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

CDS

• 批准号: 2001411
• 负责人: Axel van de Walle
• 金额: $ 37.97万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2001411&HistoricalAwards=false
• 关键词: CDS& Systematic; Exploration; Entropy; Alloy

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.

非技术摘要材料科学领域一直着迷于一类被称为“高熵”合金的合金的发现，这种合金的特点是通过大量数量相当的不同元素的组合，使简单的晶体结构意外地稳定。此类合金的发现和设计需要详细了解在具有潜在许多竞争晶体结构的极高维成分空间中控制稳定性的热力学因素。由于所需的输入数据和进入模型的参数数量巨大，这种复杂性测试了当前热力学建模能力的极限。该项目通过使用正式统计和主动机器学习技术，将（i）实验得出的热力学模型的大型元数据库与（ii）来自高通量量子力学计算的热力学数据相结合来解决这个问题。这项工作的最终产品是一个开放分布的大规模热力学模型，可以在高维成分空间中查询，并将结构稳定性信息作为交互式三维横截面或与成分和温度相关的热力学性质返回。由于与已知的高熵合金的数量相比，可能的合金空间如此之大，因此通过该工具发现新型合金的潜力是巨大的，这可能会广泛影响能力受材料特性限制的众多工程应用。技术摘要该项目利用并集成了 PI 小组的两项最新开发成果：(i) 搜索引擎（热力学数据库或 TDBDB），它可以索引 科学文献中以标准化格式提供的所有可用的实验得出的热力学数据； (ii) 一套软件工具（合金理论自动化工具包或 ATAT），可简化从头开始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，即前者的准确性更高，而后者的高通量性质。主动机器学习和统计技术用于（i）目标是探索可能形成具有简单晶体结构的固溶体的有前景的成分区域，以及（ii）开发不需要从头开始的有效的非化学计量固体统计力学模型 输入，从而实现“高吞吐量”操作。虽然现有的计算高通量工作主要集中在绝对零时无缺陷化学计量化合物的特性，但该项目的目标是在所有温度下更广泛的材料，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。这需要通过利用已知趋势和数据挖掘趋势，建立 (i) 短程有序、(ii) 强非谐相和 (iii) 磁有序的高效统计力学模型，每种模型都需要比现有强力方法更少的从头开始输入。该项目期间设计的数据和工具预计将产生更广泛的影响：几乎所有工程材料都是非化学计量合金，其性能通过受控添加来调整 许多组件（高熵合金仅代表一个极端的例子）。因此，该资源可以通过增强现有的计算高通量工作（目前仅针对绝对零的有序化合物）来极大地促进材料发现和优化。所涵盖的广泛成分范围还使得应用范围广泛，从识别玻璃形成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 NSF 的法定使命，并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，被认为值得支持。

项目成果

A systematic analysis of phase stability in refractory high entropy alloys utilizing linear and non-linear cluster expansion models

利用线性和非线性簇扩展模型系统分析难熔高熵合金的相稳定性

• DOI: 10.1016/j.actamat.2021.117269
• 发表时间: 2021
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: Nataraj, Chiraag;Borda, Edgar Josué;van de Walle, Axel;Samanta, Amit
• 通讯作者: Samanta, Amit

A simple method for computing the formation free energies of metal oxides

• DOI: 10.1016/j.commatsci.2021.110692
• 发表时间: 2021-10
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Hantong Chen;Qi-Jun Hong;S. Ushakov;A. Navrotsky;A. Walle

Rapid screening of high-throughput ground state predictions

• DOI: 10.1016/j.calphad.2021.102306
• 发表时间: 2021-09
• 期刊: Calphad-computer Coupling of Phase Diagrams and Thermochemistry
• 影响因子: 2.4
• 作者: Sayan Samanta;A. Walle

Temperature-Dependent Configurational Entropy Calculations for Refractory High-Entropy Alloys

• DOI: 10.1007/s11669-021-00879-9
• 发表时间: 2021-04
• 期刊: Journal of Phase Equilibria and Diffusion
• 影响因子: 1.4
• 作者: Chiraag M Nataraj;A. van de Walle;A. Samanta

Computational Assessment of Novel Predicted Compounds in Ni-Re Alloy System

镍铼合金体系中新型预测化合物的计算评估

• DOI: 10.1007/s11669-021-00884-y
• 发表时间: 2021
• 期刊: Journal of Phase Equilibria and Diffusion
• 影响因子: 1.4
• 作者: Zhu, Siya;van de Walle, Axel
• 通讯作者: van de Walle, Axel