453762 Property Prediction of Crystalline Solids from Composition and Crystal Structure

Friday, November 18, 2016: 2:00 PM
Yosemite A (Hilton San Francisco Union Square)
Bruno A. Calfa, Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI and John R. Kitchin, Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA

Property Prediction of Crystalline Solids from Composition and Crystal Structure

Bruno A. Calfa[1] and John R. Kitchin[2]

Predicting properties of compounds has received considerable attention across different disciplines and has seen applications in diverse areas. The approach proposed in this paper is influenced by the advances in Group Contribution Methods (GCMs).

A key characteristic of GCMs in general is that the property descriptors (i.e., predictors) are explicitly represented by the molecular structure (and chemical composition) of a compound. One can pose an optimal inverse (design) problem, which attempts to obtain the original molecule (structure and composition) given a target property value. This is the main goal of the research area called Computer-Aided Molecular/Mixture Design (CAMD) as exemplified by several works in the literature (Eljack et al., 2007; Samudra and Sahinidis, 2013).

In the crystalline solids literature, several research groups have proposed statistical (machine learning) approaches for property prediction. Saad et al. (2012) investigated both unsupervised and supervised machine learning techniques to predict structure and properties of crystals with chemical formula AB. Ma et al. (2015) developed a machine-learning-augmented model based on artificial neural networks (ANNs) that captures nonlinear adsorbate-substrate interactions.

We propose using kernel regression (Li and Racine, 2007) as a data-driven and rigorous nonparametric statistical technique to predict properties of atomic crystals. A key feature of the proposed approach is the possibility of treating predictors not only as continuous, but also as categorical data. The latter specifically allows the predictive model to capture the discrete nature of crystals with regards to composition (number of atoms in the chemical formula) and spatial configuration (finite number of crystallographic space groups). Another important aspect of using kernel regression is the direct access to its explicit mathematical form, which can be directly embedded in optimal inverse problems to design new crystalline materials with given target properties. The property prediction approach is illustrated by training models to predict electronic properties of 746 binary metal oxides and elastic properties of 1,173 crystals. As a first approach to solving the inverse problem, we describe an exhaustive enumeration algorithm (Calfa and Kitchin, 2016).


Calfa, B. A.; and Kitchin, J. R. 2016. Property Prediction of Crystalline Solids from Composition and Crystal Structure. AIChE Journal. In press. DOI: 10.1002/aic.15251.

Eljack, F. T.; Eden, M. R.; Kazantzi, V.; Qin, X.; and El-Halwagi, M. M. 2007. Simultaneous Process and Molecular DesignÑA Property Based Approach. AIChE Journal. 53(5):1232Ð1239.

Li, Q., and Racine, J. S. 2007. Nonparametric Econometrics: Theory and Practice. Themes in Modern Econometrics. Princeton University Press. New Jersey, NJ. USA.

Ma, X.; Li, Z.; Achenie, L. E. K.; and Xin, H. 2015. Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening. Journal of Physi- cal Chemistry Letters. 6(18):3528Ð3533.

Saad, Y.; Gao, D.; Ngo, T.; Bobbitt, S.; Chelikowsky, J. R.; and Andreoni, W. 2012. Data Mining for Materials: Computational Experiments with AB Compounds. Physical Review B. 85(10):104104.

Samudra, A. P., and Sahinidis, N. V. 2013. Optimization-Based Framework for Computer- Aided Molecular Design. AIChE Journal. 59(10):3686Ð3701.

[1] Department of Chemical and Biological Engineering. University of Wisconsin-Madison. Madison, WI 53706. USA.

[2] Department of Chemical Engineering. Carnegie Mellon University. Pittsburgh, PA 15213. USA.

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