Final Report : Data-Driven Prediction of Band Gap of Materials

Advancements in material chemistry have remained one of the biggest challenges of the 21 century. Many material discoveries have been the result of purely experimental inference and trial-anderror [Jain et al. 2013]. Accurate prediction of material properties using computational methods like density functional theory (DFT) or Molecular Dynamics (MD) have limitations of being computationally expensive [Ma and Wang-Wang 2016] and are often sizelimited. In recent years, there has been a growing interest in developing machine learning based high-throughput predictive algorithms [Kusne et al. 2015]. Most notably, Gaultois et al. showed that Random Forest ensemble methods with leave one out cross validation (LOOCV) can accurately predict the electrical conductivities and Seebeck coefficients of thermoelectric materials that are different from those commonly found in literature [Gaultois et al. 2016]. Recent studies have used machine learning algorithms to predict properties of materials such as melting temperature, mechanical properties of alloys, and dielectric properties for various applications [Ward et al. 2016]. Ward et al. implemented a Random Forest method after partitioning the dataset into sets of similar materials (based on range of band gaps, or whether a compound contains a halogen, chalcogen or pnictogen). Though prediction accuracy increased by five times, the model assumes materials belong to these pre-defined partitioned subsets, thereby sacrificing robustness. Other machine learning models have focused on predicting band gaps for solar cell devices where a precise band gap is necessary for both tandem and single junction performance [Dey 2014]. One such machine learning model implemented a linear least squares regression for feature selection compounded with kernel ridge regression (KRR)–a method capable of handling complex non-linear relationships between features–to predict band gaps of double perovskites [Pilania et al. 2015]. Although the model has reported training and test set r scores of 0.997 and 0.937, respectively, this model can only be applied to a limited chemical space for nonmagnetic perovskites. A model to predict important electronic properties like band gap can help guide the search for materials for perovskite and tandem solar cells, heterojunction optical devices, and many other fields. Machine learning based approaches may be used to find a correlation between certain material properties and attributes, which can help direct new material discovery [Gaultois et al. 2016; Meredig et al. 2014]. With this goal, we apply machine learning techniques to predict band gaps of compound materials. To represent the materials in feature space, we follow a chemical composition based approach similar to [Meredig et al. 2014], where features are derived from atomistic, electronic, and structural properties of the constituent elements. Based on a dataset listing previous DFTcalculated band gaps, we evaluate the performance of several regression techniques including ordinary least squares (OLS) linear regression, Ridge, Lasso, Random Forest, Ada Boosting, and Multilayer Perceptron. We also focus on finding significant features and attempt to explain their correlation to band gap based on the underlying physics and chemistry. This report presents the summary of our progress in applying machine learning techniques to predict the band gaps of materials. In Sec. 2, we discuss the database and important features. In Sec. 3, various machine learning algorithms that we have used are discussed. In Sec. 4, we discuss feature ranking as well as results obtained from applying the different models to our dataset. Finally, in Sec. 5, we discuss our overall conclusions and ideas for future work.