For very high shear rates beyond 108 s-1, nucleation is strongly inhibited. Our results indicate that the optimal shear rates have a non-monotonic dependence on temperature.The reliability of molecular mechanics (MM) simulations in describing biomolecular ion-driven processes depends on their ability to accurately model interactions of ions simultaneously with water and other biochemical groups. In these models, ion descriptors are calibrated against reference data on ion-water interactions, and it is then assumed that these descriptors will also satisfactorily describe interactions of ions with other biochemical ligands. The comparison against the experiment and high-level quantum mechanical data show that this transferability assumption can break down severely. One approach to improve transferability is to assign cross terms or separate sets of non-bonded descriptors for every distinct pair of ion type and its coordinating ligand. Here, we propose an alternative solution that targets an error-source directly and corrects misrepresented physics. In standard model development, ligand descriptors are never calibrated or benchmarked in the high electric fields present near ions. We demonstrate for a representative MM model that when the polarization descriptors of its ligands are improved to respond to both low and high fields, ligand interactions with ions also improve, and transferability errors reduce substantially. In our case, the overall transferability error reduces from 3.3 kcal/mol to 1.8 kcal/mol. These improvements are observed without compromising on the accuracy of low-field interactions of ligands in gas and condensed phases. Reference data for calibration and performance evaluation are taken from the experiment and also obtained systematically from "gold-standard" CCSD(T) in the complete basis set limit, followed by benchmarked vdW-inclusive density functional theory.The high-pressure properties of fluorine and chlorine are not yet well understood because both are highly reactive and volatile elements, which have made conducting diamond anvil cell and x-ray diffraction experiments a challenge. Here, we use ab initio methods to search for stable crystal structures of both elements at megabar pressures. We demonstrate how symmetry and geometric constraints can be combined to efficiently generate crystal structures that are composed of diatomic molecules. Our algorithm extends the symmetry driven structure search method [R. Domingos et al., Phys. https://www.selleckchem.com/products/ars-1323.html Rev. B 98, 174107 (2018)] by adding constraints for the bond length and the number of atoms in a molecule while still maintaining generality. As a method of validation, we have tested our approach for dense hydrogen and reproduced the known molecular structures of Cmca-12 and Cmca-4. We apply our algorithm to study chlorine and fluorine in the pressure range of 10 GPa-4000 GPa while considering crystal structures with up to 40 atoms per unit cell. We predict chlorine to follow the same series of phase transformations as elemental iodine from Cmca to Immm to Fm3¯m, but at substantially higher pressures. We predict fluorine to transition from a C2/c to Cmca structure at 70 GPa, to a novel orthorhombic and metallic structure with P42/mmc symmetry at 2500 GPa, and finally to its cubic analog form with Pm3¯n symmetry at 3000 GPa.Machine learning-based interatomic potentials are currently garnering a lot of attention as they strive to achieve the accuracy of electronic structure methods at the computational cost of empirical potentials. Given their generic functional forms, the transferability of these potentials is highly dependent on the quality of the training set, the generation of which can be highly labor-intensive. Good training sets should at once contain a very diverse set of configurations while avoiding redundancies that incur cost without providing benefits. We formalize these requirements in a local entropy-maximization framework and propose an automated sampling scheme to sample from this objective function. We show that this approach generates much more diverse training sets than unbiased sampling and is competitive with hand-crafted training sets.Antifreeze proteins (AFPs) are biopolymers capable of interfering with ice growth. Their antifreeze action is commonly understood considering that the AFPs, by pinning the ice surface, force the crystal-liquid interface to bend forming an ice meniscus, causing an increase in the surface free energy and resulting in a decrease in the freezing point ΔTmax. Here, we present an extensive computational study for a model protein adsorbed on a TIP4P/Ice crystal, computing ΔTmax as a function of the average distance d between AFPs, with simulations spanning over 1 µs. First, we show that the lower the d, the larger the ΔTmax. Then, we find that the water-ice-protein contact angle along the line ΔTmax(d) is always larger than 0°, and we provide a theoretical interpretation. We compute the curvature radius of the stable solid-liquid interface at a given supercooling ΔT ≤ ΔTmax, connecting it with the critical ice nucleus at ΔT. Finally, we discuss the antifreeze capability of AFPs in terms of the protein-water and protein-ice interactions. Our findings establish a unified description of the AFPs in the contest of homogeneous ice nucleation, elucidating key aspects of the antifreeze mechanisms and paving the way for the design of novel ice-controlling materials.The purpose of this study was to obtain a more comprehensive understanding of critical thinking within the clinical nursing context. In this review, we addressed the following specific research questions what are the levels of critical thinking among clinical nurses?; what are the antecedents of critical thinking?; and what are the consequences of critical thinking? A narrative literature review was applied in this study. Thirteen articles published from July 2013 to December 2019 were appraised since the most recent scoping review on critical thinking among nurses was conducted from January 1999 to June 2013. The levels of critical thinking among clinical nurses were moderate or high. Regarding the antecedents of critical thinking, the influence of sociodemographic variables on critical thinking was inconsistent, with the exception that levels of critical thinking differed according to years of work experience. Finally, little research has been conducted on the consequences of critical thinking and related factors.