AUTOGRAPH: ChemicalReaction Networks in 3D

Understanding and analyzing large-scale reaction networks is a fundamental challenge due to their complexity and size, often beyond human comprehension. In this paper, we introduce AUTOGRAPH, the first web-based tool designed for the interactive three-dimensional (3D) visualization and construction of reaction networks. AUTOGRAPH emphasizes ease of use, allowing users to intuitively build, modify, and explore individual reaction networks in real time. The platform supports a wide range of formats, including CHEMKIN, ensuring compatibility and seamless integration with existing data. Key features of AUTOGRAPH include advanced 3D visualization techniques combined with a fast force-directed algorithm, shortest-path searching, and filtering, facilitating the in-depth exploration of reaction networks. By providing detailed and interactive visualizations, our tool enhances users’ ability to comprehend, analyze, and present complex reaction networks, making it a valuable resource for researchers dealing with intricate chemical systems.

Publication Details

Journal
Figshare
Published
2025-01-15
DOI
https://doi.org/10.1021/acs.jcim.4c02106.s001
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

AUTOGRAPH: ChemicalReaction Networks in 3D

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Machine Learning in Materials Science
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AUTOGRAPH: ChemicalReaction Networks in 3D

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Abstract

Understanding and analyzing large-scale reaction networks is a fundamental challenge due to their complexity and size, often beyond human comprehension. In this paper, we introduce AUTOGRAPH, the first web-based tool designed for the interactive three-dimensional (3D) visualization and construction of reaction networks. AUTOGRAPH emphasizes ease of use, allowing users to intuitively build, modify, and explore individual reaction networks in real time. The platform supports a wide range of formats, including CHEMKIN, ensuring compatibility and seamless integration with existing data. Key features of AUTOGRAPH include advanced 3D visualization techniques combined with a fast force-directed algorithm, shortest-path searching, and filtering, facilitating the in-depth exploration of reaction networks. By providing detailed and interactive visualizations, our tool enhances users’ ability to comprehend, analyze, and present complex reaction networks, making it a valuable resource for researchers dealing with intricate chemical systems.

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Openalex Percentile: Top 64%
Machine Learning in Materials Science
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