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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. 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.

Application of Cascade Correlation Networks for Structures to Chemistry | SpringerLink

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.

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Neural Networks for Chemists : An Introduction

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Neural networks as tools to solve problems in physics and chemistry

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An Introduction. Zupan, Jure and Johann Gasteiger. Gasteiger; J.