High-throughput computational discovery of 3218 ultralow thermal conductivity and dynamically stable materials by dual machine learning models

Ojih, Joshua; Shen, Chen; Rodriguez, Alejandro; Zhang, Hongbin; Choudhary, Kamal; Hu, Ming

doi:10.1039/d3ta04874h

J. Mater. Chem. A

2023

DOI: 10.1039/d3ta04874h

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High-throughput computational discovery of 3218 ultralow thermal conductivity and dynamically stable materials by dual machine learning models

Joshua Ojih,

Chen Shen,

Alejandro Rodriguez

et al.

Abstract: Using dual machine learning models, we identified 3218 inorganic crystals with ultralow lattice thermal conductivity (LTC), which will be of great interest for technologically important applications such as thermal insulators and thermoelectrics.

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2024

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Cited by 2 publications

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High throughput substrate screening for interfacial thermal management of β-Ga2O3 by deep convolutional neural network

Al-Fahdi,

2024

Journal of Applied Physics

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Electronic devices get smaller and smaller in every generation. In micro-/nano-electronic devices such as high electron mobility transistors, heat dissipation has become a crucial design consideration due to the ultrahigh heat flux that has a negative effect on devices' performance and their lifetime. Therefore, thermal transport performance enhancement is required to adapt to the device size reduction. β-Ga2O3 has recently gained significant scientific interest for future power devices because of its inherent material properties such as extremely wide bandgap, outstanding Baliga's figure of merit, large critical electric field, etc. This work aims to use a machine learning approach to search promising substrates or heat sinks for cooling β-Ga2O3, in terms of high interfacial thermal conductance (ITC), from large-scale potential structures taken from existing material databases. With the ITC dataset of 1633 various substrates for β-Ga2O3 calculated by full density functional theory, we trained our recently developed convolutional neural network (CNN) model that utilizes the fused orbital field matrix (OFM) and composition descriptors. Our model proved to be superior in performance to traditional machine learning algorithms such as random forest and gradient boosting. We then deployed the CNN model to predict the ITC of 32 716 structures in contact with β-Ga2O3. The CNN model predicted the top 20 cubic and noncubic substrates with ITC on the same level as density functional theory (DFT) results on β-Ga2O3/YN and β-Ga2O3/MgO interfaces, which has the highest ITC of 1224 and 1211 MW/m2K, respectively, among the DFT-ITC datasets. Phonon density of states, group velocity, and scattering effect on high heat flux transport and consequently increased ITC are also investigated. Moderate to high phonon density of states overlap, high group velocity, and low phonon scattering are required to achieve high ITC. We also found three Magpie descriptors with strong Pearson correlation with ITC, namely, mean atomic number, mean atomic weight, and mean ground state volume per atom. Calculations of such descriptors are computationally efficient, and therefore, these descriptors provide a new route for quickly screening potential substrates from large-scale material pools for high-performance interfacial thermal management of high-electron mobility transistor devices.

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High throughput substrate screening for interfacial thermal management of β-Ga2O3 by deep convolutional neural network

Al-Fahdi,

2024

Journal of Applied Physics

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Unleashing the power of artificial intelligence in phonon thermal transport: Current challenges and prospects

2024

Journal of Applied Physics

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The discovery of advanced thermal materials with exceptional phonon properties drives technological advancements, impacting innovations from electronics to superconductors. Understanding the intricate relationship between composition, structure, and phonon thermal transport properties is crucial for speeding up such discovery. Exploring innovative materials involves navigating vast design spaces and considering chemical and structural factors on multiple scales and modalities. Artificial intelligence (AI) is transforming science and engineering and poised to transform discovery and innovation. This era offers a unique opportunity to establish a new paradigm for the discovery of advanced materials by leveraging databases, simulations, and accumulated knowledge, venturing into experimental frontiers, and incorporating cutting-edge AI technologies. In this perspective, first, the general approach of density functional theory (DFT) coupled with phonon Boltzmann transport equation (BTE) for predicting comprehensive phonon properties will be reviewed. Then, to circumvent the extremely computationally demanding DFT + BTE approach, some early studies and progress of deploying AI/machine learning (ML) models to phonon thermal transport in the context of structure–phonon property relationship prediction will be presented, and their limitations will also be discussed. Finally, a summary of current challenges and an outlook of future trends will be given. Further development of incorporating AI/ML algorithms for phonon thermal transport could range from phonon database construction to universal machine learning potential training, to inverse design of materials with target phonon properties and to extend ML models beyond traditional phonons.

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High-throughput computational discovery of 3218 ultralow thermal conductivity and dynamically stable materials by dual machine learning models

Abstract: Using dual machine learning models, we identified 3218 inorganic crystals with ultralow lattice thermal conductivity (LTC), which will be of great interest for technologically important applications such as thermal insulators and thermoelectrics.

Cited by 2 publications

References 86 publications

High throughput substrate screening for interfacial thermal management of β-Ga2O3 by deep convolutional neural network

High throughput substrate screening for interfacial thermal management of β-Ga2O3 by deep convolutional neural network

Unleashing the power of artificial intelligence in phonon thermal transport: Current challenges and prospects

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