Magnetic Parameters from First-principles

第一原理的磁参数

基本信息

  • 批准号:
    1206920
  • 负责人:
  • 金额:
    $ 18.79万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2012
  • 资助国家:
    美国
  • 起止时间:
    2012-09-01 至 2016-08-31
  • 项目状态:
    已结题

项目摘要

TECHNICAL SUMMARYThis award support research aimed at the development and assessment of computational methods for the evaluation of magnetic parameters in confined and extended structures. The expected outcome is an improved understanding of magnetic phenomena in materials, which will be achieved by implementing and utilizing a fundamentally different way to extract magnetic parameters from first-principles computations based on density functional theory. The proposed methodology allows one to express magnetic parameters in terms of derivatives of the total electronic energy of a reference state with respect to a parameter that probes local or global rotations of the magnetization density, analogous to molecular response properties. In combination with analytic perturbation theory, magnetic parameters will be straightforwardly evaluated, providing a unique tool that will be used to explore the magnetic properties of new materials in an efficient and reliable way. This methodology will initially be developed for finite systems and local atomic Gaussian-type orbitals, and later extended to crystal systems and used to characterize the magnetic parameters of transition-metal complexes and Heusler alloys. This award also supports an educational and outreach program which will involve (i) the training of one Ph.D. student in the new Science of Advanced Materials program at Central Michigan University and (ii) PI's participation in a series of physics demonstrations for K-12 students. The PI will continue his activities on the integration of research with education at the undergraduate and graduate levels while promoting the involvement of women and minorities in science.NON-TECHNICAL SUMMARYUnderstanding magnetism at the molecular level is of both fundamental and technological importance. Many examples can be found that make use of molecular magnetism for practical applications, such as spintronics devices, magnetic memory alloys, and single-molecule magnets. This award support research aimed at the development and assessment of computational methods for the evaluation of magnetic parameters in small-scale as well as extended bulk structures. The expected outcome is an improved understanding of magnetic phenomena in materials, which will be achieved by implementing and utilizing a fundamentally different way to extract magnetic parameters from parameter-free, unbiased, and reliable computations. Such elucidation of the fundamental physical and chemical mechanisms that lead to microscopic magnetism is essential for the design and optimization of new materials and devices.This award also supports an educational and outreach program which will involve (i) the training of one Ph.D. student in the new Science of Advanced Materials program at Central Michigan University and (ii) PI's participation in a series of physics demonstrations for K-12 students. The PI will continue his activities on the integration of research with education at the undergraduate and graduate levels while promoting the involvement of women and minorities in science.
该奖项支持旨在开发和评估计算方法的研究,用于评估受限和扩展结构中的磁参数。 预期的结果是提高对材料中磁现象的理解,这将通过实施和利用一种从根本上不同的方式来实现,从基于密度泛函理论的第一原理计算中提取磁参数。所提出的方法允许一个来表示磁参数的参考状态的总电子能量的导数相对于一个参数,探测局部或全局旋转的磁化强度密度,类似于分子的响应特性。结合解析微扰理论,磁参数将被直接评估,提供一个独特的工具,将用于探索新材料的磁性在一个有效的和可靠的方式。这种方法最初将开发有限系统和本地原子高斯型轨道,后来扩展到晶体系统,并用于表征过渡金属络合物和Heusler合金的磁性参数。 该奖项还支持一项教育和推广计划,该计划将涉及(i)培训一名博士,在新的科学先进材料计划在中密歇根大学的学生和(ii)PI的参与一系列的物理演示K-12学生。PI将继续他的活动,在本科和研究生阶段的研究与教育的整合,同时促进妇女和少数民族在科学的参与。非技术总结在分子水平上理解磁性是基础和技术的重要性。可以找到许多将分子磁性用于实际应用的例子,例如自旋电子学器件、磁记忆合金和单分子磁体。 该奖项支持旨在开发和评估小规模以及扩展块体结构中磁参数评估的计算方法的研究。 预期的结果是提高对材料中磁现象的理解,这将通过实施和利用一种根本不同的方法来实现,从无参数,无偏和可靠的计算中提取磁参数。 这种导致微观磁性的基本物理和化学机制的阐明对于新材料和设备的设计和优化至关重要。该奖项还支持一项教育和推广计划,该计划将涉及(i)培养一名博士生,在新的科学先进材料计划在中密歇根大学的学生和(ii)PI的参与一系列的物理演示K-12学生。PI将继续开展将研究与本科生和研究生教育相结合的活动,同时促进妇女和少数民族参与科学。

项目成果

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Juan Peralta其他文献

Forecasting seasonal time series with computational intelligence: contribution of a combination of distinct methods
利用计算智能预测季节性时间序列:不同方法组合的贡献
  • DOI:
    10.2991/eusflat.2011.7
  • 发表时间:
    2011
  • 期刊:
  • 影响因子:
    0
  • 作者:
    M. Štěpnička;Juan Peralta;P. Cortez;L. Vavrickova;G. Gutiérrez
  • 通讯作者:
    G. Gutiérrez
Modelado Físico y Matemático del Sistema de Suspensión de un Cuarto de Vehículo
车辆悬架系统的机械和数学模型
  • DOI:
    10.18687/laccei2017.1.1.295
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jorge Luis Hurel Ezeta;E. Teran;Francisca Angelica Flores Nicolalde;Juan Peralta;Bolivar Flores
  • 通讯作者:
    Bolivar Flores
Design of Artificial Neural Networks Based on Genetic Algorithms to Forecast Time Series
基于遗传算法预测时间序列的人工神经网络设计
  • DOI:
    10.1007/978-3-540-74972-1_31
  • 发表时间:
    2008
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Juan Peralta;G. Gutiérrez;A. Sanchis
  • 通讯作者:
    A. Sanchis
Short-term electric load forecasting using computational intelligence methods
使用计算智能方法进行短期电力负荷预测

Juan Peralta的其他文献

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{{ truncateString('Juan Peralta', 18)}}的其他基金

NSF-BSF: Quantum Magnetization Dynamics in Open Molecular Junctions
NSF-BSF:开放分子结中的量子磁化动力学
  • 批准号:
    2318872
  • 财政年份:
    2023
  • 资助金额:
    $ 18.79万
  • 项目类别:
    Standard Grant
Magnetic Properties from First-Principles
第一原理的磁性
  • 批准号:
    0906617
  • 财政年份:
    2009
  • 资助金额:
    $ 18.79万
  • 项目类别:
    Standard Grant

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