DMREF: AI-Guided Accelerated Discovery of Multi-Principal Element Multi-Functional Alloys

形状记忆合金(SMA)是一类在施加应力、磁场和/或温度变化时发生可逆和可重复的马氏体相变(MT)的金属合金。这些转变可以实现广泛的技术，包括紧凑型固态执行器、固态冰箱、热存储和管理系统，以及针对广泛温度变化保持稳定的结构。不幸的是，目前的合金配方(化学成分相对简单)已被发现在性能上有很大限制，这阻碍了它们在变革性技术中的广泛应用。这推动了该领域向化学成分日益复杂且大量存在三到四种以上成分的合金[即多主元素多功能合金(MPEMFA)]的研究方向发展。在这个广袤的化学空间中航行是极具挑战性的。为了应对这一挑战，该项目将开发一种新型的闭环材料设计框架，它可以集成实验、计算材料科学模型和机器学习(ML)/人工智能(AI)方法，并具有定制的接口，连接实验、模型、现有数据，更重要的是，连接跨学科的研究人员。这个设计材料以革新和设计我们的未来(DMREF)项目旨在加强对一类重要材料的理解，以实现广泛的技术。参与项目的学生将在材料基因组计划(MGI)的精神下接受跨学科材料发现方法的培训。该项目旨在发现具有极端性能组合的MPEMFA，例如具有低滞后的超高温马氏体相变(MTS)、在压力下稳定的可逆形状变化、在远远超出最先进水平的温度下的超弹性；极端性能，如高达800°C的因瓦和Elinvar效应；或独特的量身定制的性能，如SMA-As相变材料(PCM)，具有高导热系数和相变热，但也具有非常不同的MT温度。为了在这个巨大的化学空间中航行，将开发一个新的框架：(I)使用新的物理信息机器学习来有效地识别可进行优化的可行区域；(Ii)融合模拟和实验以获得高效的ML模型；(Iii)开发新的批次(并行)贝叶斯优化(BO)策略以进行全局最优迭代实验设计；以及(Iv)能够同时考虑多个目标和约束。其目的是超越加速发现，寻求解决关于导致MPEMFA多功能行为的潜在因素的问题。生成的元数据，连同计算和ML模型、开放访问代码、端到端工作流以及高质量的数据库，将为在数据稀缺的情况下学习复杂系统，特别是在ML/AI驱动的材料发现中开发和验证ML/AI框架提供试验台。该项目将利用最近建立的关于材料科学、信息学和设计的跨学科研究生证书，即数据支持的能源材料发现和设计(D3EM)，来培训由这一努力支持的博士生，为材料基因组计划的劳动力发展目标做出贡献。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。

Shape Memory Alloys (SMAs) are a class of metallic alloys that undergo reversible and repeatable martensitic transformations (MT) upon applying stress, magnetic fields, and/or temperature changes. These transformations can enable a wide range of technologies, including compact solid-state actuators, solid-state refrigerators, thermal storage and management systems, and structures that are stable against wide temperature changes. Unfortunately, current alloy formulations (with relatively simple chemistries) have been found to have significant limitations in their performance that prevent their widespread deployment in transformative technologies. This has pushed the field towards exploring alloys with increasingly complex chemistries and with more than three or four constituents being present in significant amounts [i.e., multi-principal element multi-functional alloys (MPEMFAs)]. Navigating this vast chemical space is extremely challenging. To address this challenge, this project will develop a novel closed-loop materials design framework, which can integrate experiments, computational materials science models, and machine learning (ML) / artificial intelligence (AI) approaches, with customized interfaces connecting experiments, models, existing data, and more critically, researchers across disciplines. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project aims to result in an enhanced understanding of an important class of materials to enable a wide range of technologies. Participating students will be trained in interdisciplinary approaches to materials discovery in the spirit of the Materials Genome Initiative (MGI).This project aims to discover MPEMFAs with extreme property combinations, such as ultra-high temperature martensitic transformations (MTs) with low hysteresis, stable reversible shape change under stress, superelasticity at temperatures significantly beyond state-of-the-art; extreme properties, such as Invar and Elinvar effects up to 800°C; or uniquely tailored properties, such as SMAs-as-phase-change-materials (PCMs) with high thermal conductivity and transformation enthalpy but also with widely different MT temperatures. To navigate this vast chemical space a new framework will be developed that: (i) employs novel physics-informed machine learning to efficiently identify the feasible regions amenable to optimization; (ii) fuses simulations and experiments to obtain efficient ML models; (iii) develops new Batch (parallel) Bayesian Optimization (BO) strategies to make globally optimal iterative experimental design; and (iv) is capable of simultaneously considering multiple objectives and constraints. The aim is to go beyond accelerated discovery, seeking to address questions about the underlying factors responsible for the multi-functional behavior in MPEMFAs. The generated metadata, together with the computation and ML models, open-access code, end-to-end workflows, as well as high quality databases, will provide a testbed for developing and validating ML/AI frameworks when learning complex systems under data scarcity, particularly in ML/AI-drive materials discovery. The project will leverage the recently established interdisciplinary graduate certificate on materials science, informatics and design, Data-Enabled Discovery and Design of Energy Materials (D3EM), to train the PhD students supported by this effort, contributing to the workforce development goals of the Materials Genome Initiative.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.