Improved Scaling of Molecular Network Calculations: The Emergence of Molecular Domains

Controlling Aggregation-Induced Two-Photon Absorption Enhancement via Intermolecular Interactions

Historically, two-photon absorption (2PA) cross sections reported in the literature have been derived from solution-phase measurements. However, such techniques fail to grasp the implications of how these cross sections can be impacted by varying degrees of aggregation or in the condensed phase as bulk solids or thin films. For a precise determination of how aggregation impacts 2PA at a molecular level, computational methods present themselves as ideal. Herein, a series of quadrupolar π-conjugated dyes were simulated by molecular dynamics (MD) in the gas phase and condensed phase. In the condensed phase, their intermolecular interactions and electronic coupling behavior were fully characterized, both quantitatively and qualitatively. Using quadratic-response time-dependent density functional theory, 2PA cross sections of structures derived from MD trajectories were calculated. Comparisons are made between gas-phase and condensed-phase results, and enhancement factors are defined to show how certain dyes may experience changes in their respective 2PA cross sections as a function of aggregation. It was found that these cross sections depend heavily on conformational locking in the condensed phase and relative stacking arrangements. J-aggregates were associated with enhanced 2PA and H-aggregates with quenched 2PA activity. However, in a highly disordered aggregate, the effects of these stacking arrangements are averaged out of the bulk result, and the effects of conformational locking dominate.

Prediction of chemical compounds properties using a deep learning model

The discovery of new medications in a cost-effective manner has become the top priority for many pharmaceutical companies. Despite decades of innovation, many of their processes arguably remain relatively inefficient. One such process is the prediction of biological activity. This paper describes a new deep learning model, capable of conducting a preliminary screening of chemical compounds in-silico. The model has been constructed using a variation autoencoder to generate chemical compound fingerprints, which have been used to create a regression model to predict their LogD property and a classification model to predict binding in selected assays from the ChEMBL dataset. The conducted experiments demonstrate accurate prediction of the properties of chemical compounds only using structural definitions and also provide several opportunities to improve upon this model in the future.

Nanotechnology for catalysis and solar energy conversion

This roadmap on Nanotechnology for Catalysis and Solar Energy Conversion focuses on the application of nanotechnology in addressing the current challenges of energy conversion: 'high efficiency, stability, safety, and the potential for low-cost/scalable manufacturing' to quote from the contributed article by Nathan Lewis. This roadmap focuses on solar-to-fuel conversion, solar water splitting, solar photovoltaics and bio-catalysis. It includes dye-sensitized solar cells (DSSCs), perovskite solar cells, and organic photovoltaics. Smart engineering of colloidal quantum materials and nanostructured electrodes will improve solar-to-fuel conversion efficiency, as described in the articles by Waiskopf and Banin and Meyer. Semiconductor nanoparticles will also improve solar energy conversion efficiency, as discussed by Boschloo et al in their article on DSSCs. Perovskite solar cells have advanced rapidly in recent years, including new ideas on 2D and 3D hybrid halide perovskites, as described by Spanopoulos et al 'Next generation' solar cells using multiple exciton generation (MEG) from hot carriers, described in the article by Nozik and Beard, could lead to remarkable improvement in photovoltaic efficiency by using quantization effects in semiconductor nanostructures (quantum dots, wires or wells). These challenges will not be met without simultaneous improvement in nanoscale characterization methods. Terahertz spectroscopy, discussed in the article by Milot et al is one example of a method that is overcoming the difficulties associated with nanoscale materials characterization by avoiding electrical contacts to nanoparticles, allowing characterization during device operation, and enabling characterization of a single nanoparticle. Besides experimental advances, computational science is also meeting the challenges of nanomaterials synthesis. The article by Kohlstedt and Schatz discusses the computational frameworks being used to predict structure-property relationships in materials and devices, including machine learning methods, with an emphasis on organic photovoltaics. The contribution by Megarity and Armstrong presents the 'electrochemical leaf' for improvements in electrochemistry and beyond. In addition, biohybrid approaches can take advantage of efficient and specific enzyme catalysts. These articles present the nanoscience and technology at the forefront of renewable energy development that will have significant benefits to society.

Photovoltaic Blend Microstructure for High Efficiency Post-Fullerene Solar Cells. To Tilt or Not To Tilt?

Achieving efficient polymer solar cells (PSCs) requires a structurally optimal donor-acceptor heterojunction morphology. Here we report the combined experimental and theoretical characterization of a benzodithiophene-benzothiadiazole donor polymer series (PBTZF4-R; R = alkyl substituent) blended with the non-fullerene acceptor ITIC-Th and analyze the effects of substituent dimensions on blend morphology, charge transport, carrier dynamics, and PSC metrics. Varying substituent dimensions has a pronounced effect on the blend morphology with a direct link between domain purity, to some extent domain dimensions, and charge generation and collection. The polymer with the smallest alkyl substituent yields the highest PSC power conversion efficiency (PCE, 11%), reflecting relatively small, high-purity domains and possibly benefiting from "matched" donor polymer-small molecule acceptor orientations. The distinctive morphologies arising from the substituents are investigated using molecular dynamics (MD) simulations which reveal that substituent dimensions dictate a well-defined set of polymer conformations, in turn driving chain aggregation and, ultimately, the various film morphologies and mixing with acceptor small molecules. A straightforward energetic parameter explains the experimental polymer domain morphological trends, hence PCE, and suggests strategies for substituent selection to optimize PSC materials morphologies.

Electrothermal Transistor Effect and Cyclic Electronic Currents in Multithermal Charge Transfer Networks

A theory is developed to describe the coupled transport of energy and charge in networks of electron donor-acceptor sites which are seated in a thermally heterogeneous environment, where the transfer kinetics are dominated by Marcus-type hopping rates. It is found that the coupling of heat and charge transfer in such systems gives rise to exotic transport phenomena which are absent in thermally homogeneous systems and cannot be described by standard thermoelectric relations. Specifically, the directionality and extent of thermal transistor amplification and cyclical electronic currents in a given network can be controlled by tuning the underlying temperature gradient in the system. The application of these findings toward the optimal control of multithermal currents is illustrated on a paradigmatic nanostructure.

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

The response of a composite material is the result of a complex interplay between the prevailing mechanics and the heterogenous structure at disparate spatial and temporal scales. Understanding and capturing the multiscale phenomena is critical for materials modeling and can be pursued both by physical simulation-based modeling as well as data-driven machine learning-based modeling. In this work, we build machine learning-based data models as surrogate models for approximating the microscale elastic response as a function of the material microstructure (also called the elastic localization linkage). In building these surrogate models, we particularly focus on understanding the role of contexts, as a link to the higher scale information that most evidently influences and determines the microscale response. As a result of context modeling, we find that machine learning systems with context awareness not only outperform previous best results, but also extend the parallelism of model training so as to maximize the computational efficiency.