From Static Pathways to Dynamic Mechanisms: A Committor-Based Data-Driven Approach to Chemical Reactions

TL;DR

This paper presents a novel data-driven workflow combining neural networks and hybrid DFT potentials to analyze dynamic chemical reaction mechanisms, revealing new pathways and metastable intermediates with high accuracy.

Contribution

The study introduces a committor-based neural network approach integrated with a hybrid DFT potential to uncover complex reaction pathways and intermediates in chemical reactions.

Findings

Revealed a concerted SNAr mechanism with a lower barrier than static methods.

Discovered three competing pathways in isobutanol isomerization, including previously unreported intermediates.

Achieved barrier heights and intermediates in close agreement with high-level DFT benchmarks.

Abstract

As computational chemistry methods evolve, dynamic effects have been increasingly recognized to govern chemical reaction pathways in both organic and inorganic systems. Here, we introduce a committor-based workflow that integrates a path-committor-consistent artificial neural network (PCCANN) with an iteratively trained hybrid-DFT-level message passing atomic convolutional encoder (MACE) potential. Beginning with a static nudged elastic band path, PCCANN extracts a committor-consistent string to represent the reactive ensemble. We illustrate the power of this methodology through two representative applications. First, we investigate an SNAr reaction using MACE trained at hybrid DFT level with implicit solvent. The mechanism is found to be concerted, and the dynamic approach reveals a lower barrier than static treatments. Second, we apply the same protocol to the isomerization of protonated isobutanol to protonated 2-butanol, yielding a quantitatively accurate free-energy landscape. We uncover three competing channels: the established concerted mechanism and two asynchronous stepwise routes mediated by water and methyl transfer, all with comparable activation barriers. Notably, the stepwise pathways traverse metastable intermediates that, to the best of our knowledge, have not been described in prior mechanistic studies. Calculated barrier heights and intermediate stabilities are in close agreement with high-level DFT benchmarks, demonstrating the framework's accuracy. Together, these studies highlight mechanistic diversity across distinct systems and establish the synergistic PCCANN-MACE protocol as a proof-of-concept approach for committor-based discovery of complex reaction dynamics.