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

ENGER：基于深度学习的高通量计算筛选热材料发现摘要：具有目标导热系数的材料的高通量计算筛选能够改变许多行业，如热电和高性能微纳电子器件，这些行业在运行过程中产生大量过剩热量，寻找具有高导热系数的材料对于此类微纳电子设备的颠覆性发展极其重要，以延长其工作寿命和增加可靠性，由于从材料的原子结构到热输运性质的高度复杂和非线性关系，当前基于第一性原理的热导率计算需要巨大的计算资源，以及理论模型的挑战，这一潜力尚未得到实现。深度学习已经改变了越来越多大数据可用的领域，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，但它在很大程度上仍未被深度学习所开发。这个为期两年的迫切项目旨在开发新的深度神经网络技术，为高通量热材料发现实现快速准确的导热系数计算预测。开发可靠、快速、准确的深度学习模型是高通量热力材料筛选实验验证和实际应用的需要。同时，该计划旨在通过吸引少数族裔和代表性不足的学生参与STEM研究来增强多样性。学员还将加深对热传输的原子模拟和大数据分析的理解，从而为劳动力发展做出贡献。技术摘要：深度学习算法在计算机科学中得到了很好的开发，而根据原子结构直接预测导热系数已在材料科学中实现。然而，直观地结合这一进展并非易事，因为科学数据(材料的导热系数)不能迅速扩展到深度学习所需的水平。为此，本项目将利用异质多分辨率导热系数数据和稀缺数据来训练高效准确的深度学习模型，这是以前类似AlphaGO的深度学习模型从未实现过的。在这一探索阶段，重点将集中在(1)利用图形和空间三维卷积神经网络(CNN)的自动分层特征学习和非线性映射学习来开发用于热导率建模的图形和空间卷积神经网络(CNN)，以及(2)开发用于热导率预测的基于多分辨率数据的深层神经网络模型。(3)对具有极高或极低导热系数的预测材料的实验和基于DFT的计算验证。用于热导率预测的稳健、可靠和高精度的深度学习模型将促进工业中先进功能材料的开发，如能量转换、存储和热管理。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.