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A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery

preprint
revised on 11.05.2019 and posted on 15.05.2019 by Jon Paul Janet, Chenru Duan, Tzuhsiung Yang, Aditya Nandy, Heather Kulik

Machine learning (ML) models, such as artificial neural networks, have emerged as a complement to high-throughput screening, enabling characterization of new compounds in seconds instead of hours. The promise of ML models to enable large-scale, chemical space exploration can only be realized if it is straightforward to identify when molecules and materials are outside the model’s domain of applicability. Established uncertainty metrics for neural network models are either costly to obtain (e.g., ensemble models) or rely on feature engineering (e.g., feature space distances), and each has limitations in estimating prediction errors for chemical space exploration. We introduce the distance to available data in the latent space of a neural network ML model as a low-cost, quantitative uncertainty metric that works for both inorganic and organic chemistry. The calibrated performance of this approach exceeds widely used uncertainty metrics and is readily applied to models of increasing complexity at no additional cost. Tightening latent distance cutoffs systematically drives down predicted model errors below training errors, thus enabling predictive error control in chemical discovery or identification of useful data points for active learning.

Funding

DARPA grant D18AP00039

Office of Naval Research grant N00014-18-1-2434

History

Email Address of Submitting Author

hjkulik@mit.edu

Institution

Massachusetts Institute of Technology

Country

United States of America

ORCID For Submitting Author

0000-0001-9342-0191

Declaration of Conflict of Interest

The authors declare no conflict of interest.

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