Combining Transfer Learning and Representation Learning to Improve Predictive Analytics on Small Materials Data

Modern data mining methods have seen a widespread and growing application in the field of materials science for regression-based predictive modeling due to their effectiveness in extracting and utilizing the hidden information from the materials datasets. However, due to the costly and time-consuming nature of the methods involved in obtaining the experimental and computational data, the majority of the materials datasets are small in size. Moreover, limited hand-engineered representations available from the raw materials data make it harder to improve the accuracy of predictive models on such small and specialized training datasets. In this paper, we introduce a novel technique that combines transfer learning (TL) and representation learning (RL) using a pre-trained deep neural network to maximize accuracy without additional computational costs on inorganic material properties. The performance of the proposed method is compared against traditional machine learning (ML), and deep neural network models trained from scratch (SC) with elemental fraction (EF) as input, more informative physical attributes (PA) as input (for a stringent comparison), as well as conventional TL and RL techniques using deep neural networks. The results demonstrate that the proposed method can improve the accuracy as compared to SC models and conventional TL and RL techniques.