CDS&E: Systematic Exploration of the High Entropy Alloy Space through High-Dimensional Thermodynamic Modeling from High-Throughput Computations and Experimental Data

材料科学领域已经被一类被称为“高熵”合金的合金的发现所吸引，这种合金的特点是通过大量不同元素的相当数量的组合，使简单的晶体结构意想不到的稳定。这种合金的发现和设计需要详细了解在具有潜在的许多竞争晶体结构的高维成分空间中控制稳定性的热力学因素。由于需要大量的输入数据和进入模型的参数，这种复杂性测试了当前热力学建模能力的极限。该项目通过结合(i)实验导出的热力学模型的大规模元数据库和（ii）高通量量子力学计算的热力学数据，使用正式统计和主动机器学习技术来解决这个问题。这项工作的最终成果是一个公开分布的大规模热力学模型，可以在高维成分空间中查询，并返回结构稳定性信息，如交互式三维横截面，或作为依赖于成分和温度的热力学性质。与已知的高熵合金相比，这种可能的合金空间是如此之大，通过这种工具发现新型合金的潜力是巨大的，这可能会广泛影响许多工程应用，这些应用的能力受到材料特性的限制。技术总结该项目利用并整合了PI小组最近的两项发展：(i)一个搜索引擎（热力学数据库数据库或TDBDB），索引所有可用的实验衍生的热力学数据，以标准格式在科学文献中可用；（ii）一套软件工具（合金理论自动化工具包或ATAT），它简化了从头算数据生成热力学数据库的过程。这种混合方法旨在结合最先进的实验和计算方法的独特优势，即前者的更高准确性和后者的高通量性质。主动机器学习和统计技术用于(i)目标探索有希望的组成区域，可能形成具有简单晶体结构的固溶体；（ii）开发非化学计量固体的有效统计力学模型，需要很少的从头计算输入，从而实现“高通量”操作。现有的高通量计算工作主要集中在绝对零度下无缺陷化学计量化合物的性质，而该项目的目标是在所有温度下更广泛的材料范围，包括可能具有短程有序的无序合金和可能具有点缺陷的有序合金。这需要有效的统计力学模型(i)短程有序，（ii）强非谐相和（iii）磁有序，每一个都需要比现有的蛮力方法更少的从头算输入，利用已知和数据挖掘的趋势。在这个项目中设计的数据和工具有望产生更广泛的影响：几乎所有的工程材料都是非化学计量合金，其性能是通过控制添加大量成分来调整的（高熵合金只是一个极端的例子）。因此，这种资源可以极大地促进材料的发现和优化，通过增加现有的计算高通量的努力，目前只针对有序化合物在绝对零度。广泛的成分范围也使应用范围从识别玻璃形成的金属合金到确定可能的系外行星核心成分和结构。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Nontechnical summaryThe field of materials science has been captivated by the discovery of a class of alloys known as "high entropy" alloys, which are characterized by the unexpected stabilization of simple crystal structures through the combination of a large number of different elements in comparable amounts. The discovery and design of such alloys demand a detailed knowledge of the thermodynamic factors governing stability in a very high dimensional composition space with potentially many competing crystal structures. This complexity tests the limits of current thermodynamic modeling capabilities due to the sheer number of input data needed and of parameters entering the model. The project addresses this by combining (i) large-scale meta-databases of experimentally-derived thermodynamic models with (ii) thermodynamic data from high-throughput quantum-mechanical calculations, using formal statistical and active machine learning techniques. The end product of this effort is an openly distributed large-scale encompassing thermodynamic model that can be queried in a high-dimensional compositional space and that returns structural stability information as interactive tridimensional cross-sections, or as composition- and temperature-dependent thermodynamic properties. As this space of possible alloys is so vast compared to the number of known high-entropy alloys, the potential for discoveries of novel alloys through this tool is significant and this could broadly impact numerous engineering applications where capabilities are limited by materials properties.Technical summaryThe project leverages and integrates two recent developments from the PI's group: (i) a search engine (the Thermodynamic DataBase DataBase or TDBDB), that indexes all available experimentally-derived thermodynamic data electronically available in standardized format in the scientific literature; (ii) a suite of software tools (the Alloy Theoretic Automated Toolkit or ATAT) that streamlines the generation of thermodynamic databases from ab initio data. This hybrid approach aims to combine the distinct advantage of state-of-the-art experimental and computational methods, namely, the higher accuracy of the former and the high-throughput nature of the latter.Active machine learning and statistical techniques are used to (i) target the exploration of promising composition regions likely to form solid solutions with simple crystal structures and (ii) develop efficient statistical mechanics models of non-stoichiometric solids that require few ab initio inputs, thus enabling "high-throughput" operation. Whereas existing computational high-throughput efforts primarily focus on the properties of defect-free stoichiometric compounds at absolute zero, this project targets, at all temperatures, the broader range of materials including disordered alloys with possible short-range order and ordered alloys with possible point defects. This demands efficient statistical mechanical models of (i) short-range order, (ii) strongly anharmonic phases and (iii) magnetic ordering, each of which requiring fewer ab initio input than existing brute force methods, by exploiting both known and data-mined trends.The data and tools devised during this project are expected to have broader impacts: virtually all engineering materials are nonstoichiometric alloys whose properties are tuned by controlled additions of numerous components (with high entropy alloys only representing an extreme example). Hence, this resource could greatly facilitate materials discovery and optimization by augmenting existing computational high-throughput efforts that currently only target ordered compounds at absolute zero. The broad compositional gamut covered also enables applications that could range from identifying glass-forming metallic alloys to determining possible exoplanet core compositions and structures.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.