A fast algorithm is developed for ranking the species in a chemistry set according to their importance to the modeled densities of user-specified species of interest. The species ranking can be constructed for any set of user-specified plasma conditions, but here we focus predominantly on low-temperature plasmas, with gas temperatures between 300 and 1500K covering the typical range of ICP and CCP plasma sources. This ranking scheme can be used to acquire insight into complex chemistry sets for modeling plasma phenomena or for a species-oriented reduction of the given chemistry set. The species-ranking method presented is based on a graph-theoretical representation of the detailed chemistry set and establishing indirect asymmetric coupling coefficients between pairs of species by the means of widely used graph search algorithms. Several alternative species-ranking schemes are proposed, all building on the theory behind different flavors of the directed relation graph method. The best-performing ranking method is identified statistically, by performing and evaluating a species-oriented iterative skeletal reduction on six, previously available, test chemistry sets (including O₂–He and N₂–H₂) with varying plasma conditions. The species-ranking method presented leads to reductions of between 10 and 75% in the number of species compared to the original detailed chemistry set, depending on the specific test chemistry set and plasma conditions.

开发了一种快速算法，可根据物种对用户指定的关注物种的建模密度的重要性对化学组中的物种进行排名。可以针对任何一组用户指定的等离子体条件构建物种等级，但是这里我们主要关注低温等离子体，气体温度在300至1500K之间，涵盖了ICP和CCP等离子体源的典型范围。该排序方案可用于获取对复杂化学集合的见解，以对等离子体现象进行建模或对给定化学集合进行面向物种的还原。提出的物种排名方法基于详细化学组的图论表示，并通过广泛使用的图搜索算法建立物种对之间的间接非对称耦合系数。提出了几种替代的物种排序方案，所有方案均基于有向关系图方法的不同形式背后的理论。通过对六个先前可用的测试化学组（包括O）进行和评估面向物种的迭代骨骼减少，从统计学上确定性能最佳的排序方法₂ -He和N ₂ -H ₂）随等离子体条件的变化而变化。根据特定的测试化学组和血浆条件，与原始的详细化学组相比，提出的物种分级方法可将物种数量减少10％至75％。