“Property Phase Diagrams” for Compound Semiconductors through Data Mining

This paper highlights the capability of materials informatics to recreate “property phase diagrams” from an elemental level using electronic and crystal structure properties. A judicious selection of existing data mining techniques, such as Principal Component Analysis, Partial Least Squares Regression, and Correlated Function Expansion, are linked synergistically to predict bandgap and lattice parameters for different stoichiometries of GaxIn1−xAsySb1−y, starting from fundamental elemental descriptors. In particular, five such elemental descriptors, extracted from within a database of highly correlated descriptors, are shown to collectively capture the widely studied “bowing” of energy bandgaps seen in compound semiconductors. This is the first such demonstration, to our knowledge, of establishing relationship between discrete elemental descriptors and bandgap bowing, whose underpinning lies in the fundamentals of solid solution thermodyanamics.

[1]  R. Howe,et al.  Application of principal component analysis to a full profile correlative analysis of FTIR spectra , 2012 .

[2]  I T Joliffe,et al.  Principal component analysis and exploratory factor analysis , 1992, Statistical methods in medical research.

[3]  P. Paufler,et al.  Numerical Data and Functional Relationships in Science and Technology - New Series. , 1994 .

[4]  Herschel Rabitz,et al.  Multicomponent semiconductor material discovery guided by a generalized correlated function expansion , 1999 .

[5]  Krishna Rajan,et al.  Identifying the ‘inorganic gene’ for high-temperature piezoelectric perovskites through statistical learning , 2011, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[6]  P. Villars,et al.  A simple method for the prediction of new intermetallic phases , 1982 .

[7]  V. Mosorov,et al.  Principal component analysis for characterising homogeneity in powder mixing using image processing techniques , 2006 .

[8]  Krishna Rajan,et al.  Combinatorial design of semiconductor chemistry for bandgap engineering: “virtual” combinatorial experimentation , 2004 .

[9]  Wei Luo,et al.  Information-Theoretic Approach for the Discovery of Design Rules for Crystal Chemistry , 2012, J. Chem. Inf. Model..

[10]  Krishna Rajan,et al.  Combinatorial Materials Sciences: Experimental Strategies for Accelerated Knowledge Discovery , 2008 .

[11]  Jerry R. Meyer,et al.  Band parameters for III–V compound semiconductors and their alloys , 2001 .

[12]  Partha S. Dutta,et al.  Band gap and lattice constant of GaxIn1−xAsySb1−y , 2000 .

[13]  Gertrud Beggerow,et al.  Numerical data and functional relationships in science and technology , 1976 .

[14]  J. S. Blakemore.  Semiconducting and other major properties of gallium arsenide , 1982 .

[15]  K. Rajan,et al.  Quantitative Structure-Activity Relationships (QSARs) for Materials Science , 2001 .

[16]  A R Plummer,et al.  Introduction to Solid State Physics , 1967 .

[17]  J. Connolly.  Multivariable phase diagrams; an algorithm based on generalized thermodynamics , 1990 .

[18]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[19]  Krishna Rajan,et al.  Tracking chemical processing pathways in combinatorial polymer libraries via data mining. , 2010, Journal of combinatorial chemistry.

[20]  Matthieu Verstraete,et al.  First-principles computation of material properties: the ABINIT software project , 2002 .

[21]  Donald S. McClure,et al.  The Theory of Brillouin Zones and Electronic States in Crystals. , 1961 .

[22]  Harald Martens,et al.  A multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables , 1983 .

[23]  S. Ambler,et al.  THE ELEMENTS , 1998 .

[24]  Andreas Daffertshofer,et al.  PCA in studying coordination and variability: a tutorial. , 2004, Clinical biomechanics.

[25]  Krishna Rajan,et al.  Secondary descriptor development for zeolite framework design: an informatics approach , 2003 .

[26]  S. Broderick,et al.  Eigenvalue decomposition of spectral features in density of states curves , 2011 .