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GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets

GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2647909680

GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets

About this item

Full title

GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2022-09

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

More information

Scope and Contents

Contents

Recent years have seen the advent of molecular simulation datasets that are orders of magnitude larger and more diverse. These new datasets differ substantially in four aspects of complexity: 1. Chemical diversity (number of different elements), 2. system size (number of atoms per sample), 3. dataset size (number of data samples), and 4. domain shi...

Alternative Titles

Full title

GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2647909680

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2647909680

Other Identifiers

E-ISSN

2331-8422

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