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Towards Autonomous Machine Learning in Chemistry via Evolutionary Algorithms

preprint
submitted on 07.09.2019, 01:18 and posted on 09.09.2019, 20:51 by Gaurav Vishwakarma, Mojtaba Haghighatlari, Johannes Hachmann
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.

History

Email Address of Submitting Author

mojtabah@buffalo.edu

Institution

University at Buffalo

Country

United States

ORCID For Submitting Author

0000-0002-3779-2246

Declaration of Conflict of Interest

The authors declare to have no competing financial interests.

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