Improving prediction accuracy of high-performance materials via modified machine learning strategy

In general, machine learning models trained by traditional cross validation evaluation strategies always have excellent interpolation ability in the known data space, but weak extrapolation ability in the unknown data space. However, in the field of material research, discovering new materials with higher performance always needs high prediction accuracy on the unknown data space outside the existing data range. In order to solve this challenge, we propose a modified evaluation strategy of machine learning modeling to promote the extrapolation ability by sorting the existing data according to the property value before the training/testing data set partition. It is demonstrated that the extrapolation and prediction abilities of machine learning model can be significantly improved by using this strategy. Machine learning models with lower prediction error and higher efficiency of discovering new materials are obtained on three materials data sets (bulk metallic glasses dataset, high-entropy alloys dataset, and piezoelectric materials dataset). The model errors are reduced from 23.84%, 9.79% and 14.47% of the traditional strategy to 2.44%, 7.67% and 8.07% of the modified strategy, respectively. It is indicated that this strategy can provide a solution for machine learning modeling in material field with requirements of discovering new materials with higher performance.