential for research and discovery. [199][200][201] These studies include a wide range of materials analysis challenges including crystal structure [202][203][204] and phase diagram 130,[205][206][207] determination, materials property predictions, 208,209 micrograph analysis, 210,211 development of interatomic potentials [212][213][214] and energy functionals 215 to improve materials simulations, and on-the-fly data analysis of high-throughput experiments. 216 …”

High energy x-ray diffraction/x-ray fluorescence spectroscopy for high-throughput analysis of composition spread thin films

“…Barr and co-workers 13,14 demonstrated significant advancements in the analysis of powder XRD patterns from organic libraries using principal-component analysis, metric multidimensional scaling, and clustering techniques. These techniques have been adapted for the analysis of inorganic libraries 15 but several properties of inorganic libraries pose significant problems for these algorithms. One particular challenge is the automated detection of phases in multiphase samples.…”

SMT-Aided Combinatorial Materials Discovery

“…Most of the solutions in the literature are based on unsupervised machine learning techniques, such as clustering and non-negative matrix factorization [13,12]. While these approaches are quite effective at extracting information from large amounts of noisy data, their major limitation is that it is hard to enforce the physical constraints of the problem at the same time.…”