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

这个NSERC发现基金项目涉及从头算第一原理密度泛函理论的材料性质的计算。晶体固体的许多重要性质，包括金属和绝缘体，都是通过晶体结构中的缺陷来调节的，即使它们在非常稀薄的浓度下也是如此。因此，对材料性能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，因为缺陷紧密地结合了核心中断裂的键的复杂化学，环形区域的离散原子性质，以及连续统弹性场的长期缓慢衰减。已经提出了一些多尺度方法来解决这个问题，但这些方法需要渐近假设或临时补丁，在实施过程中需要特定于案例的专业知识。这些不仅与从头算哲学背道而驰，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，受控的数值近似使得能够研究实际浓度下的检测。 这个项目有三个目标。第一个是建立MacroDFT作为执行大规模从头计算模拟的标准工具之一。我们试图研究物质的显着性质是如何从原子和电子成分的复杂关联中出现的，以及我们如何通过仅使用DFT的多尺度建模和模拟来控制它们。 该项目的第二个目标是系统地研究镁及其合金中的缺陷。镁合金是金属中强度与重量比最高的合金之一，镁含量丰富。这使得它对各种应用都很有吸引力，但由于其延展性有限，这一点失败了。因此，我们试图通过提高镁合金的延展性和成形性来改善其力学性能。据介绍，新型镁合金有望在国家未来的交通、能源和环境方面发挥关键作用。加拿大政府制定了一个目标，通过使用新的镁合金减轻汽车重量，到2050年将温室气体(GHG)排放总量减少45%-65%。因此，本研究项目的主要发现将对加拿大的研究和工业应用产生很高的影响。 这项提议的第三个目标是使用仅基于从头计算的中尺度模型对基于石墨烯的晶体管进行计算工程。我们试图通过缺陷在石墨烯及其变种制成的纳米器件中设计带隙。这一点很重要，因为它可以应用于新一代超小型和超高速电子应用。最后，我们注意到，虽然我们目前建议在该项目中重点关注镁和石墨烯，但DFT适用于所有材料，因此MacroDFT适用于所有晶体固体。

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.