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.

非技术总结材料科学领域已通过发现一类称为“高熵”合金的合金来吸引，这些合金的特征是通过大量不同元素（相当数量）结合了简单的晶体结构意外稳定。此类合金的发现和设计要求对在很高的尺寸组成空间中控制稳定性的热力学因素有详细的了解，并具有许多竞争晶体结构。由于所需的输入数据数量庞大，并且输入模型的参数，这种复杂性测试了当前热力学建模功能的极限。该项目通过使用正式的统计和主动的机器学习技术组合（i）（i）（i）实验衍生的热力学模型的大规模元数据库与（ii）来自高通量量子力学计算的热力学数据。这项工作的最终产品是一个公开分布的大规模的热力学模型，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跨区域，或作为组成和温度依赖性的热力学特性。 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所有可用的实验衍生的热力学数据在科学文献中以标准化的格式获得； （ii）一套软件工具（合金理论自动化工具包或ATAT），该软件工具从AB INLIO算数据中简化了热力学数据库的生成。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，前者的较高准确性以及后者的高通量性质。主动的机器学习和统计技术用于（i）针对探索有前途的固定解决方案可能会形成简单的固体结构和统计学模型（II）的良好构成区域的探索。输入，从而实现“高通量”操作。尽管现有的计算高通量工作主要集中于绝对零的无缺陷化学计量化合物的性能，但该项目在所有温度下都针对更广泛的材料，包括可能的短距离订单，并订购了可能点缺陷的合金。这需要（i）（i）短期顺序的有效统计机械模型，（ii）强烈的非谐波阶段和（iii）磁性排序，每种磁性顺序比现有的蛮力方法所需的每个趋势较少，而不是现有的蛮力方法，通过利用已知的趋势和数据迁移趋势。该项目在该项目中均具有更广泛的影响。许多组件（具有高熵合金仅代表极端示例）。因此，通过扩大现有的计算高通量工作，目前仅针对绝对零以有序化合物为目标，该资源可以极大地促进材料发现和优化。涵盖的广泛构图还可以实现，从识别形成玻璃的金属合金到确定可能的外部外部核心组成和结构的应用。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。

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