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首页> 外文期刊>The Journal of Chemical Physics >Multivariable extrapolation of grand canonical free energy landscapes
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Multivariable extrapolation of grand canonical free energy landscapes

机译：大型规范自由能景观的多变量外推

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摘要

We derive an approach for extrapolating the free energy landscape of multicomponent systems in the grand canonical ensemble, obtained from flat-histogram Monte Carlo simulations, from one set of temperature and chemical potentials to another. This is accomplished by expanding the landscape in a Taylor series at each value of the order parameter which defines its macrostate phase space. The coefficients in each Taylor polynomial are known exactly from fluctuation formulas, which may be computed by measuring the appropriate moments of extensive variables that fluctuate in this ensemble. Here we derive the expressions necessary to define these coefficients up to arbitrary order. In principle, this enables a single flat-histogram simulation to provide complete thermodynamic information over a broad range of temperatures and chemical potentials. Using this, we also show how to combine a small number of simulations, each performed at different conditions, in a thermodynamically consistent fashion to accurately compute properties at arbitrary temperatures and chemical potentials. This method may significantly increase the computational efficiency of biased grand canonical Monte Carlo simulations, especially for multicomponent mixtures. Although approximate, this approach is amenable to high-throughput and data-intensive investigations where it is preferable to have a large quantity of reasonably accurate simulation data, rather than a smaller amount with a higher accuracy.
机译：我们推出一种方法，用于将多组分系统的自由能景观推出在大规范集合中，从平板直方图蒙特卡罗模拟，从一组温度和化学电位到另一组。这是通过在定义其宏稳定相空间的顺序参数的每个值下扩展泰勒序列中的景观来实现的。每个泰勒多项式中的系数完全来自波动式，这可以通过测量在该集合中波动的广泛变量的适当变量来计算。在这里，我们派生了将这些系数定义为任意顺序所需的表达式。原则上，这使得单个平面直方图模拟能够在广泛的温度和化学势范围内提供完整的热力学信息。使用此，我们还展示了如何将少量模拟组合，每个模拟，每个模拟在不同的条件下，以热力学方式一致的方式，以准确地计算任意温度和化学电位的特性。该方法可以显着提高偏置大规范蒙特卡罗模拟的计算效率，特别是对于多组分混合物。虽然近似，这种方法适用于高吞吐量和数据密集型调查，在那里优选具有大量合理准确的模拟数据，而不是具有更高精度的较小量。

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