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Interatomic potential parameterization using particle swarm optimization: Case study of glassy silica

机译:使用粒子群优化的内部潜在参数化:玻璃二氧化硅案例研究

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Classical molecular dynamics simulations of glassy materials rely on the availability of accurate yet computationally efficient interatomic force fields. The parameterization of new potentials remains challenging due to the non-convex nature of the accompanying optimization problem, which renders the traditional optimization methods inefficient or subject to bias. In this study, we present a new parameterization method based on particle swarm optimization (PSO), which is a stochastic population-based optimization method. Using glassy silica as a case study, we introduce two interatomic potentials using PSO, which are parameterized so as to match structural features obtained from ab initio simulations and experimental neutron diffraction data. We find that the PSO algorithm is highly efficient at searching for and identifying viable potential parameters that reproduce the structural features used as the target in the parameterization. The presented approach is very general and can be easily applied to other interatomic potential parameterization schemes.
机译:玻璃材料的经典分子动力学模拟依赖于精确但计算效率高的原子间力场的可用性。由于伴随优化问题的非凸性,新势的参数化仍然具有挑战性,这使得传统的优化方法效率低下或存在偏差。在本研究中,我们提出了一种新的基于粒子群优化(PSO)的参数化方法,这是一种基于随机种群的优化方法。以玻璃态二氧化硅为例,我们使用粒子群算法引入了两个原子间势,对它们进行了参数化,以匹配从从头算模拟和实验中子衍射数据中获得的结构特征。我们发现,粒子群优化算法在搜索和识别可行的潜在参数方面非常高效,这些参数可以重现作为参数化目标的结构特征。所提出的方法非常通用,可以很容易地应用于其他原子间势参数化方案。

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