Browsing by Subject "Chemical reactions"

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    Effect of Salt Additives on Concrete Degradation (Phase II): Executive Summary
    (Minnesota Department of Transportation, 1995-02) Jang, Ji-Won; Iwasaki, Iwao; Weiblen, Paul
    This research builds on a previous investigation, which found that corrosion-inhibitor-added deicing salts caused degradation of concrete by both anions and cations. The latest research phase looked at methods to detect the chemical reactions between concrete and corrosion-inhibitor-added deicing salts, and to determine the chemical compositions of the precipitates formed by chemical reactions between concrete and the corrosion-inhibitor-added deicing salts. The research led to the following conclusions: * a simple method was developed for the detection of chemical reactions between deicing chemicals and concrete; * the results provided clear evidence of the chemical reactions between concrete and the corrosion-inhibitoradded deicing salts; * the different amounts of precipitates (chemical reaction products) found in the test cells were dependent on the type and concentration of corrosion-inhibitor-added deicing salts; * precipitates formed by chemical reactions between deicing chemicals and concrete were identified by using chemical analysis, scanning electron microscopy, and X-ray diffraction analysis; * and precipitates were calcium and/or magnesium phosphates as a major component, and gypsum as a minor component.
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    Neural Network Potentials for Atomistic Simulations of Reactive Chemistry
    (2024-05) Gordon, Adrian
    Atomistic simulations play an important role in a wide range of chemical investigations, including studies of chemical kinetics. These simulations rely on accurate energies and forces, often obtained through expensive ab initio electronic structure calculations. Recently researchers have explored the use of machine learning models to provide analytical and differentiable potential energy surfaces for use in atomistic simulations. These ML models can provide energies at a fraction of the cost of ab initio methods and are also highly accurate within the chemical space represented in the training data. In this work, we explore methods for data sampling techniques for training datasets used to train ML potentials, specifically to calculate chemical kinetics of the OH+ CH4 hydrogen abstraction reaction. In addition, combined ML and molecular mechanics methods for condensed phase reactions is discussed.