Active Meta-Learning for Predicting and Selecting Perovskite Crystallization Experiments

27 October 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. In this paper, we applied the model-agnostic meta-learning (MAML) model and Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammoniumn lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models. We then evaluated the best three algorithms (PLATIPUS, and active-learning k-Nearest Neighbor and Decision Tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcome compared to other active-learning algorithms and a random baseline.


active learning
machine learning
autonomous experimentation

Supplementary materials

Supplementary Materials
Supplementary tables and figures described in the text

Supplementary weblinks


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