Combinatorial exploration of stability regions of high component single-phase solid solutions with near-equiatomic composition

近等原子组成的高组分单相固溶体稳定区域的组合探索

• 批准号: 1609391
• 负责人: Jan Schroers
• 金额: $ 44.81万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609391&HistoricalAwards=false
• 关键词: Combinatorial; exploration; stability; regions; component

Non-technical AbstractTechnological advances require higher performing materials, which are often realized by increasing the material's complexity. A new class of materials, called "High entropy alloys" consist of at least 5 components and exemplify this trend. This is in contrast to traditional alloys that are typically composed of a principal element with other constituents in only small fractions. Identifying these materials out of the potential vast compositional space has been challenging. Addressing this vast composition space with theories or correlations with a priori known information has been very limited. In this work, a new combinatorial strategy will be employed.This approach will allow considering ~1,000 alloys simultaneously, hence generating an unprecedented quantity of data. All data will be openly shared through an online data repository. The rapid data generation and open sharing of such data will allow the scientific community to develop improved theories for understanding and eventually developing new materials. More generally, proposed strategy offers a novel approach to materials research where very large amounts of data that are consistently measured are accessible to the entire scientific community. The students involved in this project will be trained in this open approach to materials science, which will contribute greatly to the training of the next generation of scientists at the cutting edge of their field. Technical AbstractHigh Entropy Alloys (HEAs) form single-phase solid solutions at equiatomic or near-equiatomic composition. This design principle provides a new perspective on alloy discovery, by turning our focus away from the corner of the phase diagram towards the center. A broad range of promising mechanical properties drives technological excitement about HEAs. However, identifying HEAs out of the potential vast compositional space has been challenging. Addressing this vast composition space with first principle theories or correlations with a priori known information has been very limited. Due to the vast potential compositional space and the weak predictability, a combinatorial strategy will be employed. This approach will allow considering ~1,000 alloys simultaneously, hence generating an unprecedented quantity of data. All data will be openly shared through an online data repository. The rapid data generation and open sharing of such data will allow the scientific community to develop improved theories for understanding formation motifs and eventually predicting HEAs. More generally, the proposed strategy offers a novel approach to materials research where very large amounts of data that are consistently measured are accessible to the entire scientific community. The students involved in this project will be trained in this open approach to materials science, which will contribute greatly to the training of the next generation of scientist at the cutting edge of their field. As a high-throughput materials synthesis method, combinatorial sputtering is used to create large libraries of ~1000 alloys. Rapid screening chemical analysis and structural analysis are used to characterize the alloys within the library. Altogether, over 100,000 alloys will be fabricated and phase boundaries identified within this project. These data will be curated and shared with the community through the Materials Atlas Project online repository. Curated data sets will indicate the compositional space of the single phase solid solution, these of multiple phase regions, and their boundaries. Mining of the data will be employed to identify correlations between properties of the alloy and alloy components. Based on such correlations, current theories can be tested and new theories developed. For example, quantifying if and when enthalpic motifs can be overwritten by entropic benefits, e.g., suppression of phase separation.

非技术抽象技术的进步需要更高的性能材料，这通常通过增加材料的复杂性来实现。 一种称为“高熵合金”的新型材料至少由5个组成部分组成，并举例说明了这一趋势。这与通常由仅小部分组成的主要元素与其他成分组成的传统合金相反。 从潜在的巨大组成空间中识别出这些材料一直具有挑战性。通过与先验已知信息相关的理论或相关性解决这个庞大的构图空间非常有限。 在这项工作中，将采用一种新的组合策略。这种方法将同时考虑约1,000个合金，从而产生前所未有的数据。所有数据将通过在线数据存储库公开共享。此类数据的快速数据生成和公开共享将使科学界能够开发出改进的理论，以理解并最终开发新材料。更普遍的是，提议的策略为材料研究提供了一种新颖的方法，在该方法中，整个科学界都可以访问大量的数据。 参与该项目的学生将接受这种开放的材料科学方法的培训，这将为下一代科学家在其领域的尖端培训。技术抽吸熵合金（HEAS）形成均衡或近程组成的单相固溶物。该设计原理通过将我们的焦点从相图的角度转移到中心，从而为合金发现提供了新的视角。广泛的有希望的机械性能引起了人们对HEAS的技术兴奋。但是，从潜在的巨大组成空间中识别出来一直具有挑战性。通过首先原理理论解决这个庞大的构图空间或与先验已知信息的相关性非常有限。由于潜在的组成空间和弱可预测性，将采用组合策略。这种方法将同时考虑约1,000个合金，从而产生前所未有的数据。所有数据将通过在线数据存储库公开共享。此类数据的快速数据生成和公开共享将使科学界能够发展出改进的理论，以理解形成图案并最终预测HEAS。 更普遍地，拟议的策略为材料研究提供了一种新颖的方法，在该方法中，整个科学界都可以访问大量的数据。 参与该项目的学生将接受这种开放的材料科学方法的培训，这将为下一代科学家在其领域的最前沿做出巨大贡献。作为一种高通量材料合成方法，组合溅射用于创建约1000个合金的大型库。快速筛选化学分析和结构分析用于表征库中的合金。总共将制造超过100,000个合金，并在该项目中确定相位边界。这些数据将通过材料Atlas Project在线存储库策划并与社区共享。策划的数据集将指示单相固体解决方案的组成空间，这些解决方案的多个相区域及其边界。将使用数据挖掘来确定合金和合金组件的性能之间的相关性。基于此类相关性，可以测试当前理论并开发新的理论。例如，量化是否以及何时可以被熵益处（例如抑制相分离）覆盖。