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The discovery of new compounds and materials has a fundamental impact on industrial and economic development. The discovery process is increasingly supported by computational approaches as they provide efficient means to uncover promising targets. In the past two decades, we have witnessed tremendous growth in the drug discovery field due to the implementation of virtual high-throughput screening (HTPS) techniques. Recently, these techniques have been embraced in various materials applications, such as catalysis, energy materials, optoelectronics, photovoltaics, etc., thereby developing into a promising tool for the discovery of nextgeneration materials. In addition to the discovery of new materials, these HTPS studies provide a solid data foundation for rational design approaches as well as guidance for experimental partners. In this chapter, we review recent HTPS efforts undertaken for new materials for photovoltaics, gas separation, optical devices, and OLEDs. We also review HTPS projects for catalyst materials for various important reactions, such as the oxygen reduction reaction (ORR), oxygen evolution reaction (OER), hydrogen evolution reaction (HER), and carbon dioxide reduction reaction (CO2RR). 

In a previous study, we introduced a new computational protocol to accurately predict the index of refraction (RI) of organic polymers using a combination of first-principles and data modeling. This protocol is based on the Lorentz–Lorenz equation and involves the calculation of static polarizabilities and number densities of oligomer sequences, which are extrapolated to the polymer limit. We chose to compute the polarizabilities within the density functional theory (DFT) framework using the PBE0/def2-TZVP-D3 model chemistry. While this ad hoc choice proved remarkably successful, it is also relatively expensive from a computational perspective. It represents the bottleneck step in the overall RI modeling protocol, thus limiting its utility for virtual high-throughput screening studies, in which efficiency is essential. For polymers that exhibit late-onset extensivity, the employed linear extrapolation scheme can require demanding calculations on long-oligomer sequences, thus becoming another bottleneck. In the work presented here, we benchmark DFT model chemistries to identify approaches that optimize the balance between accuracy and efficiency for this application domain. We compare results for conjugated and non-conjugated polymers, augment our original extrapolation approach with a non-linear option, analyze how the polarizability errors propagate into the RI predictions, and offer guidance for method selection. 

The process of developing new compounds and materials is increasingly driven by computational modeling and simulation, which allow us to characterize candidates before pursuing them in the laboratory. One of the non-trivial properties of interest for organic materials is their packing in the bulk, which is highly dependent on their molecular structure. By controlling the latter, we can realize materials with a desired density (as well as other target properties). Molecular dynamics simulations are a popular and reasonably accurate way to compute the bulk density of molecules, however, since these calculations are computationally intensive, they are not a practically viable option for high-throughput screening studies that assess material candidates on a massive scale. In this work, we employ machine learning to develop a data-derived prediction model that is an alternative to physics-based simulations, and we utilize it for the hyperscreening of 1.5 million small organic molecules as well as to gain insights into the relationship between structural makeup and packing density. We also use this study to analyze the learning curve of the employed neural network approach and gain empirical data on the dependence of model performance and training data size, which will inform future investigations. 

Guest Editors Andrew Ferguson and Johannes Hachmann introduce this themed collection of papers showcasing the latest research leveraging data science and machine learning approaches to guide the understanding and design of hard, soft, and biological materials with tailored properties, function and behaviour. 

Abstract Quantum chemistry must evolve if it wants to fully leverage the benefits of the internet age, where the worldwide web offers a vast tapestry of tools that enable users to communicate and interact with complex data at the speed and convenience of a button press. The Open Chemistry project has developed an open‐source framework that offers an end‐to‐end solution for producing, sharing, and visualizing quantum chemical data interactively on the web using an array of modern tools and approaches. These tools build on some of the best open‐source community projects such as Jupyter for interactive online notebooks, coupled with 3D accelerated visualization, state‐of‐the‐art computational chemistry codes including NWChem and Psi4, and emerging machine learning and data mining tools such as ChemML and ANI. They offer flexible formats to import and export data, along with approaches to compare computational and experimental data.