Mapping atomistic to coarse-grained polymer models using automatic simplex optimization to fit structural properties

We develop coarse-grained force fields for poly (vinyl alcohol) and poly (acrylic acid) oligomers. In both cases, one monomer is mapped onto a coarse-grained bead. The new force fields are designed to match structural properties such as radial distribution functions of various kinds derived by atomistic simulations of these polymers. The mapping is therefore constructed in a way to take into account as much atomistic information as possible. On the technical side, our approach consists of a simplex algorithm which is used to optimize automatically non-bonded parameters as well as bonded parameters. Besides their similar conformation (only the functional side group differs), poly (acrylic acid) was chosen to be in aqueous solution in contrast to a poly (vinyl alcohol) melt. For poly (vinyl alcohol) a non-optimized bond angle potential turns out to be sufficient in connection with a special, optimized non-bonded potential. No torsional potential has to be applied here. For poly (acrylic acid), we show that each peak of the radial distribution function is usually dominated by some specific model parameter(s). Optimization of the bond angle parameters is essential. The coarse-grained forcefield reproduces the radius of gyration of the atomistic model. As a first application, we use the force field to simulate longer chains and compare the hydrodynamic radius with experimental data.Comment: 34 pages, 3 tables, 16 figure

Mapping atomistic to coarse-grained polymer models using automatic simplex optimization to fit structural properties

We develop coarse-grained force fields for poly (vinyl alcohol) and poly (acrylic acid) oligomers. In both cases, one monomer is mapped onto a coarse-grained bead. The new force fields are designed to match structural properties such as radial distribution functions of various kinds derived by atomistic simulations of these polymers. The mapping is therefore constructed in a way to take into account as much atomistic information as possible. On the technical side, our approach consists of a simplex algorithm which is used to optimize automatically non-bonded parameters as well as bonded parameters. Besides their similar conformation (only the functional side group differs), poly (acrylic acid) was chosen to be in aqueous solution in contrast to a poly (vinyl alcohol) melt. For poly (vinyl alcohol) a non-optimized bond angle potential turns out to be sufficient in connection with a special, optimized non-bonded potential. No torsional potential has to be applied here. For poly (acrylic acid), we show that each peak of the radial distribution function is usually dominated by some specific model parameter(s). Optimization of the bond angle parameters is essential. The coarse-grained forcefield reproduces the radius of gyration of the atomistic model. As a first application, we use the force field to simulate longer chains and compare the hydrodynamic radius with experimental data.Comment: 34 pages, 3 tables, 16 figure

AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization

Many researchers and practitioners see artificial intelligence as a game changer compared to classical statistical models. However, some software providers engage in “AI washing”, relabeling solutions that use simple statistical models as AI systems. By contrast, research on algorithm aversion unsystematically varied the labels for advisors and treated labels such as artificial intelligence and statistical model synonymously. This study investigates the effect of individual labels on users\u27 actual advice utilization behavior. Through two incentivized online within-subjects experiments on regression tasks, we find that labeling human advisors with labels that suggest higher expertise leads to an increase in advice-taking, even though the content of the advice remains the same. In contrast, our results do not suggest such an expert effect for advice-taking from algorithms, despite differences in self-reported perception. These findings challenge the effectiveness of framing intelligent systems as AI-based systems and have important implications for both research and practice

SignatureSpace: a multidimensional, exploratory approach for the analysis of volume data

The analysis of volumetric data is a crucial part in the visualization pipeline, since it determines the features in a volume dataset and henceforth, also its rendering parameters. Unfortunately, volume analysis can also be a very tedious and difficult challenge. To cope with this challenge, this paper describes a novel information visualization driven, explorative approach that allows users to perform an analysis in a comprehensive fashion. From the original data volume, a variety of auxiliary data volumes, the signature volumes, are computed, which are based on intensity, gradients, and various other statistical metrics. Each of these signatures (or signatures in short) is then unified into a multi-dimensional signature space to create a comprehensive scope for the analysis. A mosaic of visualization techniques ranging from parallel coordinates, to colormaps and opacity modulation, is available to provide insight into the structure and feature distribution of the volume dataset, and thus enables a specification of complex multi-dimensional transfer functions and segmentations

Deriving effective mesoscale potentials from atomistic simulations

We demonstrate how an iterative method for potential inversion from distribution functions developed for simple liquid systems can be generalized to polymer systems. It uses the differences in the potentials of mean force between the distribution functions generated from a guessed potential and the true (simulated) distribution functions to improve the effective potential successively. The optimization algorithm is very powerful: convergence is reached for every trial function in few iterations. As an extensive test case we coarse-grained an atomistic all-atom model of poly (isoprene) (PI) using a 13:1 reduction of the degrees of freedom. This procedure was performed for PI solutions as well as for a PI melt. Comparisons of the obtained force fields are drawn. They prove that it is not possible to use a single force field for different concentration regimes.Comment: 32 pages including 12 figure

Integrated platform for high-throughput media optimization 79 Integrated platform for high-throughput media optimization

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