Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions

One of the grand challenges of utilizing machine learning for the discovery of innovative new polymers lies in the difficulty of accurately representing the complex structures of polymeric materials. Although a wide array of hand-designed polymer representations have been explored, there has yet to be an ideal solution for how to capture the periodicity of polymer structures, and how to develop polymer descriptors without the need for human feature design. In this work, we tackle these problems through the development of our periodic polymer graph representation. Our pipeline for polymer property predictions is comprised of our polymer graph representation that naturally accounts for the periodicity of polymers, followed by a message-passing neural network (MPNN) that leverages the power of graph deep learning to automatically learn chemically-relevant polymer descriptors. Across a diverse dataset of 10 polymer properties, we find that this polymer graph representation consistently outperforms hand-designed representations with a 20% average reduction in prediction error. Our results illustrate how the incorporation of chemical intuition through directly encoding periodicity into our polymer graph representation leads to a considerable improvement in the accuracy and reliability of polymer property predictions. We also demonstrate how combining polymer graph representations with message-passing neural network architectures can automatically extract meaningful polymer features that are consistent with human intuition, while outperforming human-derived features. This work highlights the advancement in predictive capability that is possible if using chemical descriptors that are specifically optimized for capturing the unique chemical structure of polymers

Isoindigo-Containing Molecular Semiconductors: Effect of Backbone Extension on Molecular Organization and Organic Solar Cell Performance

We have synthesized three new isoindigo-based small molecules by extending the conjugated length through the incorporation of octyl-thiophene units between the isoindigo core and benzothiophene terminal units. Both UV–vis and Grazing incidence X-ray diffraction experiments show that such extension of the π-conjugated backbone can induce H-aggregation, and enhance crystallinity and molecular ordering of these isoindigo-based small molecules in the solid state. Compared to two other isoindigo-based derivatives in the series, the derivative with two octyl-thiophene units, BT-T2-ID, is the most crystalline and ordered, and its molecular packing motif appears to be substantially different. Devices utilizing these new extended isoindigo-based small molecules as the electron donor exhibit higher performance than those utilizing nonextended BT-ID as the electron donor. Particularly, devices containing BT-T2-ID in an as-cast blend with PC₆₁BM show power conversion efficiencies up to 3.4%, which is comparable to the best devices containing isoindigo-based molecular semiconductors and is a record among devices containing isoindigo-based small molecules that were processed in the absence of any additives

Data-driven materials research enabled by natural language processing and information extraction

© 2020 Author(s). 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

Structure–Property Relationship Study of Substitution Effects on Isoindigo-Based Model Compounds as Electron Donors in Organic Solar Cells

We designed and synthesized a series of isoindigo-based derivatives to investigate how chemical structure modification at both the 6,6′<i>-</i> and 5,5′-positions of the core with electron-rich and electron-poor moieties affect photophysical and redox properties as well as their solid-state organization. Our studies reveal that 6,6′-substitution on the isoindigo core results in a stronger intramolecular charge transfer band due to strong electronic coupling between the 6,6′-substituent and the core, whereas 5,5′-substitution induces a weaker CT band that is more sensitive to the electronic nature of the substituents. In the solid state, 6,6′-derivatives generally form <i>J</i>-aggregates, whereas 5,5′-derivatives form <i>H</i>-aggregates. With only two branched ethylhexyl side chains, the 6,6′-derivatives form organized lamellar structures in the solid state. The incorporation of electron-rich benzothiophene, <b>BT</b>, substituents further enhances ordering, likely because of strong intermolecular donor–acceptor interactions between the <b>BT</b> substituent and the electron-poor isoindigo core on neighboring compounds. Collectively, the enhanced photophysical properties and solid-state organization of the 6,6′-benzothiophene substituted isoindigo derivative compared to the other isoindigo derivatives examined in this study resulted in solar cells with higher power conversion efficiencies when blended with a fullerene derivative

Data Mining for Parameters Affecting Polymorph Selection in Contorted Hexabenzocoronene Derivatives

The macroscopic properties of molecular materials can be drastically influenced by their solid-state packing arrangements, of which there can be many (e.g., polymorphism). Strategies to controllably and predictively access select polymorphs are thus highly desired, but computationally predicting the conditions necessary to access a given polymorph is challenging with the current state of the art. Using derivatives of contorted hexabenzocoronene, cHBC, we employed data mining, rather than first-principles approaches, to find relationships between the crystallizing molecule, postdeposition solvent-vapor annealing conditions that induce polymorphic transformation, and the resulting polymorphs. This analysis yields a correlative function that can be used to successfully predict the appearance of either one of two polymorphs in thin films of cHBC derivatives. Within the postdeposition processing phase space of cHBC derivatives, we have demonstrated an approach to generate guidelines to select crystallization conditions to bias polymorph access. We believe this approach can be applied more broadly to accelerate the predictions of processing conditions to access desired molecular polymorphs, making progress toward one of the grand challenges identified by the Materials Genome Initiative

Data Mining for Parameters Affecting Polymorph Selection in Contorted Hexabenzocoronene Derivatives

The macroscopic properties of molecular materials can be drastically influenced by their solid-state packing arrangements, of which there can be many (e.g., polymorphism). Strategies to controllably and predictively access select polymorphs are thus highly desired, but computationally predicting the conditions necessary to access a given polymorph is challenging with the current state of the art. Using derivatives of contorted hexabenzocoronene, cHBC, we employed data mining, rather than first-principles approaches, to find relationships between the crystallizing molecule, postdeposition solvent-vapor annealing conditions that induce polymorphic transformation, and the resulting polymorphs. This analysis yields a correlative function that can be used to successfully predict the appearance of either one of two polymorphs in thin films of cHBC derivatives. Within the postdeposition processing phase space of cHBC derivatives, we have demonstrated an approach to generate guidelines to select crystallization conditions to bias polymorph access. We believe this approach can be applied more broadly to accelerate the predictions of processing conditions to access desired molecular polymorphs, making progress toward one of the grand challenges identified by the Materials Genome Initiative