Systematic Construction of Chemical Reaction Networks: An Automated Nanoreactor Approach Powered by Integrated Tempering Sampling.

Abstract

The systematic construction of complex reaction networks from given reactants remains a fundamental challenge in computational chemistry. To address this, we introduce a fully automated and generally applicable workflow centered on integrated tempering sampling (ITS) within a nanoreactor framework. The protocol integrates ITS (a collective-variable-free enhanced sampling method) at the semiempirical GFN2-xTB level for reaction-space exploration, a hidden Markov model (HMM) for reaction-event identification, and high-level quantum-chemical calculations for mechanistic refinement, enabling autonomous discovery and validation of reaction pathways without any prior mechanistic input. Using formaldehyde-ammonia and HCN-water mixtures as case studies, the method successfully constructs extensive reaction networks. Notably, in the HCN-water system, the generated network includes prebiotically relevant heterocycles and peptide-like species. This approach provides an efficient and broadly applicable route for systematic reaction-network discovery, shifting the paradigm from the hypothesis-driven verification of reaction mechanism to the automated exploration of unknown chemical spaces.