[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125502-en":3,"doc-seo-125502-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125502,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine learning models for Si nanoparticle growth in nonthermal plasma","Nanoparticle growth in nonthermal plasmas offers tunable properties but is difficult to model because NTPs are strongly non-equilibrium, making parameter estimation computationally expensive. This work accelerates estimation for growth models by tailoring machine learning approaches for reactive classical molecular dynamics data describing silane-fragment collisions. Performance depends on selecting appropriate loss functions and enforcing correct invariances. Broad molecule diversity in training improves accuracy, and results indicate only 15%–25% of energy and temperature sampling is needed.","Plasma Sources Science and  \nTechnology   \nPAPER • OPEN ACCESS  \nMachine learning models for Si nanoparticle growth in nonthermal plasma  \nTo cite this article: Matt Raymond et al 2025 Plasma Sources Sci. Technol. 34 035014  \nView the article online for updates and enhancements.  \nYou may also like  \n-Collision integrals of electronically excited atoms in air plasmas: II. 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Technol. 34 (2025) 035014 (10pp) [https://doi.org/10.1088/1361-6595/adbae1](https://doi.org/10.1088/1361-6595/adbae1)  \nMachine learning models for Si nanoparticle growth in nonthermal plasma  \nMatt Raymond 1 􀁂, Paolo Elvati2 􀁂, Jacob C Saldinger3,4, Jonathan Lin 1 􀁂, Xuetao Shi2,5 􀁂 and Angela Violi 1,2,3, ∗ 􀁂  \n1 Mechanical Engineering, University of Michigan, Ann Arbor 48109-2125, MI, United States of America  \n2 Electrical Engineering and Computer Science, University of Michigan, Ann Arbor 48109-2125, MI, United States of America  \n3 Chemical Engineering, University of Michigan, Ann Arbor 48109-2125, MI, United States of America  \nE-mail: [avioli@umich.edu](avioli@umich.edu)  \nReceived 31 October 2024, revised 1 February 2025 Accepted for publication 26 February 2025  \nPublished 21 March 2025  \nAbstract  \nNanoparticles 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.  \nSupplementary material for this article is available online  \nKeywords: molecular dynamics, sticking coefficient, silane, machine learning, nanoparticle, nonthermal plasma  \n4 5  \n∗  \nNow at Low Carbon Pathway Innovation at BP.  \nNow at the Dana-Farber Cancer Institute at Harvard.  \nAuthor to whom any correspondence should be addressed.  \nOriginal Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any  \nfurther distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \n1 © 2025 The Author(s) . Published by IOP Publishing Ltd  \n1. Introduction  \nNonthermal plasmas (NTPs) are unique environments where low-temperature neutral species and ions coexist with hightemperature electrons. For this reason, these systems have received considerable attention, especially for synthesizing particles and nanoparticles (NPs) with significant tunability. This flexibility in the final particle properties results from an environment with enough localized energy to cross relatively high free energy barrie","cbCaicGKqxDB5MQ0","https://ap.wps.com/l/cbCaicGKqxDB5MQ0","pdf",2335524,1,11,"English","en",105,"# Introduction\n## Nonthermal plasmas and nanoparticle synthesis\n## Modeling challenges: non-equilibrium and multiscale effects\n## Growth mechanisms and parameter estimation","[{\"question\":\"Why is modeling Si nanoparticle growth in nonthermal plasma computationally challenging?\",\"answer\":\"Nonthermal plasmas are non-equilibrium and multiscale, so describing growth requires expensive simulations to estimate parameters reliably.\"},{\"question\":\"What machine learning strategy is used to improve predictions?\",\"answer\":\"Different machine learning models are tailored to reactive classical molecular dynamics data, with emphasis on appropriate loss functions and enforcing correct invariances.\"},{\"question\":\"How much sampling is needed to achieve high accuracy?\",\"answer\":\"Only about 15%–25% of the energy and temperature sampling is required to reach high prediction accuracy for similar systems.\"}]","Machine learning models for Si nanoparticle growth in nonthermal plasma | 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is modeling Si nanoparticle growth in nonthermal plasma computationally challenging?","Question",{"text":75,"@type":76},"Nonthermal plasmas are non-equilibrium and multiscale, so describing growth requires expensive simulations to estimate parameters reliably.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning strategy is used to improve predictions?",{"text":80,"@type":76},"Different machine learning models are tailored to reactive classical molecular dynamics data, with emphasis on appropriate loss functions and enforcing correct invariances.",{"name":82,"@type":73,"acceptedAnswer":83},"How much sampling is needed to achieve high accuracy?",{"text":84,"@type":76},"Only about 15%–25% of the energy and temperature sampling is required to reach high prediction accuracy for similar 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