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

渴望:摘要：非技术摘要：具有目标导热系数的材料的高通量计算筛选有能力改变许多行业，如热电发电和高性能微/纳米电子器件。在运行过程中产生大量的多余热量，寻找具有高导热性的材料对于这种微/纳米电子学的颠覆性发展至关重要，以延长其工作寿命并提高可靠性。由于目前基于第一性原理的导热系数计算需要大量的计算资源，并且由于材料的原子结构与热输运性质之间的高度复杂和非线性关系，理论模型面临挑战，因此这种潜力尚未实现。深度学习已经改变了越来越多的大数据领域，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仍未被开发。这个为期两年的EAGER项目旨在开发新的深度神经网络技术，以实现快速准确的热导率计算预测，用于高通量热材料的发现。开发可靠、快速、准确的深度学习模型是实现高通量热材料筛选实验验证和实际应用的必要条件。同时，该项目旨在通过吸引少数族裔和代表性不足的学生参与STEM研究来提高多样性。参与者还将加深对热输运原子模拟和大数据分析的理解；从而促进劳动力发展。技术总结：深度学习算法在计算机科学中得到了很好的发展，而从原子结构直接预测导热系数在材料科学中已经成为可能。然而，直观地结合这一进展并不是一件容易的事情，因为科学数据（材料的导热性）无法迅速扩展到深度学习所需的水平。为此，本项目将利用异构多分辨率导热系数数据和稀缺数据，训练高效、准确的深度学习模型，这是此前从未实现的类alphago深度学习模型。在这个探索阶段，重点将放在(1)开发图和空间三维卷积神经网络（cnn），通过利用其自动分层特征学习和非线性映射学习来进行导热系数建模；(2)开发基于多分辨率数据的深度神经网络模型，用于导热系数预测。(3)对导热系数极高或极低的预测材料进行实验和基于dft的计算验证。一个强大的、可靠的、高精度的热导率预测深度学习模型将促进工业上先进功能材料的发展，如能量转换、存储和热管理。该奖项反映了美国国家科学基金会的法定使命，并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。

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.