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  • 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

  • Supporting data: Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles

    work
    Creator:
    Saldinger, Jacob, Raymond, Matt, Elvati, Paolo, and Violi, Angela
    Description:
    The accurate and rapid prediction of generic nanoscale interactions is a challenging problem with broad applications. Much of biology functions at the nanoscale, and our ability to manipulate materials and purposefully engage biological machinery requires knowledge of nano-bio interfaces. While several protein-protein interaction models are available, they leverage protein-specific information, limiting their abstraction to other structures. Here, we present NeCLAS, a general, and rapid machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions. Two key aspects distinguish NeCLAS: coarse-grained representations, and the use of environmental features to encode the chemical neighborhood. We showcase NeCLAS with challenges for protein-protein, protein-nanoparticle and nanoparticle-nanoparticle systems, demonstrating that NeCLAS replicates computationally- and experimentally-observed interactions. NeCLAS outperforms current nanoscale prediction models, and it shows cross-domain validity, qualifying as a tool for basic research, rapid prototyping, and design of nanostructures., Software: - To reproduce all-atom molecular dynamics (MD) NAMD is required (version 2.14 or later is suggested). NAMD software and documentation can be found at  https://www.ks.uiuc.edu/Research/namd/, - To reproduce coarse-grained MD simulations, LAMMPS (version 29 Sep 2021 - Update 2 or later is suggested). LAMMPS software and documentation can be found at  https://www.lammps.org, - To rebuild free energy profiles, the PLUMED plugin (version 2.6) was used. PLUMED software and documentation can be found at  https://www.plumed.org/ , and - To generate force matching potentials, the was used the OpenMSCG software was used. OpenMSCG software and documentation can be found at  https://software.rcc.uchicago.edu/mscg/
    Keyword:
    Neural Networks, Proteins, Dimensionality Reduction, Nanoparticles, and Coarse-Graining
    Citation to related publication:
     https://www.biorxiv.org/content/10.1101/2022.08.09.503361v2
    Discipline:
    Science