Though both the crystal structure and molecular orientation of organic semiconductors are known to impact charge transport in thin-film devices, separately accessing different polymorphs and varying the out-of-plane molecular orientation is challenging, typically requiring stringent control over film deposition conditions, film thickness, and substrate chemistry. Here we demonstrate independent tuning of the crystalline polymorph and molecular orientation in thin films of contorted hexabenzocoronene, c-HBC, during post-deposition processing without the need to adjust deposition conditions. Three polymorphs are observed, two of which have not been previously reported. Using our ability to independently tune the crystal structure and out-of-plane molecular orientation in thin films of c-HBC, we have decoupled and evaluated the effects that molecular packing and orientation have on device performance in thin-film transistors (TFTs). In the case of TFTs comprising c-HBC, polymorphism and molecular orientation are equally important; independently changing either one affects the field-effect mobility by an order of magnitude.

Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains-enabled by techniques adapted from the field of natural language processing-therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. We discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.

This work explores the formation of well-defined molecular p-n junctions in solution-processed self-assembled heterojunction solar cells using dodecyloxy-substituted contorted hexabenzocoronene (12-c-HBC) as a donor material and phenyl-C(70)-butyric acid methyl ester (PC(70)BM) as an acceptor. We find that the contorted 12-c-HBC molecules effectively assemble in solution to form a nested structure with the ball-shaped PC(70)BM. The result is a self-assembled molecular-scale p-n junction. When this well-defined p-n junction is embedded in active films, we can make efficient self-assembled solar cells with minimal amounts of donor material relative to the acceptor. The power conversion efficiency is drastically enhanced by the mode of donor and acceptor assembly within the film.

Material scientists are increasingly adopting the use of machine learning (ML) for making potentially important decisions, such as, discovery, development, optimization, synthesis and characterization of materials. However, despite ML's impressive performance in commercial applications, several unique challenges exist when applying ML in materials science applications. In such a context, the contributions of this work are twofold. First, we identify common pitfalls of existing ML techniques when learning from underrepresented/imbalanced material data. Specifically, we show that with imbalanced data, standard methods for assessing quality of ML models break down and lead to misleading conclusions. Furthermore, we find that the model's own confidence score cannot be trusted and model introspection methods (using simpler models) do not help as they result in loss of predictive performance (reliability-explainability trade-off). Second, to overcome these challenges, we propose a general-purpose explainable and reliable machine-learning framework. Specifically, we propose a novel pipeline that employs an ensemble of simpler models to reliably predict material properties. We also propose a transfer learning technique and show that the performance loss due to models' simplicity can be overcome by exploiting correlations among different material properties. A new evaluation metric and a trust score to better quantify the confidence in the predictions are also proposed. To improve the interpretability, we add a rationale generator component to our framework which provides both model-level and decision-level explanations. Finally, we demonstrate the versatility of

No abstract