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

Project Personnel

Chen Li

University of South Carolina, Columbia

Jianjun Hu

University of South Carolina, Columbia

Ming Hu

University of South Carolina, Columbia

Funding Divisions

Division of Materials Research (DMR)

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.


Physics guided deep learning for generative design of crystal materials with symmetry constraints
Y. Zhao, E. M. D. Siriwardane, Z. Wu, N. Fu, M. Al-Fahdi, M. Hu, and J. Hu
Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table
A. Rodriguez, C. Lin, H. Yang, M. Al-Fahdi, C. Shen, K. Choudhary, Y. Zhao, J. Hu, B. Cao, H. Zhang, and M. Hu
Material transformers: deep learning language models for generative materials design
N. Fu, L. Wei, Y. Song, Q. Li, R. Xin, S. S. Omee, R. Dong, E. M. D. Siriwardane, and J. Hu
DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition
R. Dong, Y. Zhao, Y. Song, N. Fu, S. S. Omee, S. Dey, Q. Li, L. Wei, and J. Hu
Predicting Lattice Vibrational Frequencies Using Deep Graph Neural Networks
N. Nguyen, S. V. Louis, L. Wei, K. Choudhary, M. Hu, and J. Hu
Accurate Prediction of Voltage of Battery Electrode Materials Using Attention-Based Graph Neural Networks
S. Louis, E. M. D. Siriwardane, R. P. Joshi, S. S. Omee, N. Kumar, and J. Hu
Scalable deeper graph neural networks for high-performance materials property prediction
S. S. Omee, S. Louis, N. Fu, L. Wei, S. Dey, R. Dong, Q. Li, and J. Hu
Zintl Phase Compounds Mg3Sb2−xBix (x = 0, 1, and 2) Monolayers: Electronic, Phonon and Thermoelectric Properties From ab Initio Calculations
Z. Chang, J. Ma, K. Yuan, J. Zheng, B. Wei, M. Al-Fahdi, Y. Gao, X. Zhang, H. Shao, M. Hu, and D. Tang
TCSP: a Template-Based Crystal Structure Prediction Algorithm for Materials Discovery
L. Wei, N. Fu, E. M. D. Siriwardane, W. Yang, S. S. Omee, R. Dong, R. Xin, and J. Hu
4/14/2022 a materials informatics web app platform for materials discovery and survey of state-of-the-art
J. Hu, S. Stefanov, Y. Song, S. S. Omee, S. Louis, E. M. D. Siriwardane, Y. Zhao, and L. Wei
Anomalous Thermal Conductivity Induced by High Dispersive Optical Phonons in Rubidium and Cesium Halides
Z. Chang, K. Yuan, J. Li, Z. Sun, J. Zheng, M. Al-Fahdi, Y. Gao, B. Wei, X. Zhang, M. Hu, and D. Tang
High-throughput computational evaluation of lattice thermal conductivity using an optimized Slack model
G. Qin, A. Huang, Y. Liu, H. Wang, Z. Qin, X. Jiang, J. Zhao, J. Hu, and M. Hu
Novel insights into lattice thermal transport in nanocrystalline Mg3Sb2 from first principles: the crucial role of higher-order phonon scattering
Z. Chang, J. Zheng, Y. Jing, W. Li, K. Yuan, J. Ma, Y. Gao, X. Zhang, M. Hu, J. Yang, and D. Tang
Crystal structure prediction of materials with high symmetry using differential evolution
W. Yang, E. M. Dilanga Siriwardane, R. Dong, Y. Li, and J. Hu
High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks
Y. Zhao, M. Al‐Fahdi, M. Hu, E. M. D. Siriwardane, Y. Song, A. Nasiri, and J. Hu
Active-Learning-Based Generative Design for the Discovery of Wide-Band-Gap Materials
R. Xin, E. M. D. Siriwardane, Y. Song, Y. Zhao, S. Louis, A. Nasiri, and J. Hu
Computational Discovery of New 2D Materials Using Deep Learning Generative Models
Y. Song, E. M. D. Siriwardane, Y. Zhao, and J. Hu
Contact map based crystal structure prediction using global optimization
J. Hu, W. Yang, R. Dong, Y. Li, X. Li, S. Li, and E. M. D. Siriwardane
Distance Matrix-Based Crystal Structure Prediction Using Evolutionary Algorithms
J. Hu, W. Yang, and E. M. Dilanga Siriwardane
Machine Learning based prediction of noncentrosymmetric crystal materials
Y. Song, J. Lindsay, Y. Zhao, A. Nasiri, S. Louis, J. Ling, M. Hu, and J. Hu
Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission
S. M. Louis, A. Nasiri, J. Bao, Y. Cui, Y. Zhao, J. Jin, X. Huang, and J. Hu
Graph convolutional neural networks with global attention for improved materials property prediction
S. Louis, Y. Zhao, A. Nasiri, X. Wang, Y. Song, F. Liu, and J. Hu
Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning
X. Li, Y. Dan, R. Dong, Z. Cao, C. Niu, Y. Song, S. Li, and J. Hu

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Designing Materials to Revolutionize and Engineer our Future (DMREF)