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

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

doi:10.34770/wq4t-wa25

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

Dataset
Other Related Research

Abstract

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.

Authors:

;

Princeton University

Publication Date:: Wed Sep 10 00:00:00 EDT 2025

DOE Contract Number:: AC02-09CH11466

Research Org.:: Princeton Plasma Physics Laboratory

Sponsoring Org.:: United States Department of Energy; United States Department of Energy; United States Department of Energy

Subject:: active learning; deep potential; interatomic potentials; molecular dynamics; plasma-surface interactions

OSTI Identifier:: 2589045

DOI:: https://doi.org/10.34770/wq4t-wa25

Citation Formats


                    Draney, Jack S., Panagiotopoulos, Athanassios, and Graves, David. Dataset, Code, and Models for Training Deep Learning Potentials for Low Temperature Plasma-Surface Interactions.  United States: N. p., 2025. 
        Web.  doi:10.34770/wq4t-wa25.

Copy to clipboard


                    Draney, Jack S., Panagiotopoulos, Athanassios, & Graves, David. Dataset, Code, and Models for Training Deep Learning Potentials for Low Temperature Plasma-Surface Interactions.  United States.  doi:https://doi.org/10.34770/wq4t-wa25

Copy to clipboard


                    Draney, Jack S., Panagiotopoulos, Athanassios, and Graves, David. 2025.  
"Dataset, Code, and Models for Training Deep Learning Potentials for Low Temperature Plasma-Surface Interactions".  United States.  doi:https://doi.org/10.34770/wq4t-wa25.  https://www.osti.gov/servlets/purl/2589045. Pub date:Wed Sep 10 00:00:00 EDT 2025

Copy to clipboard


                    
@article{osti_2589045,

  title        = {Dataset, Code, and Models for Training Deep Learning Potentials for Low Temperature Plasma-Surface Interactions},

  author       = {Draney, Jack S. and Panagiotopoulos, Athanassios and Graves, David},

  abstractNote = {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.},

  doi          = {10.34770/wq4t-wa25},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Wed Sep 10 00:00:00 EDT 2025},

  month        = {Wed Sep 10 00:00:00 EDT 2025}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.34770/wq4t-wa25

Save / Share:

Export Metadata

Save to My Library

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

Data from "Deep Potential Molecular Dynamics Simulations of Low-Temperature Plasma-Surface Interactions"

Dataset Kounis-Melas, Andreas ; Vella, Joseph ; Panagiotopoulos, Athanassios ; ...

Data and input files related to the paper "Deep Potential Molecular Dynamics Simulations of Low-Temperature Plasma-Surface Interactions" (https://doi.org/10.1116/6.0004027). This includes the final DP model used in all simulations, training data set, example input files to run DeepMD (with LAMMPS), and data tables summarizing the results obtained from the simulations.
BUTTER - Empirical Deep Learning Dataset

Dataset Tripp, Charles ; Perr-Sauer, Jordan ; Hayne, Lucas ; ...

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four learning rates, six minibatch sizes, four levels of label noise, and fourteen levels of L1 and L2 regularization each. Multiple repetitions (typically 30, sometimes 10) of each combination of hyperparameters were preformed, and statistics including training and test loss (using a 80% / 20% shuffled train-test split) are recorded at the end of each training epoch. In total, this dataset covers 178 thousandmore »« less
Datasets for Custom-trained Machine-learning Interatomic Potentials: Nitric Acid Aqueous Solution

Dataset Dinpajooh, Mohammadhasan ; LaCount, Michael D ; Muller, Scott E ; ...

This dataset was generated using an iterative active learning strategy with the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials (MLIPs) for aqueous nitric acid. Each active-learning cycle consisted of three stages: (1) training, (2) exploration, and (3) labeling. The initial training set comprised approximately 800 randomly selected configurations from a previous study by Lewis et al. (https://doi.org/10.1021/jp205510q), which investigated nitric acid solutions at 2, 3, 4, and 5 mol/L. For all configurations, single-point calculations of atomic forces and total energies were performed at the quantum density functional theory BLYP-D2 and PBE-D3 levels of theory using the CP2K Quickstepmore »« less
Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)

Dataset Willard, Jared ; Ciulla, Fabio ; Weierbach, Helen ; ...

This data release provides all data and code used in the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)" to model stream temperature, evaluate, and assess results. The associated manuscript explores current open questions in prediction in ungauged and unmonitored basins concerning top-down versus bottom-up approaches, tradeoffs between data available and input requirements, and the appropriate representation of catchment attributes as inputs to deep learning models. Modeling was done primarily with long short-term memory (LSTM) models, and stream site coverage spans 1362 locations across the conterminous United States.more »« less
BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

Dataset Tripp, Charles ; Perr-Sauer, Jordan ; Bensen, Erik ; ...

The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects. BUTTER-E is intended to be joinedmore »« less

Similar Records