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A combination of systematic density functional theory (DFT) calculations andmachine learning techniques has a wide range of potential applications. Thisstudy presents an application of the combination of systematic DFT calculationsand regression techniques to the prediction of the melting temperature forsingle and binary compounds. Here we adopt the ordinary least-squaresregression (OLSR), partial least-squares regression (PLSR), support vectorregression (SVR) and Gaussian process regression (GPR). Among the four kinds ofregression techniques, the SVR provides the best prediction. In addition, theinclusion of physical properties computed by the DFT calculation to a set ofpredictor variables makes the prediction better. Finally, a simulation to findthe highest melting temperature toward the efficient materials design usingkriging is demonstrated. The kriging design finds the compound with the highestmelting temperature much faster than random designs. This result may stimulatethe application of kriging to efficient materials design for a broad range ofapplications.

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids