GoFlow: Efficient Transition State Geometry Prediction with Flow Matching and E(3)-Equivariant Neural Networks

Code to reproduce the results of the GoFlow paper. Instruction can be found on https://github.com/heid-lab/goflow

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-02
DOI
https://doi.org/10.5281/zenodo.17251207
Primary Topic
Nuclear Materials and Properties
Type
article
Field-Weighted Citation Impact
0.00
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article

GoFlow: Efficient Transition State Geometry Prediction with Flow Matching and E(3)-Equivariant Neural Networks

Zenodo (CERN European Organization for Nuclear Research)
Nuclear Materials and Properties
article

GoFlow: Efficient Transition State Geometry Prediction with Flow Matching and E(3)-Equivariant Neural Networks

article en

Abstract

Code to reproduce the results of the GoFlow paper. Instruction can be found on https://github.com/heid-lab/goflow

Zenodo (CERN European Organization for Nuclear Research)
TU Wien (AT)
Openalex Percentile: Top 58%
Nuclear Materials and Properties
DOIOpen Access PDFOpenAlex
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GoFlow: Efficient Transition State Geometry Prediction with Flow Matching and E(3)-Equivariant Neural Networks · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS