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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616104
• 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.

该NSERC DISCOVER GANT项目涉及Ab-Initio第一原理密度功能理论的材料属性的计算。包括金属和绝缘子在内的晶体固体的许多重要特性即使晶体结构的缺陷也以非常稀释的浓度出现。因此，对材料特性的预测理解需要了解缺陷。不幸的是，这是具有挑战性的，因为缺陷紧密融合了核心中断裂键的复杂化学反应，环形区域的离散原子性质以及连续弹性场的远距离慢衰变。已经提出了许多多尺度方法来解决此问题，但是这些方法需要在实施中需要特定于案例专业知识的渐近假设或临时补丁。这些不仅与Ab-Initio哲学相反，而且还限制了它们的可转移性和预测能力。相比之下，拟议的项目采用了一种方法，其中DFT是唯一的输入，并且受控的数值近似能够研究逼真的浓度检测。 该项目有三个目标。首先是建立大型AB-Initio模拟的标准工具之一。我们试图研究来自原子和电子成分的复杂相关性的显着特性，以及如何仅使用DFT进行多尺度模型和仿真来控制它们。 该项目的第二个目标是对镁及其合金缺陷的系统研究。 MG合金在金属中具有最高的强度与重量比，并且MG丰富。这使其对各种应用程序有吸引力，但由于其有限的延展性，因此失败了。因此，我们试图通过提高其可连接性和形成性来改善新的基于MG的合金的机械性能。预计新的MG合金将在美国运输能源和环境未来中发挥关键作用。加拿大政府通过使用新的MG合金减轻汽车的重量，将总温室气（GHG）排放量减少45-65％的目标。因此，该研究项目的主要发现将对加拿大的研究和行业应用产生重大影响。 该提案的第三个目标是仅基于AB-Initio计算的中尺度模型的基于石墨烯的晶体管的计算工程。我们寻求通过缺陷来设计石墨烯及其变体制成的纳米式缝隙。这很重要，因为它可以应用于新一代的超小型和超快速电子应用。我们最终注意到，尽管我们目前建议在该项目中专注于MG和石墨烯，但DFT适用于所有材料，因此Macrodft适用于所有晶体固体。