Machine Learning Potential Energy Surfaces for Two-Dimensional Metal-Organic Frameworks based on Committee Neural Networks

The input & output files and NNP models parameters for the "Machine Learning Potential Energy Surfaces for Two-Dimensional Metal-Organic Frameworks based on Committee Neural Networks"

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-01
DOI
https://doi.org/10.5281/zenodo.14002969
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Machine Learning Potential Energy Surfaces for Two-Dimensional Metal-Organic Frameworks based on Committee Neural Networks

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

Machine Learning Potential Energy Surfaces for Two-Dimensional Metal-Organic Frameworks based on Committee Neural Networks

article en

Abstract

The input & output files and NNP models parameters for the "Machine Learning Potential Energy Surfaces for Two-Dimensional Metal-Organic Frameworks based on Committee Neural Networks"

Zenodo (CERN European Organization for Nuclear Research)
New Jersey Institute of Technology (US)
Affordable and clean energy
Openalex Percentile: Top 77%
Machine Learning in Materials Science
DOIOpen Access PDFOpenAlex
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Machine Learning Potential Energy Surfaces for Two-Dimensional Metal-Organic Frameworks based on Committee Neural Networks · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS