skip to main content

Title: Predicting lattice thermal conductivity from fundamental material properties using machine learning techniques
High-throughput screening and material informatics have shown a great power in the discovery of novel materials, including batteries, high entropy alloys, and photocatalysts. However, the lattice thermal conductivity ( κ ) oriented high-throughput screening of advanced thermal materials is still limited to the intensive use of first principles calculations, which is inapplicable to fast, robust, and large-scale material screening due to the unbearable computational cost demanding. In this study, 15 machine learning algorithms are utilized for fast and accurate κ prediction from basic physical and chemical properties of materials. The well-trained models successfully capture the inherent correlation between these fundamental material properties and κ for different types of materials. Moreover, deep learning combined with a semi-supervised technique shows the capability of accurately predicting diverse κ values spanning 4 orders of magnitude, especially the power of extrapolative prediction on 3716 new materials. The developed models provide a powerful tool for large-scale advanced thermal functional materials screening with targeted thermal transport properties.  more » « less
Award ID(s):
2110033 2030128
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Journal of Materials Chemistry A
Page Range / eLocation ID:
5801 to 5810
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. High-throughput computational screening of materials with targeted thermal conductivity ( κ ) plays an important role in promoting the advancement of material design and enormous applications. The Slack model has been widely applied for the fast evaluation of κ with minimal time and resources, showing the potential capability of high-throughput screening of κ . However, after examining the Slack model on a large set of 353 materials, a huge discrepancy is found between the predicted κ and the correspondingly measured κ in experiments for some materials in addition to the generally overestimated κ by the Slack model. Thus, it is necessary to optimize the Slack model for efficiently and accurately evaluating κ . In this study, based on the high-throughput comparison of the κ predicted by the Slack model using elastic properties and those measured in experiments, an optimized Slack model is proposed. As a result, the κ predicted by the optimized Slack model agrees reasonably with the κ measured in experiments, which is much better than the previous prediction. The optimized Slack model proposed in this study can be used for further high-throughput computational evaluation of κ , which would be helpful for finding materials of ultrahigh or ultralow κ with broad applications. 
    more » « less
  2. Data driven generative deep learning models have recently emerged as one of the most promising approaches for new materials discovery. While generator models can generate millions of candidates, it is critical to train fast and accurate machine learning models to filter out stable, synthesizable materials with the desired properties. However, such efforts to build supervised regression or classification screening models have been severely hindered by the lack of unstable or unsynthesizable samples, which usually are not collected and deposited in materials databases such as ICSD and Materials Project (MP). At the same time, there is a significant amount of unlabelled data available in these databases. Here we propose a semi-supervised deep neural network (TSDNN) model for high-performance formation energy and synthesizability prediction, which is achieved via its unique teacher-student dual network architecture and its effective exploitation of the large amount of unlabeled data. For formation energy based stability screening, our semi-supervised classifier achieves an absolute 10.3% accuracy improvement compared to the baseline CGCNN regression model. For synthesizability prediction, our model significantly increases the baseline PU learning's true positive rate from 87.9% to 92.9% using 1/49 model parameters. To further prove the effectiveness of our models, we combined our TSDNN-energy and TSDNN-synthesizability models with our CubicGAN generator to discover novel stable cubic structures. Out of the 1000 recommended candidate samples by our models, 512 of them have negative formation energies as validated by our DFT formation energy calculations. Our experimental results show that our semi-supervised deep neural networks can significantly improve the screening accuracy in large-scale generative materials design. Our source code can be accessed at https://git/ 
    more » « less
  3. Abstract Although first principles based anharmonic lattice dynamics is one of the most common methods to obtain phonon properties, such method is impractical for high-throughput search of target thermal materials. We develop an elemental spatial density neural network force field as a bottom-up approach to accurately predict atomic forces of ~80,000 cubic crystals spanning 63 elements. The primary advantage of our indirect machine learning model is the accessibility of phonon transport physics at the same level as first principles, allowing simultaneous prediction of comprehensive phonon properties from a single model. Training on 3182 first principles data and screening 77,091 unexplored structures, we identify 13,461 dynamically stable cubic structures with ultralow lattice thermal conductivity below 1 Wm −1 K −1 , among which 36 structures are validated by first principles calculations. We propose mean square displacement and bonding-antibonding as two low-cost descriptors to ease the demand of expensive first principles calculations for fast screening ultralow thermal conductivity. Our model also quantitatively reveals the correlation between off-diagonal coherence and diagonal populations and identifies the distinct crossover from particle-like to wave-like heat conduction. Our algorithm is promising for accelerating discovery of novel phononic crystals for emerging applications, such as thermoelectrics, superconductivity, and topological phonons for quantum information technology. 
    more » « less
  4. Thermoelectric materials, which can convert waste heat into electricity or act as solid‐state Peltier coolers, are emerging as key technologies to address global energy shortages and environmental sustainability. However, discovering materials with high thermoelectric conversion efficiency is a complex and slow process. The emerging field of high‐throughput material discovery demonstrates its potential to accelerate the development of new thermoelectric materials combining high efficiency and low cost. The synergistic integration of high‐throughput material processing and characterization techniques with machine learning algorithms can form an efficient closed‐loop process to generate and analyze broad datasets to discover new thermoelectric materials with unprecedented performances. Meanwhile, the recent development of advanced manufacturing methods provides exciting opportunities to realize scalable, low‐cost, and energy‐efficient fabrication of thermoelectric devices. This review provides an overview of recent advances in discovering thermoelectric materials using high‐throughput methods, including processing, characterization, and screening. Advanced manufacturing methods of thermoelectric devices are also introduced to realize the broad impacts of thermoelectric materials in power generation and solid‐state cooling. In the end, this article also discusses the future research prospects and directions.

    more » « less
  5. The process of developing new compounds and materials is increasingly driven by computational modeling and simulation, which allow us to characterize candidates before pursuing them in the laboratory. One of the non-trivial properties of interest for organic materials is their packing in the bulk, which is highly dependent on their molecular structure. By controlling the latter, we can realize materials with a desired density (as well as other target properties). Molecular dynamics simulations are a popular and reasonably accurate way to compute the bulk density of molecules, however, since these calculations are computationally intensive, they are not a practically viable option for high-throughput screening studies that assess material candidates on a massive scale. In this work, we employ machine learning to develop a data-derived prediction model that is an alternative to physics-based simulations, and we utilize it for the hyperscreening of 1.5 million small organic molecules as well as to gain insights into the relationship between structural makeup and packing density. We also use this study to analyze the learning curve of the employed neural network approach and gain empirical data on the dependence of model performance and training data size, which will inform future investigations. 
    more » « less