Computational study of the atomistic kinetics and structure of complex materials

复杂材料原子动力学和结构的计算研究

• 批准号: RGPIN-2014-06563
• 负责人: Mousseau, Normand
• 金额: $ 3.93万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=633590
• 关键词: Computational; study; atomistic; kinetics; structure

This research programme focuses on the development and the application of advanced computational approaches for the study of structural and dynamical properties of complex materials — alloys, disordered systems, defective materials, and proteins. While they appear very diverse, all these systems share a similar problematic : their configurational space is very rich and difficult to explore using standard methods such as molecular dynamics and understanding the atomistic details of their evolution over long time scales (i.e., beyond a microsecond or so) is crucial to the proper characterization of their properties. Achieving these goals on computer, even with access to state-of-the-art computing facilities, remains a challenge and requires new and, often, specific approaches. Over the years, I have developed many algorithms to bridge the gap between computer and experimental time scales that have allowed my group to remain at the forefront of accelerated methods and their application to complex materials. Over the next five years, I propose to concentrate a good deal of my efforts to apply and further optimize the kinetic Activation-Relaxation Technique (k-ART), the only kinetic Monte-Carlo algorithm able today to simulate, without any restriction, the kinetics of glasses, interfaces and alloys over time scales of a second or more. With such a method, I have access to vast families of problems that could not be addressed numerically until now such as the evolution of low-temperature glass and disordered systems, the formation of nanostructures in complex environment (e.g., silicon nanocrystals in silica) and the diffusion of defects and impurities in materials (e.g., self-defects, carbon and helium atoms in iron). I will also continue development on the biophysics front, focusing in large part on further characterizing the aggregation process of amyloid proteins in order to identify the neurotoxic oligomer species. Here, the challenge is also one of time scale and sampling and we have shown that simplified potential, such as the coarse grained OPEP forcefield, could help us push these limits further. However, coarse-grained forcefields also come with limitations and part of this programme will focus on the development of a new all-atom implicit solvent potential that will sit between OPEP and the more realistic descriptions such as CHARMM and AMBER, giving us more precision and remain as light as possible. With this forcefield, we will continue our study of amyloid proteins using a multiple approaches — large assembly of short peptides, and small assembly of full-length with and without membranes — focusing on the detailed comparison between various sequences to extract qualitative results less likely to be finely dependent on the forcefield specifics. Beyond these applications, we will make significant efforts to distribute the various unique tools that we are developing as those are already attracting a lot interest around the world. Maintaining and distributing these codes is time and resource consuming but it is an essential part of method development today. Overall, this research programme proposes a good equilibrium between methodology and physics to ensure that my group remains at the forefront of methods development and continues to deepen our understanding of complex materials.

该研究计划的重点是开发和应用先进的计算方法来研究复杂材料的结构和动力学特性-合金，无序系统，缺陷材料和蛋白质。虽然它们看起来非常多样化，但所有这些系统都有一个类似的问题：它们的构型空间非常丰富，难以使用标准方法进行探索，例如分子动力学和理解它们在长时间尺度上进化的原子细节（即，超过微秒左右）对于它们的性质的适当表征是至关重要的。利用计算机实现这些目标，即使有最先进的计算设施，仍然是一项挑战，需要新的、往往是具体的办法。多年来，我开发了许多算法来弥合计算机和实验时间尺度之间的差距，使我的团队能够保持在加速方法及其应用于复杂材料的最前沿。在接下来的五年里，我打算集中大量的精力来应用和进一步优化动力学激活-弛豫技术（k-ART），这是当今唯一能够无限制地模拟玻璃、界面和合金在一秒或更长时间尺度上的动力学的蒙特-卡罗算法。有了这样的方法，我可以接触到大量的问题，这些问题到目前为止还不能用数字来解决，例如低温玻璃和无序系统的演变，复杂环境中纳米结构的形成（例如，二氧化硅中的硅纳米晶体）以及材料中缺陷和杂质的扩散（例如，自缺陷，铁中的碳和氦原子）。我也将继续在生物物理学方面的发展，主要集中在进一步表征淀粉样蛋白的聚集过程，以确定神经毒性寡聚体种类。在这里，挑战也是时间尺度和采样的挑战之一，我们已经表明，简化的潜力，如粗粒度的OPEP力场，可以帮助我们进一步推动这些限制。然而，粗粒力场也有局限性，该计划的一部分将专注于开发一种新的全原子隐式溶剂势，该势将位于OPEP和更现实的描述（如CHARMM和AMBER）之间，为我们提供更高的精度，并尽可能保持轻便。有了这个力场，我们将继续使用多种方法研究淀粉样蛋白-短肽的大型组装，以及有膜和无膜的全长的小型组装-专注于各种序列之间的详细比较，以提取定性结果，不太可能精细地依赖于力场细节。除了这些应用程序之外，我们还将做出重大努力，分发我们正在开发的各种独特工具，因为这些工具已经吸引了世界各地的兴趣。维护和分发这些代码是耗时耗力的，但它是当今方法开发的重要组成部分。总的来说，这个研究计划提出了方法和物理之间的良好平衡，以确保我的团队仍然处于方法开发的最前沿，并继续加深我们对复杂材料的理解。