Abstract Predictive Molecular Dynamics simulations of thermal transport require forcefields that can simultaneously reproduce several structural, thermodynamic and vibrational properties of materials like lattice constants, phonon density of states, and specific heat. This requires a multi-objective optimization approach for forcefield parameterization. Existing methodologies for forcefield parameterization use ad-hoc and empirical weighting schemes to convert this into a single-objective optimization problem. Here, we provide and describe software to perform multi-objective optimization of Stillinger–Weber forcefields (SWFF) for two-dimensional layered materials using the recently developed 3rd generation non-dominated sorting genetic algorithm (NSGA-III). NSGA-III converges to the set of optimal forcefields lying on the Pareto front in the multi-dimensional objective space. This set of forcefields is used for uncertainty quantification of computed thermal conductivity due to variability in the forcefield parameters. We demonstrate this new optimization scheme by constructing a SWFF for a representative two-dimensional material, 2H-MoSe2 and quantifying the uncertainty in their computed thermal conductivity. Program summary Program Title: MOGA-NSGA3 Program Files doi: http://dx.doi.org/10.17632/pbc6nb7hp9.1 Licensing Provisions: GNU General Public License 3 Programming Language: C++ Nature of problem: Interatomic forcefields used for molecular dynamics simulations of thermal conductivity must be parameterized to accurately capture structural and vibrational properties of the material system being modeled. Therefore, these forcefields must be simultaneously optimized against several (n ≥ 5) material properties. However, such parameterization is difficult using existing forcefield parameterization schemes, which are limited to optimization of a single or few (n 3) objectives. Solution method: We present software to perform evolutionary optimization of forcefields for thermal conductivity simulations using the recently developed 3rd generation non-dominated sorting genetic algorithm (NSGA-III). The algorithm’s unique reference-point-based niching and non-dominated sorting schemes enable efficient exploration of higher-dimensional objective spaces while preserving diversity among forcefield populations. The best set of forcefields on the Pareto front are used for estimating uncertainty in computed thermal conductivity due to forcefield parameterization.

中文翻译：

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用于热导率模拟的原子间力场的进化多目标优化和帕累托前沿不确定性量化

摘要 热传输的预测分子动力学模拟需要能够同时再现材料的多种结构、热力学和振动特性的力场，例如晶格常数、声子态密度和比热。这需要一种用于力场参数化的多目标优化方法。现有的力场参数化方法使用临时和经验加权方案将其转换为单目标优化问题。在这里，我们提供并描述了使用最近开发的第三代非支配排序遗传算法 (NSGA-III) 对二维层状材料执行斯蒂林格-韦伯力场 (SWFF) 多目标优化的软件。NSGA-III 收敛于多维目标空间中位于帕累托前沿的一组最优力场。由于力场参数的可变性，这组力场用于计算热导率的不确定性量化。我们通过为代表性二维材料 2H-MoSe2 构建 SWFF 并量化其计算热导率的不确定性来演示这种新的优化方案。程序摘要 程序名称：MOGA-NSGA3 程序文件 doi：http://dx.doi.org/10.17632/pbc6nb7hp9.1 许可条款：GNU 通用公共许可证 3 编程语言：C++ 问题性质：必须对用于热导率分子动力学模拟的原子间力场进行参数化，以准确捕捉所建模材料系统的结构和振动特性。因此，这些力场必须同时针对几种 (n ≥ 5) 材料特性进行优化。然而，使用现有的力场参数化方案很难进行这种参数化，这些方案仅限于优化单个或少数 (n 3) 个目标。求解方法：我们提供了使用最近开发的第三代非支配排序遗传算法 (NSGA-III) 对热导率模拟进行力场进化优化的软件。该算法独特的基于参考点的利基和非支配排序方案能够有效探索更高维的目标空间，同时保持力场种群之间的多样性。Pareto 前沿的最佳力场集用于估计由于力场参数化而导致的计算热导率的不确定性。