Collaborative Research: Elements: Phonon Database Generation, Analysis, and Visualization for Data Driven Materials Discovery

材料数据库及其相关的计算基础设施已成为当前数据驱动和基于人工智能的材料发现的主要基石。然而，在材料界感兴趣的丰富材料性质中，很少有数据库全面包括声子性质，声子性质是材料科学的中心，是发现具有多种功能的材料的关键。该项目满足了这些迫切的需求，即生成一个全面的声子数据库，以及分析、可视化、导航和可视化工具，并结合多渠道基础设施-社区通信和反馈。声子数据库将成为对目前广泛使用的材料数据库的极好补充。发展这样的基础设施将有利于材料科学和工程的各个领域，加快现代科学技术中各种新兴应用的新材料的预测、设计和合成。该项目将促进未被充分代表的少数族裔学生参与研究，为工科学生提供对其未来职业生涯至关重要的跨学科专业知识和前沿知识，并完成为科学、技术和工程培养高素质劳动力的使命。该项目还将为本科生和研究生课程开发新的课程材料。高质量的材料性质数据一直是最大限度地发挥现代人工智能和机器学习在大规模计算材料发现中的潜力的主要瓶颈，这导致了许多新的无机晶体材料的发现，这些材料极大地提高了人类的生活质量。这个项目的目标是提供(1)一个关于两个主要数据集的全面的声子数据库：一个由大约40,000个声子色散和15,000个现有晶体结构的晶格热导率组成，这些结构在热力学上是稳定的，并且完全由第一性原理计算；另一个数据库包括通过深度学习模型预测并部分由第一性原理验证的300,000个结构的声子相关性质，(2)一个网络服务器，供非营利性研究人员有效检索、快速导航、可视化和比较大型声子带结构以准确确定感兴趣的材料，以及(3)预测声子性质的工具包。我们的多渠道可通信新声子数据库和用户交互工具集将使广泛的材料物理、化学和工程社区受益，在许多具有社会重要意义的领域为新材料发现提供新的合作机会。该奖项由NSF高级网络基础设施办公室联合材料研究部支持。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。

Material databases and their related computing infrastructures have become the major cornerstone of current data driven and artificial intelligence based materials discovery. However, among the rich material properties of interest to the materials community, few databases have comprehensively included phonon properties, which are at the center of materials science and are key to discover materials with diverse functionalities. This project meets these urgent needs to generate a comprehensive phonon database along with analysis, visualization, navigation, and visualization tools, combined with multi-channel infrastructure-community communication and feedback. The phonon database will become an excellent complement to the currently widely used material databases. Developing such an infrastructure will be beneficial for all areas of materials science and engineering, accelerating the prediction, design, and synthesis of novel materials with various emerging applications in modern science and technology. The project will promote the engagement of underrepresented and minority students in research, equip engineering students with interdisciplinary expertise and frontier knowledge crucial to their future careers, and fulfill the mission to prepare a high-quality workforce for science, technology, and engineering. The project will also develop new course materials for undergraduate and graduate computational materials science courses.High-quality material property data has always been a major bottleneck for maximizing the potential of modern artificial intelligence and machine learning in large-scale computational material discovery, which has led to the discovery of numerous new inorganic crystalline materials that have dramatically improved the quality of human life. This project aims to deliver (1) a comprehensive phonon database of two major datasets: one consists of about 40,000 phonon dispersions and 15,000 lattice thermal conductivity of existing crystal structures that are thermodynamically stable and fully computed by first-principles; the other consists of phonon related properties of 300,000 structures predicted by deep learning models and partially validated by first-principles, (2) a web server for non-profit researchers to effectively retrieve, quickly navigate, visualize, and compare large pools of phonon band structures to pinpoint the materials of interest, and (3) toolkits for predicting phonon properties. Our multi-channel communicable new phonon database and user-interactive toolsets will benefit broad communities of material physics, chemistry, and engineering with new collaborative opportunities for novel materials discovery in many societally important areas.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Materials Research.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.