DMREF: AI-Accelerated Design of Synthesis Routes for Metastable Materials

材料科学和物理学目前面临的重大挑战之一是控制和处理物质远离平衡。该项目旨在确定设计规则和新途径，以合成亚稳态并在环境条件下长时间存活的所需材料。这项研究可以极大地扩展材料设计空间，以实现未来的应用-材料基因组计划（MGI）的目标之一。一个激励人心的例子是室温超导体的挑战。也就是说，最近发现的高压超导体已经达到了室温超导的长期目标。然而，它们很难在实际技术中实施，因为它们在返回到环境压力时会分解。其他材料也遇到类似的问题，如磁铁和超硬系统。在这项工作中，亚稳态材料将开发，以满足这一关键需求，因为他们提供了一个有前途的方式前进在这个重要的front.Technical SUMMARYThe项目解决了科学的非平衡过程，通过搜索和确定的设计规则的合成途径的亚稳态材料。这项工作将建立将无定形前体材料转化为在环境条件下动力学稳定的所需相的方法，防止它们转化为热力学基态。它将结合联合收割机材料信息学和机器学习的数据挖掘和结构预测与应用的外部压力通过金刚石砧单元，温度通过快速激光加热，和高磁场，以转化为所需的相非晶前体材料是亚稳态在环境条件下。该研究有望产生新的亚稳态合成方法，用于结构预测和热力学和动力学表征的机器学习方法，以及具有各种理想特性的亚稳态材料，如超导性，磁性和超硬度。该项目将培训四名初级研究人员，并通过与更广泛的团队互动，教授他们研究项目之外的科学技术和专业技能。该提案还将为实验借阅图书馆开发和提供新的K-12晶体生长实验套件的校内测试。该团队将通过研讨会培训来自美国东南部的科学教师使用该工具包，并将通过邮件向他们提供该工具包。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

NON-TECHNICAL SUMMARYOne of the current grand challenges in materials science and physics is the control and processing of matter away from equilibrium. This project aims to identify design rules and novel pathways to synthesize desired materials that are metastable and survive for long times at ambient conditions. This research can dramatically expand the materials design space to enable future applications – one of the goals of the Materials Genome Initiative (MGI). A motivating example is the challenge of room-temperature superconductors. That is, recently discovered high-pressure hydrides have reached the longstanding goal of room temperature superconductivity. However, they are difficult to implement in practical technologies because they decompose when they return to ambient pressure. Similar problems are encountered with other materials, such as magnets and superhard systems. In this work, metastable materials will be developed to address this critical need as they offer a promising way forward on this important front.TECHNICAL SUMMARYThe project addresses the science of nonequilibrium processes by searching for and identifying the design rules for synthesis pathways of metastable materials. The effort will establish methods to transform amorphous precursor materials into desired phases that are kinetically stable at ambient conditions, preventing their transformation to the thermodynamic ground state. It will combine materials informatics and machine-learning for data mining and structure prediction with the application of external pressure via diamond anvil cells, temperature via fast laser heating, and high magnetic field to transform amorphous precursor materials into desired phases that are metastable at ambient conditions. The research is expected to lead to new metastable synthesis methods, machine learning approaches for structure prediction and characterization of their thermodynamics and kinetics, and metastable materials with a wide variety of desirable properties such as superconductivity, magnetism, and superhardness. The project will train four junior researchers and teach them scientific techniques and professional skills beyond their research projects through interaction with the broader team. The proposal will also develop and provide in-school testing of a new K-12 experimental kit on crystal growth for a Lending Library of Experiments. The team will train science teachers from across the southeastern U.S. in its use through workshops, and it will make the kits available to them by mail.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.