From quantum mechanics to mesoscale science: Large scale ab-initio simulations of materials

从量子力学到介观科学：材料的大规模从头算模拟

• 批准号: RGPIN-2016-06114
• 负责人: PongadelaTorre, Mauricio
• 金额: $ 1.68万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710122
• 关键词: quantum; mechanics; mesoscale; science; Large

This NSERC Discovery grant project concerns the computation of materials properties from ab-initio first principles density functional theory. Many important properties of crystalline solids including metals and insulators are mediated through defects in the crystal structure even when they occur in very dilute concentrations. Thus, a predictive understanding of material properties requires an understanding of the defects. Unfortunately this is challenging, as defects intimately couple the complex chemistry of the broken bonds in the core, the discrete atomistic nature of the annular region, and the long-range slow decay of continuum elastic fields. A number of multiscale approaches have been proposed to address this, but these require asymptotic assumptions or ad hoc patches that require case-specific expertise in their implementation. These are not only counter to the ab-initio philosophy, but also restrict their transferability and their predictive ability. In contrast, the proposed project pursues an approach where DFT is the sole input and controlled numerical approximations enable the study of detects at realistic concentrations. This project has three goals. The first is to establish MacroDFT as one of the standard tools for performing large scale ab-initio simulations. We seek to investigate how remarkable properties of matter emerge from complex correlations of the atomic and electronic constituents and how we can control them by multiscale-modeling and simulations using solely DFT. The second goal of this project is a systematic study of defects in Magnesium and its alloys. Mg-alloys have some of the highest strength-to-weight ratios amongst metals, and Mg is abundant. This makes it attractive for a variety of applications, but this has failed due to its limited ductility. Therefore, we seek to improve the mechanical properties of new Mg-based alloys by increasing its ductibility and formability. New Mg-alloys are anticipated to play a critical role in the nation's transportation energy and environmental future according. The Government of Canada has set a target of reducing total greenhouse gas (GHG) emissions by 45-65 percent by 2050 by reducing cars weight using new Mg-alloys. Therefore, the main finding of this research projects will have a high impact in Canada's research and industry applications. The third goal of this proposal is the computational engineering of graphene-based transistors using mesoscale models based solely on ab-initio calculations. We seek to design band gaps in nanodevices made of graphene and its variants through defects. This is important since it can be applied to the new generation of ultra-small and ultra-fast electronic applications. We finally note that while we currently propose to focus on Mg and graphene in this project, DFT is applicable to all materials and thus MacroDFT is applicable to all crystalline solids.

这个NSERC发现资助项目涉及从从头算第一原理密度泛函理论计算材料特性。包括金属和绝缘体在内的结晶固体的许多重要性质是通过晶体结构中的缺陷介导的，即使它们在非常稀的浓度下也如此。因此，对材料特性的预测性理解需要对缺陷的理解。不幸的是，这是具有挑战性的，因为缺陷与核心中断裂键的复杂化学，环形区域的离散原子性质以及连续介质弹性场的长期缓慢衰减密切相关。已经提出了许多多尺度方法来解决这个问题，但这些方法需要渐进假设或特殊补丁，需要具体案例的专业知识来实施。这不仅与从头算哲学相悖，而且还限制了它们的可转移性和预测能力。相比之下，所提议的项目采用的方法是，DFT是唯一的输入，控制数值近似值可以研究实际浓度下的检测。