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

Materials property prediction with uncertainty quantification: A benchmark study

D. Varivoda, R. Dong, S. S. Omee, and J. Hu

5/23/2023

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

3/18/2023

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

2/6/2023

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

1/5/2023

Machine Learning Accelerated Discovery of Promising Thermal Energy Storage Materials with High Heat Capacity

J. Ojih, U. Onyekpe, A. Rodriguez, J. Hu, C. Peng, and M. Hu

9/15/2022

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

8/26/2022

Generative design of stable semiconductor materials using deep learning and density functional theory

E. M. D. Siriwardane, Y. Zhao, I. Perera, and J. Hu

8/4/2022

Predicting Lattice Vibrational Frequencies Using Deep Graph Neural Networks

N. Nguyen, S. V. Louis, L. Wei, K. Choudhary, M. Hu, and J. Hu

7/21/2022

Efficiently searching extreme mechanical properties via boundless objective-free exploration and minimal first-principles calculations

J. Ojih, M. Al-Fahdi, A. D. Rodriguez, K. Choudhary, and M. Hu

7/4/2022

DeepSeqPanII: An Interpretable Recurrent Neural Network Model With Attention Mechanism for Peptide-HLA Class II Binding Prediction

Z. Liu, J. Jin, Y. Cui, Z. Xiong, A. Nasiri, Y. Zhao, and J. Hu

7/1/2022

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

6/6/2022

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

5/1/2022

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

4/28/2022

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

MaterialsAtlas.org: 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

4/11/2022

Large scale dataset of real space electronic charge density of cubic inorganic materials from density functional theory (DFT) calculations

F. Q. Wang, K. Choudhary, Y. Liu, J. Hu, and M. Hu

2/21/2022

Crystal Structure Prediction Using an Age-Fitness Multiobjective Genetic Algorithm and Coordination Number Constraints

W. Yang, E. M. Dilanga Siriwardane, and J. Hu

1/21/2022

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

1/1/2022

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

1/1/2022

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

1/1/2022

Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors

Y. Li, R. Dong, W. Yang, and J. Hu

10/1/2021

Crystal structure prediction of materials with high symmetry using differential evolution

W. Yang, E. M. Dilanga Siriwardane, R. Dong, Y. Li, and J. Hu

8/31/2021

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

8/5/2021

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

7/20/2021

First-Principles Investigation of Ti₂CSO and Ti₂CSSe Janus MXene Structures for Li and Mg Electrodes

E. M. D. Siriwardane, and J. Hu

6/7/2021

Computational Discovery of New 2D Materials Using Deep Learning Generative Models

Y. Song, E. M. D. Siriwardane, Y. Zhao, and J. Hu

5/14/2021

Electronic charge density as a fast approach for predicting Li-ion migration pathways in superionic conductors with first-principles level precision

Y. Liu, X. Jiang, J. Zhao, and M. Hu

5/1/2021

Mlatticeabc: Generic Lattice Constant Prediction of Crystal Materials Using Machine Learning

Y. Li, W. Yang, R. Dong, and J. Hu

4/20/2021

Inverse design of composite metal oxide optical materials based on deep transfer learning and global optimization

R. Dong, Y. Dan, X. Li, and J. Hu

2/1/2021

The exceptionally high thermal conductivity after ‘alloying’ two-dimensional gallium nitride (GaN) and aluminum nitride (AlN)

H. Wang, D. Wei, J. Duan, Z. Qin, G. Qin, Y. Yao, and M. Hu

1/6/2021

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

1/1/2021

Lattice Thermal Conductivity Prediction Using Symbolic Regression and Machine Learning

C. Loftis, K. Yuan, Y. Zhao, M. Hu, and J. Hu

12/23/2020

Distance Matrix-Based Crystal Structure Prediction Using Evolutionary Algorithms

J. Hu, W. Yang, and E. M. Dilanga Siriwardane

12/10/2020

Why thermal conductivity of CaO is lower than that of CaS: a study from the perspective of phonon splitting of optical mode

Z. Yang, K. Yuan, J. Meng, X. Zhang, D. Tang, and M. Hu

10/14/2020

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

10/1/2020

Spatial density neural network force fields with first-principles level accuracy and application to thermal transport

A. Rodriguez, Y. Liu, and M. Hu

7/17/2020

Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials

Y. Dan, Y. Zhao, X. Li, S. Li, M. Hu, and J. Hu

6/26/2020

Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks

Y. Zhao, K. Yuan, Y. Liu, S. Louis, M. Hu, and J. Hu

6/12/2020

Strong electron-phonon coupling induced anomalous phonon transport in ultrahigh temperature ceramics ZrB2 and TiB2

J. Yang, W. Zhang, C. Xu, J. Liu, L. Liu, and M. Hu

5/1/2020

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

4/13/2020

Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions

Y. Zhao, Y. Cui, Z. Xiong, J. Jin, Z. Liu, R. Dong, and J. Hu

2/13/2020

Molecular dynamics simulations of the effect of dislocations on the thermal conductivity of iron

Y. Sun, Y. Zhou, J. Han, M. Hu, B. Xu, and W. Liu

1/28/2020

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

1/1/2020

Giant effect of spin–lattice coupling on the thermal transport in two-dimensional ferromagnetic CrI₃

G. Qin, H. Wang, L. Zhang, Z. Qin, and M. Hu

1/1/2020

Computational Prediction of Critical Temperatures of Superconductors Based on Convolutional Gradient Boosting Decision Trees

Y. Dan, R. Dong, Z. Cao, X. Li, C. Niu, S. Li, and J. Hu

1/1/2020

Evaluating explorative prediction power of machine learning algorithms for materials discovery usingk-fold forward cross-validation

Z. Xiong, Y. Cui, Z. Liu, Y. Zhao, M. Hu, and J. Hu

1/1/2020

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

12/14/2019

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