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

DMREF：通过集成材料和加工协同设计 (OPTIMA) 优化可打印耐火合金的问题表述

• 批准号: 2323611
• 负责人: Raymundo Arroyave
• 金额: $ 179.97万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-15 至 2028-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323611&HistoricalAwards=false
• 关键词: DMREF; Optimizing; Problem; formulation; prinTable

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.

设计材料以彻底改变和设计我们的未来 (DMREF) 资助的研究团队将着手一个项目，重点是加速发现具有卓越性能的新型先进材料，用于制造复杂系统中的关键部件，例如下一代涡轮叶片。该项目将探索一类特殊的高性能合金（可打印耐火合金），这种合金在高温下坚固耐用，并且适合使用 3D 制造。这很重要，因为 3D 打印可以实现更复杂的零件设计，提高能源效率，并减少下一代系统的排放。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导致的意外供应链冲击的情况下，也可以轻松获得它们。该项目结合了先进的实验技术、模拟、机器学习和人工智能，加速合金和过程共同发现，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大限制：问题表述的静态性质——即，要优化哪些数量，要保持高于或低于阈值的数量，以及迭代过程开始后需要改变的输入重点是可打印耐火合金（PRA）的加速发现，这对清洁发电、工业脱碳和运输至关重要，这是一个修正问题的动态、适应性框架。将使用实时空间，将不断变化的约束和决策者偏好集成到无缝迭代材料发现循环中，其智力优点在于在多系统中创建半自主、人机交互的问题制定方案。信息源，批量贝叶斯优化框架。这种新颖的方法保证了效率和适应性，吸收了新的决策者的输入，改进了问题的表述，并快速产生了一致的解决方案。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、可制造性和性能方面的共同设计策略。供应链考虑因素将产生超出 PRA 发现和设计之外的广泛影响，有可能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点和技术进行评估，被认为值得支持。更广泛的影响审查标准。