Computational methods for the exploration of potential energy surfaces with applications to nanoalloy materials.

我们开发了高性能的计算方法来发掘潜力 能量面(PES，描述能量的数学函数 当分子的几何构型改变时，分子发生变化)，从而提供 分子结构和化学反应研究的基础。成功实施 将创建一套独特的计算工具用于研究 分子结构、过渡态和反应路径， 以及对具有数百个原子的系统的模拟。这些工具 对加拿大和其他地方的研究人员来说都是非常重要的。 我们的其中一种方法模仿了DFT，并给出了计算加速 4到6个数量级。它将允许对纳米合金的研究 在催化和数据存储设备中具有潜在的应用。 从我们最近发现的双金属笼子中寻找线索， 我们将寻找新的、更大的全金属笼子。 PES的最小值是分子的平衡几何；鞍点是 过渡态；连接鞍点和极小点的最陡下降线是反应路径； 最陡峭下降线附近的低能量区域是重要的 以获取反应机理的详细模型。我们将继续努力 精通量子化学和密度泛函理论 (DFT)，结合我们自己的计算机代码，来研究PES。 学生将在结合方法开发的研究项目中接受培训 和应用程序，并将获得各种技能：Unix方面的专业知识 以及量子化学软件、编程和外壳脚本、数学 模特儿和一般的科学素养。这些项目的设计目标是 各层次满足本科生的需求和能力， 研究生和博士后。 我们将从三个方面研究有趣的纳米合金 性质及其在催化和信息技术中的潜在用途。 (1)全局优化。进化计算方法，如遗传 将开发算法(GA)来确定几何结构 纳米合金团簇。 (2)反应机理。我们使用了粒子群算法(PSO) 在没有人类监督的情况下，发现几何变换的序列 在化学反应过程中发生在分子中的物质(‘反应机制’)。我们会 创造一种新的方法，基于化学直觉和优化技术，寻找先验 (不需要任何DFT计算)反应机理。我们会发现，在一个 根据具体情况，此机制是否与 最低能量反应路径(真正的机理)，并将相应地对化学反应进行分类。 (3)原子模拟。我们开发了一种新的拟合方法来创建 完全自动化的高维PES，不需要 用户输入一个功能表格，它几乎适用于任何 化学成分。此PES函数模拟精确方法(DFT)并提供 相对于DFT，速度提高了4到6个数量级。我们将利用它来执行 精确蒙特卡罗(AIMC)模拟，其中PES与“重要性抽样”一起使用 以提供极大的计算速度。我们还将采取双管齐下的方法。 到全局优化，其中我们使用PES函数进行筛选 大量的几何结构，并生成小得多的子集 为其计算DFT能量。 在这三条战线上工作，我们将努力揭示以下原则 主宰着相对稳定性和几何结构，而磁性， 双金属团簇的电子和化学性质。基于这些 原则，和我们团队独有的新方法，我们将设计稳定 纳米合金在催化和磁性方面的可能应用 数据存储设备。

We develop high performance computational methods to explore potential energy surfaces (PES, mathematical functions that describe how the energy of molecules changes when the geometry of the molecule changes), thus providing the foundation for studies of molecular structure and chemical reactivity. Successful implementation of this proposal will create a unique set of computational tools for studying molecular structures, transition states, and reaction paths, and for the simulation of systems with hundreds of atoms. These tools will be of great importance to researchers in Canada and beyond. One of our methods mimics DFT and gives a computational speed-up of 4 to 6 orders of magnitude. It will allow the study of nanoalloys with potential applications in catalysis and data storage devices. Taking clues from our recent discovery of bimetallic cages, we will search for new and bigger all-metal cages. Minima of the PES are equilibrium geometries of molecules; saddle points are transition states; steepest descent lines connecting saddle points to minima are reaction paths; low energy regions in the vicinity of steepest-descent lines are important for detailed models of reaction mechanisms. We will continue to make expert use of quantum chemistry and density functional theory (DFT), combined with our own computer codes, to investigate PES. Students will get trained in research projects that combine method development and applications and will acquire a variety of skills: expertise in UNIX and quantum chemistry software, programming and shell scripting, mathematical modeling, and general scientific literacy. The projects are designed at various levels to address the needs and capabilities of undergraduate, postgraduate, and postdoctoral students. We will work on three fronts to study nanoalloys that have interesting properties and potential use in catalysis and information technologies. (1) Global optimization. Evolutionary computing methods, like Genetic Algorithms (GA), will be developed to determine the geometric structure of nanoalloy clusters. (2) Reaction mechanisms. We have used Particle Swarm Optimization (PSO) to discover, without human supervision, the sequence of geometric transformations that occur in molecules during chemical reactions (``reaction mechanisms''). We will create a new method, based on chemical intuition and optimization techniques, to find a priori (without any DFT calculation) reaction mechanisms. We will find, on a case-by-case basis, whether this mechanism is a good approximation to the lowest-energy reaction path (the true mechanism), and will classify chemical reactions accordingly. (3) Atomistic simulations. We developed a new fitting method for creating high-dimensional PES that is fully automated, does not require the user to input a function form, and which works for practically any chemical composition. This PES function mimics an accurate method (DFT) and offers a speed-up of 4 to 6 orders of magnitude relative to DFT. We will use it to carry out accurate Monte Carlo (AIMC) simulations where the PES is used with "importance sampling" to give great computational speed-up. We will also take a two-pronged approach to global optimization where we use the PES function for screening a large number of geometric structures and generate a much smaller subset for which the DFT energy gets calculated. Working on those three fronts we will try to uncover the principles that govern the relative stability and geometric structure, and the magnetic, electronic and chemical properties of bimetallic clusters. Based on these principles, and novel methods unique to our group, we will design stable nanoalloys for possible applications in catalysis and magnetic data storage devices.