We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches. File list (2) download file view on ChemRxiv Accelerated Discovery of High-Refractive-Index Polyimide... (1.02 MiB) download file view on ChemRxiv R1_R2_PI_screening_results.xlsx (10.96 MiB)

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

Size- and shape-dependent electrochemical activity of nanostructures reveals relationships between nanostructure design and electrochemical performance. However, electrochemical performance of aspect-ratio-tunable quasi-two-dimensional (2D) nanomaterials with anisotropic properties has not been fully investigated. We prepared monodispersed hexagonal covellite (CuS) nanoplatelets (NPls) of fixed thickness (∼2 nm) but broadly tunable diameter (from 8 to >100 nm). These span a range of aspect ratios, from ∼4 to >50, connecting quasi-isotropic and quasi-2D regimes. Tests of electrochemical activity of the NPls for the oxygen reduction reaction in alkaline solution showed improved activity with increasing diameter. Combining experimental results with density functional theory calculations, we attribute size-dependent enhancement to anisotropy of conductivity and electrochemical activity. The lowest computed oxygen adsorption energy was on Cu sites exposed by cleaving covellite along (001) planes through tetrahedrally coordinated Cu atoms. The specific surface area of these planes, which are the top and bottom surfaces of the NPls, remains constant with changing diameter, for fixed NPl thickness. However, charge transport through the electrocatalyst film improves with increasing NPl diameter. These CuS NPl–carbon nanocatalysts provide inspiration for creating well-controlled layered nanomaterials for electrochemical applications and open up opportunities to design new electrocatalysts using transition-metal sulfides.

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>