DMREF/GOALI/Collaborative Research: High-Throughput Simulations and Experiments to Develop Metallic Glasses

DMREF/GOALI/协作研究:开发金属玻璃的高通量模拟和实验

基本信息

  • 批准号:
    1436151
  • 负责人:
  • 金额:
    $ 40万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2014
  • 资助国家:
    美国
  • 起止时间:
    2014-10-01 至 2018-09-30
  • 项目状态:
    已结题

项目摘要

Non-Technical Description: The increasing demand for higher performing materials across many fields requires the development of ever more complex materials. In this project a novel materials discovery methodology will be developed based on advances in combinatorial computational thermodynamics and experimental techniques. Bulk metallic glasses will be taken as example materials because of their technological potential - they can be considered high-strength metals that can be formed like plastics - and their suitability for the development of a general methodology. Many alloys - on the order of thousands - will be synthesized and characterized simultaneously. The ability of these alloys to be deformed, a property that correlates with the glass forming ability, will be measured experimentally. The crystalline phases competing with the glassy phase will also be characterized. These crystalline phases will be compared with atomic modeling results considering the energy of many possible crystalline phases. Since direct modeling of the glass forming ability is not possible from first principles, correlations will be established between the experimental glass forming ability and the competing crystalline phases from atomic modeling. The development of such a methodology and correlation through this research will accelerate the pace of discovery and deployment of advanced materials. Specifically for bulk metallic glasses, the potential development of technologically relevant alloys, particularly those that are based on Cu or Al can be expected to have a lasting impact on society.Technical Description: This objective will be realized through an integrated approach of combinatorial ab-initio simulations, combinatorial synthesis of sputtered composition spreads, and high-throughput characterization methods. To massively parallel synthesize complex alloy systems comprising ~1,000 alloys, this research uses combinatorial magnetron sputtering. Compositional libraries will be characterized using specific high-throughput methods for measuring liquidus temperature, formability, thermal, and structural properties. Within such an approach a vast amount of experimental and computational data will be generated, which will be data-mined to identify correlations. Rather than trying to directly simulate glass formation, the strategy will be to integrate experiments and computations to understand which structural and energetic aspects of the liquid and competing crystalline state best correlate with glass forming ability. Identifying correlations is a key aspect of the research and these correlations will be used to search for new glass forming compositions through combined computational and experimental means.
非技术描述:许多领域对性能更高的材料的需求不断增加,这就要求开发更复杂的材料。在这个项目中,将基于组合计算热力学和实验技术的进展来开发一种新的材料发现方法。块状金属玻璃将被作为材料的范例,因为它们的技术潜力--它们可以被认为是可以像塑料一样形成的高强度金属--以及它们适合开发一般方法。许多合金--大约数千种--将被同时合成和表征。这些合金的变形能力将通过实验进行测量,这是一种与玻璃形成能力相关的特性。与玻璃相竞争的晶相也将被表征。这些晶相将与考虑许多可能晶相的能量的原子模拟结果进行比较。由于玻璃形成能力的直接模拟不可能从第一性原理出发,因此实验玻璃形成能力与原子模拟中的竞争晶相之间将建立关联。通过这项研究发展这样一种方法和相互关系,将加快发现和部署先进材料的步伐。具体来说,对于大块金属玻璃,技术上相关合金的潜在发展,特别是基于铜或铝的合金的潜在发展,有望对社会产生持久的影响。技术描述:这一目标将通过组合从头计算模拟、扩散成分扩散的组合合成和高通量表征方法的综合方法来实现。为了大规模并行合成由约1,000种合金组成的复杂合金系统,本研究使用组合磁控溅射。将使用测量液相线温度、可成形性、热学和结构性质的特定高通量方法来表征组成文库。在这种方法中,将产生大量的实验和计算数据,这些数据将被数据挖掘以确定相关性。与其试图直接模拟玻璃的形成,该策略将是将实验和计算结合起来,以了解液体和竞争晶态的哪些结构和能量方面与玻璃形成能力最相关。确定相关性是研究的一个关键方面,这些相关性将被用来通过计算和实验相结合的手段来寻找新的玻璃形成成分。

项目成果

期刊论文数量(0)
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Stefano Curtarolo其他文献

AFLOW-XtalFinder: a reliable choice to identify crystalline prototypes
AFLOW-XtalFinder:识别晶体原型的可靠选择
  • DOI:
    10.1038/s41524-020-00483-4
  • 发表时间:
    2021-02-11
  • 期刊:
  • 影响因子:
    11.900
  • 作者:
    David Hicks;Cormac Toher;Denise C. Ford;Frisco Rose;Carlo De Santo;Ohad Levy;Michael J. Mehl;Stefano Curtarolo
  • 通讯作者:
    Stefano Curtarolo
Machine learning for alloys
用于合金的机器学习
  • DOI:
    10.1038/s41578-021-00340-w
  • 发表时间:
    2021-07-20
  • 期刊:
  • 影响因子:
    86.200
  • 作者:
    Gus L. W. Hart;Tim Mueller;Cormac Toher;Stefano Curtarolo
  • 通讯作者:
    Stefano Curtarolo
Synthesis of Nanowire Heterojunctions for Advanced Nanoelectronic Devices
用于先进纳米电子器件的纳米线异质结的合成
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Andrea C. Ferrari;Stefano Curtarolo
  • 通讯作者:
    Stefano Curtarolo
Intermediate stage densification kinetics of high-entropy ceramics during spark plasma sintering
  • DOI:
    10.1016/j.jeurceramsoc.2024.117136
  • 发表时间:
    2025-05-01
  • 期刊:
  • 影响因子:
  • 作者:
    Steven M. Smith;William G. Fahrenholtz;Greg E. Hilmas;Stefano Curtarolo
  • 通讯作者:
    Stefano Curtarolo
Viscous State Effect on the Activity of Fe
粘性状态对Fe活性的影响
  • DOI:
  • 发表时间:
    2010
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Andrea C. Ferrari;Stefano Curtarolo
  • 通讯作者:
    Stefano Curtarolo

Stefano Curtarolo的其他文献

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{{ truncateString('Stefano Curtarolo', 18)}}的其他基金

CAREER: Genetic Approaches to Quantum Mechanics Predictions of Materials Structures
职业:材料结构量子力学预测的遗传方法
  • 批准号:
    0639822
  • 财政年份:
    2007
  • 资助金额:
    $ 40万
  • 项目类别:
    Continuing Grant

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