Informatics-aided bandgap engineering for solar materials

Dey, Partha Pratim; Bible, Joe; Datta, Somnath; Broderick, Scott; Jasiński, Jacek B.; Sunkara, Mahendra K.; Menon, Madhu; Rajan, Krishna

doi:10.1016/j.commatsci.2013.10.016

Cited by 141 publications

(106 citation statements)

References 39 publications

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“…Another technique is regularization of the linear coefficients (i.e., reducing the l 1 and/or l 2 norm) through approaches such as least absolute shrinkage and selection operator (LASSO) 87 (penalizes l 1 norm), ridge regression (penalizes l 2 norm), or Elastic net 88 (penalizes a combination of l 1 and l 2 norm). These methodologies help reduce the number and strength of correlated or unhelpful predictors and have been successfully applied to several materials predictions problems such as chalcopyrite band gap prediction, 89 band gap engineering, 90 scintillator discovery, 91 mechanical properties of alloys 92 and phosphor data mining. 93 An example of the application of such techniques is the use of principal component linear regression analysis by Curtarolo et al to predict energies of compounds.…”

Section: Linear Modelsmentioning

confidence: 99%

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

et al. 2016

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Data mining has revolutionized sectors as diverse as pharmaceutical drug discovery, finance, medicine, and marketing, and has the potential to similarly advance materials science. In this paper, we describe advances in simulation-based materials databases, open-source software tools, and machine learning algorithms that are converging to create new opportunities for materials informatics. We discuss the data mining techniques of exploratory data analysis, clustering, linear models, kernel ridge regression, tree-based regression, and recommendation engines. We present these techniques in the context of several materials application areas, including compound prediction, Li-ion battery design, piezoelectric materials, photocatalysts, and thermoelectric materials. Finally, we demonstrate how new data and tools are making it easier and more accessible than ever to perform data mining through a new analysis that learns trends in the valence and conduction band character of compounds in the Materials Project database using data on over 2500 compounds.Materials science has traditionally been driven by scientific intuition followed by experimental study. In recent years, theory and computation have provided a secondary avenue for materials property prediction and design. Several successful examples of materials designed in a computer and then realized in the laboratory 1 have now established such methods as a new route for materials discovery and optimization. As computational methods approach maturity, new and complementary techniques based on statistical analysis and machine learning are poised to revolutionize materials science.While the modern use of the term materials informatics dates back only a decade ago, 2 the use of an informatics approach to chemistry and materials science is as old as the periodic table. When Mendeleev grouped together elements by their properties, the electron was yet to be discovered, and the principles of electron configuration and quantum mechanics that underpin chemistry were still many decades away. However, Mendeleev's approach not only resulted in a useful classification but could also make predictions: missing positions in the periodic table indicated potential new elements that were later confirmed experimentally. Mendeleev was also able to spot inaccuracies in atomic weight data of the time. Today, the search for patterns in data remains the goal of materials informatics, although the tools have evolved considerably since Mendeleev's work.While materials informatics methods are still in their infancy compared to other fields, advancements in materials databases and software in the last decade are rapidly gaining ground. In this paper, we discuss recent developments of materials informatics, concentrating specifically on connecting a material's crystal structure and its composition to its properties (and ignoring, for instance, microstructure and processing). First, we provide a brief history of classic studies based on mining crystallographic databases. Next, we describe the recent...

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Section: Linear Modelsmentioning

confidence: 99%

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

et al. 2016

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“…Such methods have been applied to estimate a wide range of material properties such as the melting temperature [11,12], the ionic conductivity [13,14], the phase stability [15,16], the potential energy surface [17][18][19], the atomization energy of molecules [20,21], and so on. On the prediction of the band-gap, a few applications of regression methods have been reported [22][23][24]. Setwayan et al estimated a relationship between PBE KS-gap and experimental band-gap by ordinary least squares regression (OLSR) from a dataset composed of about 100 compounds established on the database AFLOWLIB [2] including both of direct and indirect band-gaps [22].…”

Section: Introductionmentioning

confidence: 99%

“…Setwayan et al estimated a relationship between PBE KS-gap and experimental band-gap by ordinary least squares regression (OLSR) from a dataset composed of about 100 compounds established on the database AFLOWLIB [2] including both of direct and indirect band-gaps [22]. Dey et al predicted the direct band-gap of about 200 ternary chalcopyrite compounds from 28 experimental band-gap observations by OLSR, sparse partial least square regression and least absolute shrinkage and selection operator (LASSO) methods with predictors such as valence, atomic number, melting point, electronegativity and pseudopotential radii of each element [23]. Gu et al, applied support vector regression (SVR) and artificial neural network to predict experimental band-gaps of 25 binary and 31 ternary compounds with some elementspecific predictors [24].…”

Section: Introductionmentioning

confidence: 99%

Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques

et al. 2016

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“…The theory has shown immense usefulness in feature selection and ranking of variables from data www.annualreviews.org • Materials Informatics or information systems (54)(55)(56). Our group has been incorporating rough-set methods to extract general patterns in the data (57,58); continuous variables need to be discretized into intervals by proper selection of variable values (or cuts) that demarcate the boundary of two consecutive intervals. In other words, if a variable contributes a larger number of cuts or subclassifications to the prediction of a property than does another variable, then that first variable is considered to be more significant in determining that property.…”

Section: Identifying Fuzzy Genesmentioning

confidence: 99%

Materials Informatics: The Materials “Gene” and Big Data

Rajan

2015

Annu. Rev. Mater. Res.

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252

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Materials informatics provides the foundations for a new paradigm of materials discovery. It shifts our emphasis from one of solely searching among large volumes of data that may be generated by experiment or computation to one of targeted materials discovery via high-throughput identification of the key factors (i.e., "genes") and via showing how these factors can be quantitatively integrated by statistical learning methods into design rules (i.e., "gene sequencing") governing targeted materials functionality. However, a critical challenge in discovering these materials genes is the difficulty in unraveling the complexity of the data associated with numerous factors including noise, uncertainty, and the complex diversity of data that one needs to consider (i.e., Big Data). In this article, we explore one aspect of materials informatics, namely how one can efficiently explore for new knowledge in regimes of structure-property space, especially when no reasonable selection pathways based on theory or clear trends in observations exist among an almost infinite set of possibilities. 153 Annu. Rev. Mater. Res. 2015.45:153-169. Downloaded from www.annualreviews.org Access provided by Yale University -Law Library on 07/04/15. For personal use only.

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Informatics-aided bandgap engineering for solar materials

Cited by 141 publications

References 39 publications

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques

Materials Informatics: The Materials “Gene” and Big Data

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