MultiSolSegment: Trained Model Weights

Sanghi, Ojas; Jost, Norman; Pierce, Benjamin G.; Cooper, Emma; Deane, Isaiah; Braid, Jennifer L.

doi:10.21948/2997859

Title: MultiSolSegment: Trained Model Weights

Dataset
Other Related Research

Abstract

The resulting model weights for MultiSolSegment to be loaded into the python code for other to use.

Authors:

Sanghi, Ojas; Jost, Norman; Pierce, Benjamin G.; Cooper, Emma; Deane, Isaiah; Braid, Jennifer L.

Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
Case Western Reserve Univ., Cleveland, OH (United States)
Univ. of Colorado, Boulder, CO (United States)

Publication Date:: Tue Oct 28 00:00:00 EDT 2025

DOE Contract Number:: AC36-08GO28308

Research Org.:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Case Western Reserve Univ.; Univ. of Colorado, Boulder

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Subject:: 14 SOLAR ENERGY; deep learning; electroluminescence; model weights; multisolsegment; segmentation; solar cell defects

OSTI Identifier:: 2997859

DOI:: https://doi.org/10.21948/2997859

Citation Formats


                    Sanghi, Ojas, Jost, Norman, Pierce, Benjamin G., Cooper, Emma, Deane, Isaiah, and Braid, Jennifer L. MultiSolSegment: Trained Model Weights.  United States: N. p., 2025. 
        Web.  doi:10.21948/2997859.

Copy to clipboard


                    Sanghi, Ojas, Jost, Norman, Pierce, Benjamin G., Cooper, Emma, Deane, Isaiah, & Braid, Jennifer L. MultiSolSegment: Trained Model Weights.  United States.  doi:https://doi.org/10.21948/2997859

Copy to clipboard


                    Sanghi, Ojas, Jost, Norman, Pierce, Benjamin G., Cooper, Emma, Deane, Isaiah, and Braid, Jennifer L. 2025.  
"MultiSolSegment: Trained Model Weights".  United States.  doi:https://doi.org/10.21948/2997859.  https://www.osti.gov/servlets/purl/2997859. Pub date:Tue Oct 28 00:00:00 EDT 2025

Copy to clipboard


                    
@article{osti_2997859,

  title        = {MultiSolSegment: Trained Model Weights},

  author       = {Sanghi, Ojas and Jost, Norman and Pierce, Benjamin G. and Cooper, Emma and Deane, Isaiah and Braid, Jennifer L.},

  abstractNote = {The resulting model weights for MultiSolSegment to be loaded into the python code for other to use.},

  doi          = {10.21948/2997859},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Oct 28 00:00:00 EDT 2025},

  month        = {Tue Oct 28 00:00:00 EDT 2025}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.21948/2997859

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

pvcracks: trained VAE model

Dataset Jost, Norman ; Pierce, Benjamin G. ; Cooper, Emma ; ...

The resulting model weights for the variational autoencoder for solar cell crack parametrization to be loaded into the python code for other to use
Tularosa Basin Play Fairway: Weights of Evidence Models

Dataset Brandt, Adam

These models are related to weights of evidence play fairway anlaysis of the Tularosa Basin, New Mexico and Texas. They were created through Spatial Data Modeler: ArcMAP 9.3 geoprocessing tools for spatial data modeling using weights of evidence, logistic regression, fuzzy logic and neural networks. It used to identify high values for potential geothermal plays and low values. The results are relative not only within the Tularosa Basin, but also throughout New Mexico, Utah, Nevada, and other places where high to moderate enthalpy geothermal systems are present (training sites).
KSTAR_UEDGE_Dataset: UEDGE Simulation Dataset for Training the KSTAR DivControlNN Surrogate Model

Dataset Zhao, Menglong ; Xu, Xueqiao ; Zhu, Ben
EMT Training Demos

Dataset Hasan, Md Shamim ; Marthi, Phani Ratna Vanamali ; Hoang, Peter ; ...

Demos for EMT Training
Dataset, Code, and Models for Training Deep Learning Potentials for Low Temperature Plasma-Surface Interactions

Dataset Draney, Jack S. ; Panagiotopoulos, Athanassios ; Graves, David

This repository contains datasets, training scripts, and finished models, and test simulations used in the development of DeepREBO— a machine-learned interatomic potential trained to emulate the REBO2 empirical potential. The data was generated to study deep potential development for simulations of plasma-surface interactions. It uses an active learning framework, starting from a minimal dataset and iteratively expanding it. Included are those generated datasets, the trained models, and simulations used to evaluate the performance of the training process. This resource supports reproducibility and provides a reference framework for training deep potentials in plasma-surface interaction studies.

Similar Records