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DMREF: Discovery and Design of Magnetic Alloys by Simulation and Experiment

DMREF: Discovery and Design of Magnetic Alloys by Simulation and Experiment
DMREF:通过模拟和实验发现和设计磁性合金
批准号:
1437106
负责人:
Elif Ertekin
金额:
$67.38万
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
项目状态:
已结题
起止时间:
2014-09-01 至 2018-08-31

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中文摘要
翻译
磁致冷利用磁场改变材料的温度,为涉及发电、加热和制冷的应用提供了一种截然不同的能源解决方案。磁制冷是一种潜在的环保、节能的技术,其性能优于传统的气体压缩制冷。自从发现了可以在正常工作条件下将磁性转化为冷却的材料以来,已经有15年多的时间了,但如何优化这些材料的制造和性能仍然是个未知数。DMREF项目致力于识别和优化磁性合金的化学成分,并了解控制这一重要行为的基本科学性质。将使用计算和实验设计相结合的方法来快速确定最佳候选系统。先进的计算方法将被用来预测和设计表现出最强效应的合金;然后将合成发现的最有希望的成分并在实验室进行测试。预期的结果是:(I)确定了几种用于制冷和制冷的高性能磁性合金,以及(Ii)评估了这一极具前景的材料类别所能达到的性能。该计划专注于优化最有前景的材料类别之一的性能:超磁性镍-锰-锡和镍-锰-In-(Co)形状记忆合金。由于两个设计自由度、磁性和晶体结构之间的独特耦合,这些Heusler类型的合金有可能极大地改变我们的冷却和制冷方法。这项工作带来了一种计算/实验相结合的方法来设计和优化,通过量子力学模拟方法和独特的实验能力来测量在大磁场下的性能。将使用量子力学模型和唯象描述相结合的方法来计算一组目标性能指标,如弹性常数、声子模、自旋波刚度和交换相互作用能。对于最有希望的候选者,将通过对单晶样品的定向实验来验证计算模型,方法是研究磁场诱导的从马氏体相到奥氏体相的转变,在选定的成分中产生显著的磁化强度变化。最终,该计划将对变磁形状记忆合金Ni-Mn-Sn和Ni-Mn-In-(Co)的一系列候选材料进行详尽的评估。
英文摘要
Magnetic cooling, in which a magnetic field is used to change the temperature of a material, offers a profoundly different energy solution to applications involving power generation, heating, and refrigeration. Magnetic cooling is potentially an environmentally friendly, energy-efficient technology capable of outperforming conventional gas-compression refrigeration. It has been over fifteen years since the discovery of materials that can convert magnetism for cooling in normal operating conditions, but much is still unknown about how to optimize the manufacture and performance of these materials. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project seeks to identify and optimize the chemical compositions of magnetic alloys, and understand the fundamental scientific properties that control this important behavior. A combined computational and experimental design approach will be used to identify the optimal candidate systems rapidly. Advanced computational methods will be used to predict and design alloys exhibiting the strongest effect; the most promising compositions discovered will then be synthesized and tested in the laboratory. The expected results are (i) the identification of several high-performance magnetic alloys for cooling and refrigeration, and (ii) an assessment of the performance attainable in this highly promising class of materials.This program focuses on optimizing performance within one of the most promising class of materials: the metamagnetic Ni-Mn-Sn and Ni-Mn-In-(Co) shape memory alloys. Due to a unique coupling between two design degrees of freedom, magnetism and crystal structure, these Heusler-type alloys have the potential to dramatically change our approach to cooling and refrigeration. This effort brings a combined computational/experimental methodology to design and optimization, via quantum mechanical simulation methods and unique experimental capabilities to measure performance under large magnetic fields. A set of target performance metrics, elastic constants, phonon modes, spin wave stiffnesses, and exchange interaction energies - will be calculated using quantum mechanical models coupled with phenomenological descriptions. For the most promising candidates, targeted experiments on single crystal samples will be used to validate the computational models by studying the magnetic field induced transition from the martensite phase to the austenite phase in selected compositions producing a significant change in magnetization. Ultimately this program will provide an exhaustive assessment across a spectrum of candidates in the metamagnetic shape memory alloys Ni-Mn-Sn and Ni-Mn-In-(Co).
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Travel Support for Workshop on Best Practices in Modeling Across Scales from Materials Discovery to Manufacturing; Arlington, Virginia; Summer 2023
DMREF: Collaborative Research: Accelerating Thermoelectric Materials Discovery via Dopability Predictions
Network for Computational Nanotechnology - Hierarchical nanoMFG Node
CAREER: Designing Functionality Into Two-Dimensional Materials Through Defects, Topology, and Disorder
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