PILOT PROGRAM OF KNOWLEDGE INNOVATION AT INSTITUTE OF THEORETICAL PHYSICS

Ⅰ. REVIEW The Institute of Theoretical Physics (ITP) of the CAS was founded in 1978 with the personal approval of Comrade Deng Xiaoping, in the same year when the Third Plenary Session of the Central Committee of the CPC (Communist Party of China) was convened. Over the past 20 years, the Institute has been striving to explore a road for sound development in a bid to achieve the following targets: In regard to research, the institute strives

Prediction of lattice thermal conductivity with two-stage interpretable machine learning

Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network(CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model–sure independence screening and sparsifying operator(SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database(OQMD)(https://www.oqmd.org/). The proposed approach guides the next step of searching for materials with ultra-high or ultralow lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials.