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Direct optimization against experimental condensed-phase properties concerning small organic molecules still represents the most reliable way to calibrate the empirical parameters of a force field. However, compared to a corresponding calibration against quantum-mechanical (QM) calculations concerning isolated molecules, this approach is typically very tedious and time-consuming. The present article describes an integrated scheme for the automated refinement of force-field parameters against experimental condensed-phase data, considering entire classes of organic molecules constructed using a fragment library via combinatorial isomer enumeration. The main steps of the scheme, referred to as CombiFF, are as follows (i) definition of a molecule family; (ii) combinatorial enumeration of all isomers; (iii) query for experimental data; (iv) automatic construction of the molecular topologies by fragment assembly; and (v) iterative refinement of the force-field parameters considering the entire family. As a first aply remarkable considering that the force-field calibration did not involve any QM calculation. Once the time-consuming task of target-data selection/curation has been performed, the optimization of a force field only takes a few days. As a result, CombiFF enables an easy assessment of the consequences of functional-form decisions on the accuracy of a force field at an optimal level of parametrization.This viewpoint intends to show recent open issues of using coarse grained models in molecular dynamics simulation. It reviews the current knowledge of the comparison between experimental and simulation data of structural and physical chemical properties that depend on the hydrophilic and hydrophobic behavior of the molecule.The greatest restriction to the theoretical study of the dynamics of photoinduced processes is computationally expensive electronic structure calculations. Machine learning algorithms have the potential to reduce the number of these computations significantly. Here, PySurf is introduced as an innovative code framework, which is specifically designed for rapid prototyping and development tasks for data science applications in computational chemistry. It comes with powerful Plugin and Workflow engines, which allows intuitive customization for individual tasks. Data is automatically stored through the database framework, which enables additional interpolation of properties in previously evaluated regions of the conformational space. To illustrate the potential of the framework, a code for nonadiabatic surface hopping simulations based on the Landau-Zener algorithm is presented here. Deriving gradients from the interpolated potential energy surfaces allows for full-dimensional nonadiabatic surface hopping simulations using only adiabatic energies (energy only). Simulations of a pyrazine model and ab initio-based calculations of the SO2 molecule show that energy-only calculations with PySurf are able to correctly predict the nonadiabatic dynamics of these prototype systems. L-Arginine purchase The results reveal the degree of sophistication, which can be achieved by the database accelerated energy-only surface hopping simulations being competitive to commonly used semiclassical approaches.A major field of current research in chemistry and biology is the development of the tools that enable in situ analysis of complex systems. However, the long-time dynamics simulation for an extremely large system in solution is almost impossible by an all-atom force field combined with an explicit solvent model. The results show that the larger the periodic box is, the closer the properties of the system are to the experimental values. Therefore, how can we carry out simulations for systems that are fast, accurate, and large enough? A method of dividing the periodic box into subdivisions with their surroundings (DBSS) is presented here, and it clearly increases the computation speed without losing accuracy and enables the simulation of extremely large systems by strongly decreasing the dimension of the charge matrix. The DBSS method divides a single periodic box or unit in an extremely large system into several subdivisions with a suitable choice according to atomic coordinates. This method ensures that theseoser to the experimental data as the sizes of the periodic box increase, further validating the need for the simulation of a large system and demonstrating the value of the DBSS method.Cancer is a major public health problem, but despite the several treatment approaches available, patients develop resistance in short time periods, making overcoming resistance or finding more efficient treatments an imperative challenge. Silver nanoparticles (AgNPs) have been described as an alternative option due to their physicochemical properties. The scope of this review was to systematize the available scientific information concerning these characteristics in AgNPs synthesized according to green chemistry's recommendations as well as their cytotoxicity in different cancer models. This is the first paper analyzing, correlating, and summarizing AgNPs' main parameters that modulate their cellular effect, including size, shape, capping, and surface plasmon resonance profile, dose range, and exposure time. It highlights the strong dependence of AgNPs' cytotoxic effects on their characteristics and tumor model, making evident the strong need of standardization and full characterization. AgNPs' application in oncology research is a new, open, and promising field and needs additional studies.Interfacial proton-coupled electron transfer (PCET) reactions are central to the operation of a wide array of energy conversion technologies, but molecular-level insights into interfacial PCET are limited. At carbon surfaces, designer sites for interfacial PCET can be incorporated by conjugating organic acid functional groups to graphite edges though aromatic phenazine linkages. At these graphite-conjugated catalysts (GCCs) bearing organic acid moieties, PCET is driven by complex interfacial electrostatic and field gradients that are difficult to probe experimentally. Herein, the spatially inhomogeneous interfacial electrostatic potentials and electric fields of GCC organic acids are computed as functions of applied potential. The calculated proton-coupled redox potentials for the PCET reactions at the GCC phenazine bridges and organic acid sites are in agreement with cyclic voltammetry measurements for a series of GCC acids. The trends in these redox potentials are explained in terms of the acidity of the molecular analogues and continuous conjugation between the acid and the graphite surface.