Curating and analyzing centralized data repositories is a valuable approach in resolving the issue of reproducibility, gaining new insights and guiding future experiments, especially in the field of nanomaterials research. In this work, a data set containing processing information and mobility values of 115 DPP-DTT-based organic field effect transistors (OFET) was constructed from 15 publications. A customized classification algorithm was applied to the data set to help identify a reduced design region for polymer solution concentration that would be more likely to result in improved hole mobility. Experiments performed to confirm the insights from the data curation exercise revealed a strong influence of solution concentration on the polymer chain excitonic interactions and electronic performance. Devices fabricated at the critical chain overlap concentration of 5 g/L in chlorobenzene resulted in improved hole mobility, and were in good agreement with the insights provided by the classification algorithm.

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The advent of data analytics techniques and materials informatics provides opportunities to accelerate the discovery and development of organic semiconductors for electronic devices. However, the development of engineering solutions is limited by the ability to control thin-film morphology in an immense parameter space. The combination of highthroughput experimentation (HTE) laboratory techniques and data analytics offers tremendous avenues to traverse the expansive domains of tunable variables offered by organic semiconductor thin films. This Perspective outlines the steps required to incorporate a comprehensive informatics methodology into the experimental development of polymer-based organic semiconductor technologies. The translation of solution processing and property metrics to thin-film behavior is crucial to inform efficient HTE for data collection and application of data-centric tools to construct new process−structure−property relationships. We argue that detailed investigation of the solution state prior to deposition in conjunction with thin-film characterization will yield a deeper understanding of the physicochemical mechanisms influencing performance in π-conjugated polymer electronics, with data-driven approaches offering predictive capabilities previously unattainable via traditional experimental means.

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Experimental data from a patent were analyzed to learn about the small molecule additives that were most effective in mitigating the degradation of polyethylene terephthalate. Two sets of molecular descriptors were calculated for a dataset of 39 additive candidates; unsupervised and supervised analyses were performed to determine the most influential structural features that led to reduced degradation. A clustering approach revealed evidence that performance differences had some structural pattern dependence on the molecular descriptors that were employed. To pinpoint the features responsible for those physical differences, a reduced design region approach was applied to analyze descriptors both individually and in multiple dimensions to determine the effectiveness in a binary classification of high and low performances. For each molecular descriptor type, two or three influential descriptors were identified and justified with respect to the additive performance and physicochemical ability to mitigate degradation. Random forest models were constructed with relatively high predictability for both MACCS-166 (AUC = 0.86) and alvaDesc molecular descriptors (AUC = 0.93). We compare molecular descriptor methods for their ability to construct classifiers and to prioritize experimental work toward building a rich dataset. We find that, in small materials datasets, understanding the underlying physicochemical behavior is indispensable for validating the effectiveness of machine learning models.