Method construction of structure-property relationships from data by machine learning assisted mining for materials design applications

Data driven material research is a hot topic in the cross field of artificial intelligence and materials science. The core of new material prediction is to find the relationship between material structure and properties. In this research, machine learning will have important advantages and play an important role for materials data. In this paper,we put forward a framework combining feature engineering and linear regression to find the correlation between structure and properties from materials data. High temperature superconductor and double perovskites for solar cell swere employed to test the feasibility of the method. In the former, we successfully rebuilt a descriptor (l zeta)(-1) from data mining which is consistent with the theoretical formula. In the latter, as an exploration, we obtain a new descriptor (chi(b2)rs(x)(2)e(rsx))(-1) from data mining which expresses the heat of formation (Delta H-F) in the double perovskite. By our experiment, the method can obtain related expressions of structure-property relationship for material.The results show that the method is a simple yet efficient paradigm to construct the structure property relationship and provides valuable hints to accelerate the process of materials design. (C) 2020 The Author(s). Published by Elsevier Ltd.