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

Abstract

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.

Collaborative methods to enhance reproducibility and accelerate discovery

“…These activities culminated in a competition between algorithms developed in the different problem domains. 57 Automation was necessary to accumulate the initial datasets needed for algorithm development and testing, as well as to define statistically significant performance baselines. However, it should be emphasized that many of these methods are applicable to manual experimentation now that these initial data exist.…”

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

“…58 Algorithmic performance can also depend on the initial data set (the "cold start" problem), and available data sets often exhibit sampling biases. 59 This problem can be partially mitigated by adding additional constraints to maximize the explored input space 54 or by incorporating human expertise in the loop. 60 While previous research articles have benchmarked computational methods and metrics for this task, 61,62 and a recent perspective discussed types of machine-learning guided iterative experimentation toward this goal, 15 a more critical view of the field is that regardless of the accuracy produced by these methods, they will not generate the materials necessary to enable paradigm shifts.…”

The rise of automated curiosity-driven discoveries in chemistry

“…Harnessing the power of serendipity has been historically challenging to chemists, although recent advancements have shown an intriguing reversal of this trend. 24,25 In a recent study, Schrier 26 and colleagues proposed a novel approach combining ML with a serendipity-based recommendation system. Using active learning strategies, i.e., iterative experiment selection, the ML tool aimed to strike a balance between prediction accuracy (exploitation) and curiosity (exploration).…”