ReaxFF is a computationally efficient force field to simulate complex reactive dynamics in extended molecular models with diverse chemistries, if reliable force-field parameters are available for the chemistry of interest. If not, they must be optimized by minimizing the error ReaxFF makes on a relevant training set. Because this optimization is far from trivial, many methods, in particular genetic algorithms (GAs), have been developed to search for the global optimum in parameter space. Recently, two alternative parameter calibration techniques were proposed, i.e. Monte-Carlo Force Field optimizer (MCFF) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). In this work, CMA-ES, MCFF and a GA method (OGOLEM) are systematically 1 compared using three training sets from the literature. GA shows the smallest risk of getting trapped into a local minimum, whereas CMA-ES is capable of reaching the lowest errors for two third of the cases. For each method, we provide reasonable default settings and our analysis offers useful guidelines for their usage in future work.An important side effect impairing the parameter optimization is numerical noise. A detailed analysis reveals that it can be reduced, e.g. by using exclusively unambiguous geometry optimizations in the training set. Even without this noise, many distinct near-optimal parameter vectors can be found, which opens new avenues for improving the training set and detecting overfitting artifacts.
Laser-induced incandescence (LII) of soot has developed into a popular method for making in situ measurements of soot volume fraction and primary particle sizes. However, there is still a lack of understanding regarding the generation and interpretation of the cooling signals. To model heat transfer from the heated soot particles to the surrounding gas, knowledge of the collision-based cooling as well as reactive events, including oxidation (exothermic) and evaporation (endothermic) is essential. We have simulated LII of soot using the ReaxFF reactive force field for hydrocarbon combustion. Soot was modeled as a stack of four graphene sheets linked together using sp(3) hybridized carbon atoms. To calculate the thermal accommodation coefficient of various gases with soot, graphene sheets of diameter 40 Å were used to create a soot particle containing 2691 atoms, and these simulations were carried out using the ReaxFF version incorporated into the Amsterdam Density Functional program. The reactive force field enables us to simulate the effects of conduction, evaporation, and oxidation of the soot particle on the cooling signal. Simulations were carried out for both reactive and nonreactive gas species at various pressures, and the subsequent cooling signals of soot were compared and analyzed. To correctly model N(2)-soot interactions, optimization of N-N and N-C-H force field parameters against DFT and experimental values was performed and is described in this paper. Subsequently, simulations were performed in order to find the thermal accommodation coefficients of soot with various monatomic and polyatomic gas molecules like He, Ne, Ar, N(2), CO(2), and CH(4). For all these species we find good agreement between our ReaxFF results and previously published accommodation coefficients. We thus believe that Molecular Dynamics using the ReaxFF reactive force field is a promising approach to simulate the physical and chemical aspects of soot LII.
Small copper clusters embedded in argon shells were obtained using the pick-up technique. The clusters were analyzed using X-ray absorption near-edge spectroscopy (XANES) measured at the Cu L 23 -edge. Theoretical interpretation of the experimental spectra has been performed using full-potential finite difference method simulations and density functional theory (DFT) to optimize the geometry of the clusters. As a result, it was found that the icosahedral Cu 13 nanocluster with the geometry optimized by DFT produces theoretical Cu L 23 -XANES similar to the experimental one, whereas the theoretical XANES of the cuboctahedral cluster differs from that of the experimental. The bonding energy for the icosahedral cluster obtained by the present DFT study has a higher absolute value than that for the cuboctahedral cluster, thus also supporting the higher stability of the icosahedral Cu 13 nanocluster.
<div>ReaxFF is a computationally efficient force field to simulate complex reactive dynamics in extended molecular models with diverse chemistries, if reliable force-field parameters are available for the chemistry of interest. If not, they must be calibrated by minimizing the error ReaxFF makes on a relevant training set. Because this optimization is far from trivial, many methods, in particular genetic algorithms (GAs), have been developed to search for the global optimum in parameter space. Recently, two alternative parameter calibration techniques were proposed, i.e.\ Monte-Carlo Force Field optimizer (MCFF) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), which have the potential to find good parameters at a relatively low computational cost. In this work, these two methods are tested, as implemented in ADF2018, using three ReaxFF training sets, which have previously been used to benchmark GAs. Even though MCFF and CMA-ES should not be considered as exhaustive global optimizers, they can find parameters that are comparable in quality to those obtained with GAs. We observe that CMA-ES leads to slightly better results and is less sensitive to the initial guess of the parameters. Concrete recipes are provided for obtaining similar results with new training sets.</div><div>Besides optimization recipes, a successful ReaxFF parameterization requires the design of a good training set. At every trial set of parameters, ReaxFF is used to optimize molecular geometries in the training set. When the optimization of some geometries fails easily, it becomes increasingly difficult to find the optimal parameters. We have addressed this issue by fixing several bugs in the ReaxFF forces and by improving the robustness of the geometry optimization. These improvements cannot eliminate all geometry convergence issues and we recommend to avoid very flexible geometries in the training set.</div><div>Both MCFF and CMA-ES are still liable to converge to sub- or near-optimal parameters, which we detected by repeating the calibration with different random seeds. The existence of distinct near-optimal parameter vectors is a general pattern throughout our study and provides opportunities to improve the training set or to detect overfitting artifacts.</div>
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