Autonomous experimentation systems use algorithms and {data|information} from prior experiments to {select|choose|pick} and {perform|carry out|execute} new experiments {in order to|to be able to|as a way to|in an effort to|so as to|so that you can} meet a specified objective. In most experimental chemistry {situations|circumstances|scenarios|conditions} {there is a|there’s a} {limited|restricted} set of prior historical {data|information} {available|accessible|obtainable|offered|readily available|out there}, and acquiring new {data|information} {may|might|could|may possibly|may well|may perhaps} be {expensive|costly|pricey|high-priced|high priced|highly-priced} and time consuming, which {places|locations|areas} constraints on machine {learning|studying|understanding|finding out|mastering} {methods|techniques|strategies|approaches|procedures|solutions}. Active {learning|studying|understanding|finding out|mastering} {methods|techniques|strategies|approaches|procedures|solutions} prioritize new experiment {selection|choice} {by using|by utilizing} machine {learning|studying|understanding|finding out|mastering} model uncertainty and predicted outcomes. Meta-learning {methods|techniques|strategies|approaches|procedures|solutions} {attempt to|try to} construct models {that can|that may|that will|that could|which will|which can} {learn|discover|find out|understand|study} {quickly|rapidly|swiftly|speedily|promptly|immediately} {with a|having a|using a} {limited|restricted} set of {data|information} {for a|to get a|for any} new {task|job|activity|process}. {In this|Within this} paper, we applied the model-agnostic meta-learning (MAML) model and Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot {learning|studying|understanding|finding out|mastering} (PLATIPUS) {approach|method|strategy}, which extends MAML to active {learning|studying|understanding|finding out|mastering}, {to the|towards the|for the} {problem|issue|difficulty|dilemma|challenge|trouble} of halide perovskite {growth|development} by inverse temperature crystallization. {Using|Utilizing|Making use of|Employing|Working with|Applying} a dataset of 1870 reactions {conducted|performed|carried out} {using|utilizing|making use of|employing|working with|applying} 19 {different|various|distinct|diverse|unique|distinctive} organoammoniumn lead iodide systems, we determined the optimal {strategies|methods|techniques|approaches|tactics} for incorporating historical {data|information} into active and meta-learning models. We then evaluated {the best|the very best|the most effective|the top|the ideal|the most beneficial} {three|3} algorithms (PLATIPUS, and active-learning k-Nearest Neighbor and {Decision|Choice|Selection} Tree algorithms) with {four|4} new chemical systems in experimental laboratory tests. {With a|Having a|Using a} fixed {budget|spending budget|price range} of 20 experiments, PLATIPUS {makes|tends to make} superior predictions of reaction outcome {compared to|in comparison to|in comparison with|when compared with} other active-learning algorithms {and a|along with a|as well as a|plus a|and also a|in addition to a} random baseline. 668261-21-0 uses 27221-49-4 site PMID:23996047
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