Search Results

Search Constraints

Start Over
Filtering by: Keyword machine learning Remove constraint Keyword: machine learning Language English Remove constraint Language: English

Search Results

  • Machine learning models for Si nanoparticle growth in nonthermal plasma

    work
    Creator:
    Raymond, Matt, Elvati, Paolo, Saldinger, Jacob C, Lin, Jonathan, Shi, Xuetao, and Violi, Angela
    Description:
    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.
    Keyword:
    machine learning, molecular dynamics, nanoparticle, nonthermal plasma, silane, and sticking coefficient
    Citation to related publication:
    Raymond, M., Elvati, P., Saldinger, J. C., Lin, J., Shi, X., & Violi, A. (2025). Machine learning models for Si nanoparticle growth in nonthermal plasma. Plasma Sources Science and Technology.  https://doi.org/10.1088/1361-6595/adbae1 and  https://arxiv.org/abs/2501.00003
    Discipline:
    Science

  • Data for Improvement of Plasma Sheet Neural Network Accuracy with Inclusion of Physical Information

    work
    Creator:
    Swiger, Brian M., Liemohn, Michael W., and Ganushkina, Natalia Y.
    Description:
    We sampled the near-Earth plasma sheet using data from the NASA Time History of Events and Macroscale Interactions During Substorms mission. For the observations of the plasma sheet, we used corresponding interplanetary observations using the OMNI database. We used these data to develop a data-driven model that predicts plasma sheet electron flux from upstream solar wind variations. The model output data are included in this work, along with code for analyzing the model performance and producing figures used in the related publication. and Data files are included in hdf5 and Python pickle binary formats; scripts included are set up for use of Python 3 to access and process the pickle binary format data.
    Keyword:
    neural network, plasma sheet, solar wind, machine learning, keV electron flux, deep learning, and space weather
    Citation to related publication:
    Swiger, B. M., Liemohn, M. W., & Ganushkina, N. Y. (2020). Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information. Frontiers in Astronomy and Space Sciences, 7.  https://doi.org/10.3389/fspas.2020.00042
    Discipline:
    Science and Engineering