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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