Elucidating the Thermodynamic and Kinetic Properties of High Temperature Materials with First-Principles Statistical Mechanics

用第一性原理统计力学阐明高温材料的热力学和动力学性质

• 批准号: 1410242
• 负责人: Anton Van der Ven
• 金额: $ 30万
• 依托单位: University of California-Santa Barbara
• 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: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1410242&HistoricalAwards=false
• 关键词: Elucidating; Thermodynamic; Kinetic; Properties; Temperature

NON-TECHNICAL SUMMARY Remarkable levels of sophistication have been reached in linking properties of a given material to its microstructure, crystal structure and electronic structure. A substantially bigger challenge, though, is predicting the dynamic evolution of a material taken out of equilibrium and determining what external stimuli must be imposed to shepherd the material into a desired end state. The desirable properties from a particular chemistry are usually manifested in metastable crystal structures and microstructures rather than in the true equilibrium state of that chemistry. In many applications it is necessary to know how a material in a particular state will evolve over time either because it is metastable or unstable, such as in high temperature applications, or due to changing boundary conditions, as in electrochemical energy storage applications.This award supports computational research and education to develop highly automated statistical mechanical software tools that will be used to predict materials properties and greatly enhance the ability to design materials for high temperature and non-equilibrium applications from first principles. Areas where such tools will prove invaluable include the design of new (i) structural materials for aerospace applications and large-scale power generation plants, (ii) electrode and electrolyte materials for electrochemical energy storage, (iii) materials for thermoelectric applications and (iv) materials for shape memory applications. The project will also involve the education and training of graduate students in computational materials science, a field that is increasingly recognized as invaluable in the design and rapid implementation of new materials.TECHNICAL SUMMARY This award supports research and educational activities aimed at extending the existing thermodynamic and kinetic foundations that underpin phenomenological descriptions of non-equilibrium processes in the solid state. This will be realized by two activities: (i) the development of statistical mechanical computational tools to automate the calculation of a wide variety of thermodynamic and kinetic properties that are essential in the description of materials evolving out of equilibrium and (ii) the development and application of new statistical mechanical theoretical methods to enable the prediction of high temperature properties of multi-component crystalline solids. A deep understanding of many high temperature materials and non-equilibrium processes is hampered by a lack of not only quantitative thermodynamic, kinetic and mechanical data, but also a lack of knowledge about qualitative trends in this data. Furthermore, a large class of technologically important high temperature materials cannot be adequately described and understood with current statistical mechanical methods. This research activity will result in highly automated computational statistical mechanical tools to predict free energies and transport coefficients as a function of concentration. Such tools will provide new knowledge about the dependence of a wide variety of materials properties on chemistry and crystal structure. The theoretical focus on high temperature phases will generate fundamental new insights about the vibrational stabilization mechanisms, atomic hop mechanisms and mechanical properties of a large and important class of poorly understood materials used in high temperature applications. The project will also involve the education and training of graduate students in computational materials science, a field that is increasingly recognized as invaluable in the design and rapid implementation of new materials.

在将特定材料的性质与其微观结构、晶体结构和电子结构联系起来方面，已经达到了相当复杂的水平。然而，一个更大的挑战是，如何预测一种失去平衡的材料的动态演变，并确定必须施加什么样的外部刺激才能使材料进入理想的最终状态。特定化学的理想性质通常表现在亚稳态晶体结构和微观结构中，而不是表现在该化学的真正平衡状态中。在许多应用中，有必要知道材料在特定状态下如何随着时间的推移而演变，因为它是亚稳态或不稳定的，例如在高温应用中，或者由于边界条件的变化，例如在电化学储能应用中。该奖项支持计算研究和教育，以开发高度自动化的统计机械软件工具，用于预测材料特性，并大大提高从第一原理设计高温和非平衡应用材料的能力。这些工具将被证明是无价的领域包括设计新的(i)用于航空航天应用和大型发电厂的结构材料，（ii）用于电化学储能的电极和电解质材料，（iii）用于热电应用的材料和（iv）用于形状记忆的材料。该项目还将涉及计算材料科学研究生的教育和培训，这一领域在新材料的设计和快速实施方面越来越被认为是无价的。该奖项支持旨在扩展现有热力学和动力学基础的研究和教育活动，这些基础是固态非平衡过程的现象学描述的基础。这将通过两项活动来实现：(i)开发统计力学计算工具，以自动计算各种热力学和动力学性质，这些性质在描述脱离平衡状态的材料中是必不可少的；（ii）开发和应用新的统计力学理论方法，以预测多组分结晶固体的高温性质。不仅缺乏定量的热力学、动力学和力学数据，而且缺乏对这些数据的定性趋势的认识，阻碍了对许多高温材料和非平衡过程的深入理解。此外，一大类技术上重要的高温材料不能用目前的统计力学方法充分描述和理解。这项研究活动将导致高度自动化的计算统计机械工具来预测自由能和输运系数作为浓度的函数。这些工具将提供关于各种材料性质对化学和晶体结构的依赖性的新知识。对高温相的理论关注将产生关于振动稳定机制、原子跳跃机制和机械性能的基本新见解，这是一大类在高温应用中鲜为人知的重要材料。该项目还将涉及计算材料科学研究生的教育和培训，这一领域在新材料的设计和快速实施方面越来越被认为是无价的。