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了解化学不平衡的机制。

Learning the mechanisms of chemical disequilibria.

作者信息

Nicholson Schuyler B, Alaghemandi Mohammad, Green Jason R

机构信息

Department of Chemistry, University of Massachusetts Boston, Boston, Massachusetts 02125, USA.

出版信息

J Chem Phys. 2016 Aug 28;145(8):084112. doi: 10.1063/1.4961485.

Abstract

When at equilibrium, large-scale systems obey thermodynamics because they have microscopic configurations that are typical. "Typical" states are a fraction of those possible with the majority of the probability. A more precise definition of typical states underlies the transmission, coding, and compression of information. However, this definition does not apply to natural systems that are transiently away from equilibrium. Here, we introduce a variational measure of typicality and apply it to atomistic simulations of a model for hydrogen oxidation. While a gaseous mixture of hydrogen and oxygen combusts, reactant molecules transform through a variety of ephemeral species en route to the product, water. Out of the exponentially growing number of possible sequences of chemical species, we find that greater than 95% of the probability concentrates in less than 1% of the possible sequences. Overall, these results extend the notion of typicality across the nonequilibrium regime and suggest that typical sequences are a route to learning mechanisms from experimental measurements. They also open up the possibility of constructing ensembles for computing the macroscopic observables of systems out of equilibrium.

摘要

当处于平衡态时,大规模系统遵循热力学,因为它们具有典型的微观构型。“典型”状态是那些具有大部分概率的可能状态中的一部分。典型状态的更精确定义是信息传输、编码和压缩的基础。然而,这个定义不适用于暂时远离平衡的自然系统。在这里,我们引入了一种典型性的变分度量,并将其应用于氢氧化模型的原子模拟。当氢气和氧气的气态混合物燃烧时,反应物分子在通往产物水的过程中通过各种短暂的物种进行转化。在化学物种可能序列呈指数增长的情况下,我们发现超过95%的概率集中在不到1%的可能序列中。总体而言,这些结果将典型性的概念扩展到非平衡态,并表明典型序列是从实验测量中学习机制的一条途径。它们还开辟了构建系综以计算非平衡系统宏观可观测量的可能性。

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