Fast exploration of chemical reaction networks

TL;DR

This paper introduces a fast, computationally inexpensive method to identify key reaction pathways in complex chemical networks, enabling more efficient high-level calculations by focusing on the most important reactions.

Contribution

The authors propose an innovative acceleration-detection approach that rapidly estimates pathway importance without prior knowledge of products or transition states.

Findings

Method successfully identified key pathways in three different systems.

Speeded up reactivity simulations of uni- and bimolecular reactions.

Validated approach within statistical error across multiple systems.

Abstract

A variety of natural phenomena comprises a huge number of competing reactions and short-lived intermediates. Any study of such processes requires the discovery and accurate modeling of their underlying reaction network. However, this task is challenging due to the complexity in exploring all the possible pathways and the high computational cost in accurately modeling a large number of reactions. Fortunately, very often these processes are dominated by only a limited subset of the network's reaction pathways. In this work we propose a novel computationally inexpensive method to identify and select the key pathways of complex reaction networks, so that high-level ab-initio calculations can be more efficiently targeted at these critical reactions. The method estimates the relative importance of the reaction pathways for given reactants by analyzing the accelerated evolution of hundreds of replicas of the system and detecting products formation. This acceleration-detection method is able to tremendously speed up the reactivity of uni- and bimolecular reactions, without requiring any previous knowledge of products or transition states. Importantly, the method is efficiently iterative, as it can be straightforwardly applied for the most frequently observed products, therefore providing an efficient algorithm to identify the key reactions of extended chemical networks. We verified the validity of our approach on three different systems, including the reactivity of t-decalin with a methyl radical, and in all cases the expected behavior was recovered within statistical error.