Abstract Molecular dynamics (MD) simulation requires an accurate potential energy function to describe atomic interactions of interest. Optimization of the function’s numerous parameters is often time-consuming and labor-intensive. In this study, a machine learning inspired evolutionary parametrization technique using the genetic algorithm is developed to decrease the time required to optimize the parameters of the ReaxFF interatomic potential. An artificial neural network is used as a surrogate for the ReaxFF potential to reduce computational time. Changes to the genetic algorithm are incrementally benchmarked for accuracy and time cost with respect to a moderately complex zinc-oxide model to find superior operators for ReaxFF parametrization. It is found that utilizing an artificial neural network significantly boosted performance, as measured by the final total error and the rate of decrease of total error with respect to time. The double-Pareto probability density based crossover operator and a multiple standard deviation based Gaussian mutation scheme outperform their counterparts. The computational time cost to achieve the same level of accuracy relative to manual training is decreased from months to days.

中文翻译：

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使用机器学习自动 ReaxFF 参数化

摘要 分子动力学 (MD) 模拟需要准确的势能函数来描述感兴趣的原子相互作用。函数的众多参数的优化通常既耗时又费力。在这项研究中，开发了一种使用遗传算法的机器学习启发的进化参数化技术，以减少优化 ReaxFF 原子间势参数所需的时间。人工神经网络用作 ReaxFF 潜力的替代品，以减少计算时间。相对于中等复杂的氧化锌模型，对遗传算法的更改在准确性和时间成本方面进行了增量基准测试，以找到用于 ReaxFF 参数化的优秀算子。发现利用人工神经网络显着提高了性能，用最终总误差和总误差随时间下降的速度来衡量。基于双帕累托概率密度的交叉算子和基于多标准差的高斯变异方案优于其对应方案。相对于手动训练，达到相同精度水平的计算时间成本从几个月减少到几天。