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AMADAR: a python-based package for large scale prediction of Diels-Alder transition state geometries and IRC path
Bienfait K Isamura1, Kevin A Lobb2,3
1Department of Chemistry, Rhodes University, Makhanda, 6140, South Africa.
AMADAR is a Python tool that automates Diels-Alder transition state geometry prediction and analysis. It successfully predicts geometries and analyzes reaction forces, aiding in understanding reaction synchronicity.
Area of Science:
- Computational Chemistry
- Chemical Reaction Dynamics
Background:
- Predicting transition state geometries is complex, requiring expert knowledge.
- Automating this process can significantly advance computational chemistry research.
Purpose of the Study:
- To introduce AMADAR, a Python tool for automated Diels-Alder transition state geometry generation.
- To analyze reaction force and atomic contributions to reaction synchronicity.
Main Methods:
- Developed a Python-based tool (AMADAR) to predict Diels-Alder transition state geometries from product SMILES.
- Implemented modules for reaction force analysis (RFA) and atomic decomposition of reaction force constants.
- Validated the tool on 2000 Diels-Alder cycloadducts from the ZINC database.
Main Results:
- Achieved a 95% success rate in locating Diels-Alder transition states.
- RFA plots effectively indicated reaction synchronicity.
- Atomic decomposition rationalized reaction synchronicity based on atom pair contributions.
Conclusions:
- AMADAR automates Diels-Alder transition state prediction and IRC path analysis.
- The tool provides insights into reaction synchronicity through RFA and atomic decomposition.
- AMADAR is available on GitHub for broader use in computational chemistry.
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