Identification of optimally stable nanocluster geometries via mathematical optimization and density-functional theory
Identification of optimally stable nanocluster geometries via mathematical optimization and density-functional theory
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通过数学优化和密度泛函理论识别最佳稳定的纳米团簇几何形状
DOI：
10.1039/c9me00108e
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发表时间：
2020
影响因子：
3.6
通讯作者：
Gounaris, Chrysanthos E.
中科院分区：
文献类型：
--
作者：
Isenberg, Natalie M.;Taylor, Michael G.;Yan, Zihao;Hanselman, Christopher L.;Mpourmpakis, Giannis;Gounaris, Chrysanthos E.
过渡金属的小纳米颗粒，又称纳米团簇，由于其高度可调的性质依赖于尺寸、结构和组成，因此在广泛的应用领域得到了广泛的研究。对于这些小粒子，在理论上预测形成纳米团簇时最有利的原子排列方式方面已经付出了相当大的努力。在这项工作中，我们开发了一个计算框架，将密度泛函理论计算与数学优化建模相结合，以识别各种尺寸的高度稳定的单金属过渡金属纳米团簇。这是通过设计和求解一个严格的数学优化模型来实现的，该模型使一般内聚能函数最大化，从而获得可证明具有最大内聚性的纳米团簇结构。然后，我们利用密度泛函理论计算和误差项回归来确定模型修正，这些修正对于不同过渡金属具有更好的准确性是必要的。这使我们能够将金属特定的、分析功能的内聚能编码到一个基于数学优化的框架中，该框架可以根据密度泛函理论计算准确地预测哪种纳米簇几何形状将是最具内聚性的。我们在Ag， Au, Cu， Pd和Pt的背景下使用了我们的框架，并且我们提出了大小高达100个原子的高度内聚纳米团簇序列，从而产生了可能在实验上可获得的结构和/或结构，可以用作模型纳米团簇进行进一步研究。
Small nanoparticles, a.k.a. nanoclusters, of transition metals have been studied extensively for a wide range of applications due to their highly tunable properties dependent on size, structure, and composition. For these small particles, there has been considerable effort towards theoretically predicting what is the most energetically favorable arrangement of atoms when forming a nanocluster. In this work, we develop a computational framework that couples density-functional theory calculations with mathematical optimization modeling to identify highly stable, mono-metallic transition metal nanoclusters of various sizes. This is accomplished by devising and solving a rigorous mathematical optimization model that maximizes a general cohesive energy function to obtain nanocluster structures of provably maximal cohesiveness. We then utilize density-functional theory calculations and error term regression to identify model corrections that are necessary to account with better accuracy for different transition metals. This allows us to encode metal-specific, analytical functions for cohesive energy into a mathematical optimization-based framework that can accurately predict which nanocluster geometries will be most cohesive according to density-functional theory calculations. We employ our framework in the context of Ag, Au, Cu, Pd and Pt, and we present sequences of highly cohesive nanoclusters for sizes up to 100 atoms, yielding insights into structures that might be experimentally accessible and/or structures that could be used as model nanoclusters for further study.
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影响因子：
3.7
作者：
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通讯作者：
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