Towards Autonomous Machine Learning in Chemistry via Evolutionary Algorithms

09 September 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Machine learning has been emerging as a promising tool in the chemical and materials domain. In this paper, we introduce a framework to automatically perform rational model selection and hyperparameter optimization that are important concerns for the efficient and successful use of machine learning, but have so far largely remained unexplored by this community. The framework features four variations of genetic algorithm and is implemented in the chemml program package. Its performance is benchmarked against popularly used algorithms and packages in the data science community and the results show that our implementation outperforms these methods both in terms of time and accuracy. The effectiveness of our implementation is further demonstrated via a scenario involving multi-objective optimization for model selection.


hyperparameter optimization
model selection
genetic algorithm
deep learning

Supplementary materials

GA hpo-SI


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