Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction.

Area of Science:

• Computational chemistry and materials science
• Machine learning applications in molecular design

Background:

• Deep learning models excel at predicting molecular properties for drug design and materials science.
• Training these models requires extensive, resource-intensive experimental data, limiting their generalization to novel molecular structures.

Purpose of the Study:

• To evaluate uncertainty quantification methods for accelerating material development via uncertainty-guided experimental design.
• To assess the efficacy of these methods for electrolyte design, focusing on aqueous solubility and redox potential prediction.
• To develop novel evaluation strategies for assessing uncertainty estimates on diverse datasets.

Main Methods:

• Comprehensive evaluation of existing uncertainty quantification techniques.
• Development of new methods to test uncertainty estimate utility on in-domain and out-of-domain data.
• Application of selected uncertainty estimation methods within an active learning framework for experimental design.

Main Results:

• Demonstrated the potential of uncertainty-guided active learning to reduce data requirements for molecular property prediction.
• Showcased improved generalization capabilities of models trained with uncertainty-guided approaches.
• Identified effective uncertainty estimation methods for electrolyte design applications.

Conclusions:

• Uncertainty-guided experimental design offers a promising strategy to accelerate the discovery of new materials, particularly electrolytes.
• Active learning powered by robust uncertainty quantification can significantly enhance the efficiency and scope of molecular design.
• The developed evaluation methods provide a framework for assessing the practical utility of uncertainty estimates in materials science.