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

EAGER：通过基于深度学习的高通量计算筛选来发现热材料

• 批准号: 1905775
• 负责人: Jianjun Hu
• 金额: $ 30万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1905775&HistoricalAwards=false
• 关键词: EAGER; Thermal; Materials; Discovery; via

EAGER: Thermal Materials Discovery via Deep Learning based High-Throughput Computational ScreeningAbstract:Non-technical summary: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.Technical summary:Deep learning algorithm has been well developed in computer science, while direct thermal conductivity prediction from atomic structures has been made available in materials science. However, intuitive combination of this progress is not an easy task, since the scientific data (thermal conductivity of materials) cannot be quickly expanded to the level required by deep learning. To this end, this project will use heterogeneous multi-resolution thermal conductivity data and scarce data for training efficient and accurate deep learning models, which has never been realized for AlphaGO-like deep learning models before. In this exploratory stage, the focus will be on (1) developing graph and spatial 3D convolutional neural networks (CNNs) for thermal conductivity modeling by exploiting their automated hierarchical feature learning and non-linear mapping learning, and (2) developing multi-resolution data based deep neural network models for thermal conductivity prediction. (3) Experimental and DFT-based computational validation of predicted materials with extremely high or low thermal conductivity. A robust, reliable, and high accuracy deep learning model for thermal conductivity prediction will facilitate development of advanced functional materials in industry, such as energy conversion, storage, and thermal management.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

EAGER：通过基于深度学习的高吞吐量计算筛选发现热材料摘要：非技术性总结：具有目标热导率的材料的高通量计算筛选具有改变许多行业的能力，例如热电发电和高性能微/纳米电子器件，其在操作期间产生大量的过量热，并且寻找具有高热导率的材料对于这种微/纳米电子器件的颠覆性发展是极其重要的，以便延长其工作寿命并增加可靠性。由于当前的第一-由于从材料的原子结构到热输运的高度复杂和非线性关系，特性.深度学习已经改变了越来越多的大数据可用领域，如图像和语音识别以及医学图像分析。然而，尽管材料科学具有很高的经济潜力，但深度学习在很大程度上尚未开发。这个为期两年的EAGER项目旨在开发新型深度神经网络技术，以实现快速准确的热导率计算预测，用于高通量热材料发现。开发可靠、快速、准确的深度学习模型是高通量热材料筛选实验验证和实际应用的必要条件。同时，该计划将旨在通过吸引少数民族和代表性不足的学生参与STEM研究来增强多样性。技术总结：深度学习算法在计算机科学中已经得到了很好的发展，而直接从原子结构预测热导率在材料科学中已经可用。然而，将这一进展直观地结合起来并不是一件容易的事情，因为科学数据（材料的热导率）无法快速扩展到深度学习所需的水平。为此，该项目将使用异构多分辨率热导率数据和稀缺数据来训练高效准确的深度学习模型，这在以前从未实现过AlphaGO类深度学习模型。在这个探索阶段，重点将是（1）开发图形和空间3D卷积神经网络（CNN），通过利用其自动分层特征学习和非线性映射学习进行热导率建模，以及（2）开发基于多分辨率数据的深度神经网络模型，用于热导率预测。(3)对具有极高或极低热导率的预测材料进行实验和基于DFT的计算验证。强大、可靠、高精度的深度学习热导率预测模型将促进能源转换、存储和热管理等行业中先进功能材料的开发。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

First-Principles Investigation of Ti 2 CSO and Ti 2 CSSe Janus MXene Structures for Li and Mg Electrodes

用于锂和镁电极的 Ti 2 CSO 和 Ti 2 CSSe Janus MXene 结构的第一性原理研究

• DOI: 10.1021/acs.jpcc.1c00082
• 发表时间: 2021
• 期刊: The Journal of Physical Chemistry C
• 作者: Siriwardane, Edirisuriya M.;Hu, Jianjun
• 通讯作者: Hu, Jianjun

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

• DOI: 10.1021/acsomega.9b04012
• 发表时间: 2020-02
• 期刊: ACS Omega
• 影响因子: 4.1
• 作者: Yong Zhao;Yuxin Cui;Zheng Xiong;Jing Jin;Zhonghao Liu;Rongzhi Dong;Jianjun Hu

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

• DOI: 10.1021/acs.jpcc.0c02348
• 发表时间: 2020-03
• 期刊: ArXiv
• 作者: Yong Zhao;Kunpeng Yuan;Yinqiao Liu;Steph-Yves M. Louis;Ming Hu;Jianjun Hu

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

• DOI: 10.1016/j.commatsci.2021.110686
• 发表时间: 2021-07-06
• 期刊: COMPUTATIONAL MATERIALS SCIENCE
• 影响因子: 3.3
• 作者: Li, Yuxin;Dong, Rongzhi;Hu, Jianjun
• 通讯作者: Hu, Jianjun

TCSP: a Template-Based Crystal Structure Prediction Algorithm for Materials Discovery

• DOI: 10.1021/acs.inorgchem.1c03879
• 发表时间: 2022-06-06
• 期刊: INORGANIC CHEMISTRY
• 影响因子: 4.6
• 作者: Wei, Lai;Fu, Nihang;Hu, Jianjun
• 通讯作者: Hu, Jianjun