用机器学习加速等离子体中硅纳米颗粒生长模拟，显著降低计算成本。
Machine learning models for Si nanoparticle growth in nonthermal plasma
- 基于分子动力学数据训练机器学习模型，预测硅烷碎片反应路径。
- 仅需15%-25%的采样即可达高精度，计算量大幅减少。
- 适合需要快速模拟等离子体纳米颗粒生长的研究者。
在非热等离子体（NTP）中形成的纳米颗粒具有独特性质和应用前景。然而，由于NTP的非平衡特性，其生长过程建模面临巨大挑战，计算成本高昂。本文针对加速模型参数估计的问题，探索不同机器学习模型的适配方法。利用反应性经典分子动力学数据，捕捉硅烷碎片在NTP中的碰撞过程。这些反应具有明确的趋势，但定量分析困难、难以泛化，且依赖耗时模拟。结果表明，采用合适损失函数并施加正确不变性约束，可实现良好预测性能。训练集分子多样性对精度至关重要，但仅需15%-25%的能量与温度采样即可达到高精度，表明类似系统有望显著降低计算开销。
原文摘要 · Abstract (English)
Nanoparticles (NPs) formed in nonthermal plasmas (NTPs) can have unique properties and applications. However, modeling their growth in these environments presents significant challenges due to the non-equilibrium nature of NTPs, making them computationally expensive to describe. In this work, we address the challenges associated with accelerating the estimation of parameters needed for these models. Specifically, we explore how different machine learning models can be tailored to improve prediction outcomes. We apply these methods to reactive classical molecular dynamics data, which capture the processes associated with colliding silane fragments in NTPs. These reactions exemplify processes where qualitative trends are clear, but their quantification is challenging, hard to generalize, and requires time-consuming simulations. Our results demonstrate that good prediction performance can be achieved when appropriate loss functions are implemented and correct invariances are imposed. While the diversity of molecules used in the training set is critical for accurate prediction, our findings indicate that only a fraction (15-25\%) of the energy and temperature sampling is required to achieve high levels of accuracy. This suggests a substantial reduction in computational effort is possible for similar systems.
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