DMREF: Optimizing Problem formulation for prinTable refractory alloys via Integrated MAterials and processing co-design (OPTIMA)

这项设计材料革命和工程我们的未来（DMREF）赠款的研究团队将着手一个项目，重点是加速发现具有制造复杂系统中关键组件所需的上级性能的新型先进材料，例如下一代清洁能源生产系统的涡轮机叶片，工业脱碳系统的组件和运输。该项目将探索一类特殊的高性能合金（可打印的耐火合金），这些合金在高温下坚固耐用，并且可以使用3D打印进行制造。这一点很重要，因为3D打印允许更复杂的零件设计，提高能源效率，并减少下一代系统的排放。加速发现可打印耐火合金的新框架还将确保所发现的材料和制造的组件能够抵御全球供应链中断，这意味着即使在经济，社会或地缘政治风险造成的意外供应链冲击的情况下，也可以轻松获得。该项目结合了先进的实验技术、模拟、机器学习和人工智能，以加速合金和工艺的共同发现，与材料基因组计划保持一致。该项目解决了材料发现贝叶斯优化的一个重要限制：问题制定的静态性质-即，什么量要优化、什么量要保持在阈值之上或之下、以及一旦迭代过程开始要改变什么输入。专注于加速发现可打印耐火合金（PRA），这对清洁发电，工业脱碳和运输至关重要，将使用一个动态的自适应框架，实时修改问题空间，将不断变化的约束条件和决策者偏好整合到无缝迭代材料发现循环中。智力的优点在于创建一个半自主的，人在回路中的问题制定计划内的多信息源，批量贝叶斯优化框架。这种新的方法保证了效率和适应性，吸收新的决策者的输入，精炼问题的配方，并迅速产生对齐的解决方案。更广泛的影响是双重的：由该项目支持的学生参与能源材料的数据驱动发现和开发（D3 EM）研究生证书课程将为他们提供跨学科的培训，以满足材料基因组计划（MGI）的劳动力发展需求。此外，该项目在性能、可制造性和供应链考虑方面的协同设计策略将产生广泛的影响，而不仅仅是PRA的发现和设计，可能会改变许多行业的材料开发方式。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。

The research team on this Designing Materials to Revolutionize and Engineer our Future (DMREF) grant will embark on a project that focuses on the accelerated discovery of new advanced materials with superior properties needed to fabricate critical components in complex systems, such as turbine blades for next-generation clean energy production systems, components for industrial de-carbonization systems, and transportation. The project will explore a particular class of high-performance alloys (printable refractory alloys) that are strong and durable at elevated temperatures and amenable to fabrication using 3D printing. This is important because 3D printing allows for more complex part design, bolsters energy efficiency, and reduces emissions in next-generation systems. The new framework for the accelerated discovery of printable refractory alloys will also ensure that the materials discovered and components fabricated are resilient to global supply chain disruptions, meaning they can be readily acquired even in the case of unexpected supply chain shocks originating from economic, societal, or geo-political risks. The project combines advanced experimental techniques, simulations, machine learning, and artificial intelligence to accelerate alloy and process co-discovery, aligning with the Materials Genome Initiative.This project addresses a significant limitation in Bayesian optimization for materials discovery: the static nature of the problem formulation––i.e., what quantities to optimize, what quantities to keep above or below a threshold value, and what inputs to change once the iterative process begins. Focusing on the accelerated discovery of printable refractory alloys (PRAs), critical for clean power generation, industrial decarbonization, and transportation, a dynamic, adaptive framework that revises the problem space in real-time, integrating evolving constraints and decision-maker preferences within a seamless iterative materials discovery loop will be used. The intellectual merit lies in creating a semi-autonomous, human-in-the-loop problem formulation scheme within a multi-information source, batch Bayesian optimization framework. This novel approach promises both efficiency and adaptability, ingesting new decision-maker inputs, refining problem formulations, and rapidly producing aligned solutions. The broader impacts are twofold: participation of students supported by this project on the Data-Enabled Discovery and Development of Energy Materials (D3EM) graduate certificate program will provide them with interdisciplinary training that addresses the workforce development needs of the Materials Genome Initiative (MGI). Additionally, the project's co-design strategies for performance, manufacturability, and supply chain considerations will have a broad impact beyond the discovery and design of PRAs, potentially transforming how materials are developed across many industries.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.