We perform a computational screening for two-dimensional (2D) magnetic materials based on experimental bulk compounds present in the Inorganic Crystal Structure Database and Crystallography Open Database. A recently proposed geometric descriptor is used to extract materials that are exfoliable into 2D derivatives and we find 85 ferromagnetic and 61 antiferromagnetic materials for which we obtain magnetic exchange and anisotropy parameters using density functional theory. For the easy-axis ferromagnetic insulators we calculate the Curie temperature based on a fit to classical Monte Carlo simulations of anisotropic Heisenberg models. We find good agreement with the experimentally reported Curie temperatures of known 2D ferromagnets and identify 10 potentially exfoliable 2D ferromagnets that have not been reported previously. In addition, we find 18 easy-axis antiferromagnetic insulators with several compounds exhibiting very strong exchange coupling and magnetic anisotropy.

The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences (MADs) of 0.6–0.7 pH units. For carboxylic acids, the accuracy (0.7–1.0 pH units) is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5–0.6), slightly worse (0.5–1.0), and worse (1.0–2.5), provided that di- and tri-ethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug-like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit) error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result, most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2–5 min per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for high-throughput screening using on the order of 100 core processors.

Research on selenium solar cells is regaining momentum due to the exciting prospect of integrating a single-element, wide-bandgap (≈1.95 eV) photoabsorber in tandem with a lower bandgap photovoltaic device. Low...

We demonstrate that elusive high-energy metastable crystal structures can be determined from molecular dynamics simulations.

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,I dunnP restholm, IlirianaQ oqaj, ChristinaB.R iel, To bias V. Rostgaard, Nora Saleh, HannibalM.S chultz, Mark Standland,Jens S. Svenningsen, RasmusTruels Sørensen, JesperV isby,E milie L. Wolff-Sneedorff, Malte Hee Zachariassen, Edmond A. Ziari, Henning O. Sørensen, and Thomas Just Sørensen* [a] To Professor Klaus Bechgaard and Professor ThomasB jørnholm for always teaching to think outside the box Abstract: Ionic self-assembly (ISA) is ap rovenm ethod that exploits non-covalenti nteractions to generate supramolecular materials. Here, we have expanded the scope of this approach fabricating thin films with nanoscopic order maintained over centimeters. Cationiclayers of benzalkonium surfactants form al amellar template. The template is able to host layers of negatively charged polyaromatic functional units, hered emonstrated with b-naphthol-derived azo-dyes. We show that av arietyo ft hese functional building blocks can be incorporated in the lamellar templatet hrough ISA. Sixteen different materials were produced,c haracterized, and processedi nto thin films, with lamellar order perpendicular to the substrate. Thus, ad esign concept is demonstrated in which diverse functional motifs can be isolated and ordered in a2 Dl attice between layers of alkyl chains in bulk and in thin films, in which the molecular orderi sm aintained and alignedt othe substrate.

We use a generative neural network model to create thousands of new one-dimensional (1D) materials. The model is trained using 508 stable one-dimensional materials from the Computational 1D Materials Database (C1DB) database. More than 500 of the new materials are shown with density-functional theory calculations to be dynamically stable and with heats of formation within 0.2 eV of the convex hull of known materials. Some of the new materials could also have been obtained by chemical element substitution in the training materials, but completely new classes of materials are also produced. The band structures, electronic densities of states, work functions, effective masses, and phonon spectra of the new materials are calculated, and the data are added to the C1DB.

The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences of 0.6 - 0.7 pH units. For carboxylic acids the accuracy (0.7 - 1.0 pH units) is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5 - 0.6), slightly worse (0.5 - 1.0), and worse (1.0 - 2.5), provided that di- and triethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit) error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2-5 minutes per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for high-throughput screening using on the order of 100 core processors.