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Novel machine learning insights into the QM7b and QM9 quantum mechanics datasets
Julio J Valdés1, Alain B Tchagang1
1National Research Council Canada, Digital Technologies Research Centre, Ottawa, Canada.
Journal of Computational Chemistry
|February 8, 2024
Summary
This study analyzes quantum mechanics datasets to improve inverse molecular design. Machine learning accurately predicts molecular composition from electronic properties, validating a generative approach for discovering new chemical compounds.
Area of Science:
- Computational Chemistry
- Materials Science
- Machine Learning
Background:
- Quantum mechanics datasets (QM7b, QM9) contain electronic properties of organic molecules.
- Understanding data structure is crucial for inverse molecular design, predicting atomic composition from properties.
Purpose of the Study:
- Explore internal structure of QM7b and QM9 datasets.
- Investigate machine learning for inverse molecular design by approximating atomic composition.
- Assess predictability for generative molecular design.
Main Methods:
- Intrinsic dimension analysis, clustering, and outlier detection.
- Multi-target regression and auto-machine learning frameworks.
- Feature engineering and selection techniques.
Main Results:
- Datasets exhibit lower intrinsic dimensionality than descriptive dimensions.
- QM7b shows distinct clusters; QM9 has core/outlier regions related to molecular properties.
- High predictability for inverse molecular design, with feature selection outperforming feature generation.
Conclusions:
- Generative approach for inverse molecular design is validated.
- Machine learning models accurately predict atomic composition from electronic properties.
- This method aids in searching vast chemical compound spaces.
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