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

设计材料革新和设计我们的未来(DMREF)拨款的研究团队将着手一个项目，重点是加速发现具有优异性能的新的先进材料，这些材料是制造复杂系统中关键部件所需的，例如用于下一代清洁能源生产系统的涡轮机叶片、工业脱碳系统的部件和交通运输。该项目将探索一种特殊类型的高性能合金(可打印的耐火合金)，这种合金在高温下坚固耐用，并可使用3D打印进行制造。这一点很重要，因为3D打印允许进行更复杂的部件设计，提高能源效率，并减少下一代系统的排放。加速发现可印刷耐火合金的新框架还将确保所发现的材料和制造的部件对全球供应链中断具有弹性，这意味着即使在经济、社会或地缘政治风险引发意外供应链冲击的情况下，也可以轻松获得这些材料和部件。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。该项目解决了贝叶斯优化中材料发现的一个重要限制：问题公式的静态性质--即，优化什么量，保持什么量高于或低于阈值，以及迭代过程开始后改变什么输入。以加速发现对清洁发电、工业脱碳和运输至关重要的可印刷耐火合金(PRA)为重点，将使用一个动态、自适应的框架，实时修正问题空间，将不断演变的约束和决策者的偏好整合在一个无缝迭代的材料发现循环中。智能的优点在于在多信息源、批处理贝叶斯优化框架内创建半自主的、人在环中的问题描述方案。这种新的方法保证了效率和适应性，吸收新的决策者输入，改进问题公式，并快速产生一致的解决方案。更广泛的影响是双重的：该项目支持的学生参与数据支持的能源材料发现和开发(D3EM)研究生证书计划，将为他们提供跨学科培训，以满足材料基因组计划(MGI)的劳动力发展需求。此外，该项目的性能、可制造性和供应链考虑因素的联合设计战略将产生广泛的影响，超越PRA的发现和设计，潜在地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。

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.