ReactionMechanismGenerator/RMG-database
Mar 2026 – Mar 2026
1 Month active
Languages Used
PythonYAML
Technical Skills
Python programmingchemistry modelingconfiguration managementdata managementkineticsthermodynamics
Alon Grinberg Dana
PROFILE
Alon Grinberg Dana
Worked on the ReactionMechanismGenerator/RMG-database repository to enhance automation and reproducibility in reaction mechanism generation. Developed a YAML-driven system for recommending libraries, standardizing auto-mode library selection across various chemistry types. Expanded the database’s coverage by adding new thermo and kinetics libraries for Formic Acid, enabling more accurate and scalable simulations. Leveraged Python and YAML for configuration management and data handling, focusing on chemistry modeling, kinetics, and thermodynamics. The approach reduced manual curation and improved model fidelity, supporting faster and more reliable decision-making in automated workflows. The work demonstrated depth in both technical implementation and domain-specific chemistry knowledge.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
3,281,136
Activity Months1
Your Network
11 peopleSame Organization
@technion.ac.il1
Work History
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026: Focused enhancements to the RMG-database to improve automation, accuracy, and reproducibility for reaction mechanism generation. Implemented YAML-driven library recommendations and expanded Formic Acid chemistry coverage, supporting faster, more reliable auto-mode decisions and simulations. These changes reduce manual curation, improve model fidelity, and enable scalable chemistry exploration across projects.
2 Commits • 1 Features
Mar 1, 2026March 2026: Focused enhancements to the RMG-database to improve automation, accuracy, and reproducibility for reaction mechanism generation. Implemented YAML-driven library recommendations and expanded Formic Acid chemistry coverage, supporting faster, more reliable auto-mode decisions and simulations. These changes reduce manual curation, improve model fidelity, and enable scalable chemistry exploration across projects.
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture100.0%
Performance90.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
Python programmingchemistry modelingconfiguration managementdata managementkineticsthermodynamics
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