Materials Chemistry

Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models



Machine learning is a useful tool for accelerating materials discovery, however it is a challenge to develop accurate methods that successfully transfer between domains while also broadening the scope of reaction conditions considered. In this paper, we consider how active- and transfer-learning methods can be used as building blocks for predicting reaction outcomes of metal halide perovskite synthesis. We then introduce a serendipity-based recommendation system that guides these methods to balance novelty and accuracy. The model-agnostic recommendation system is tested across active- and transfer-learning algorithms, using laboratory experiments for training and testing and a time-separated hold out that includes four different chemical systems. The serendipity recommendation system achieves high accuracy while increasing the scope of the synthesis conditions explored.

Version notes

Fixed author name typo


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Supplementary material

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Supplementary information

Supplementary weblinks

Github repository containing data and code
This github repository contains training and result data along with all open sourced machine learning models