Thermal Materials Discovery via Deep Learning based High-Throughput Computational Screening

High-throughput computational screening of materials with target thermal conductivity has the capability to transform many industries such as thermoelectricity generation and high performance micro-/nano electronic devices, which produce a significant amount of excess heat during operation and searching for materials with high thermal conductivity is extremely important for the disruptive development of such micro-/nano-electronics in order to prolong their working life and increase reliability However, this potential has not been implemented due to the huge computational resources needed by current first-principles based thermal conductivity calculations and challenges of theoretical models because of the highly complex and nonlinear relationships from atomic structures of materials to the thermal transport properties. Deep learning has transformed an increasing number of fields where big data are available such as image and speech recognition, and medical image analysis. However, the materials science has remained largely untapped by deep learning despite its high economical potential. This two-year EAGER project aims to develop novel deep neural network techniques to achieve fast and accurate computational prediction of thermal conductivity for high-throughput thermal material discovery. The development of a reliable, fast, and accurate deep learning models is a necessity towards experimental validation and realistic application of high-throughput thermal materials screening. Simultaneously the program will aim to enhance diversity by engaging minority and underrepresented students to participate in STEM research. The participants will also develop understanding of both atomistic simulations of thermal transport and big data analytics; hence contributing to workforce development.

# Publications

_{3}Sb

_{2}from first principles: the crucial role of higher-order phonon scattering

_{2}CSO and Ti

_{2}CSSe Janus MXene Structures for Li and Mg Electrodes

_{3}

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