Machine learning models for Si nanoparticle growth in nonthermal plasma

TL;DR

This paper explores how machine learning models can efficiently predict the growth of silicon nanoparticles in nonthermal plasma environments, reducing computational costs while maintaining accuracy.

Contribution

It demonstrates the application of tailored machine learning models to molecular dynamics data, achieving high prediction accuracy with significantly less sampling.

Findings

Good prediction performance with proper loss functions and invariances.

Only 15-25% of energy and temperature sampling needed for high accuracy.

Substantial reduction in computational effort is possible.

Abstract

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.