Learning Chemistry of Complex Reaction Systems via a Python First-Principles Reaction Rule Stencil (pReSt) Generator

Complex
reaction networks can be generated with automated network
generators from initial reactants and reaction rules. Reaction rule
specification is central to network generation. These reaction rules
are, at present, user-defined based on (intuitive) expert knowledge
of chemistry and are often transferred from gas-phase to surface processes.
The catalyst active site geometry is usually left out but is often
responsible for selectivity. We propose a first-principles-based reaction
mechanism generation framework using density functional theory (DFT)
data of published reaction mechanisms. The framework “learns
the chemistry” from published mechanisms. It can generate reaction
networks not studied before, “flag” reactions not seen
before for further DFT convergence tests, and easily reconcile differences
between catalysts and reactants that may introduce new pathways never
seen before. As such, it can be a diagnostic tool for data (mechanism)
quality assessment and novel pathway discovery to new molecules. A
software, the Python Reaction Stencil (pReSt), was developed for this
purpose to wrap around automatic mechanism generation software. Multiple
catalytic chemistries are considered to show the efficacy of the proposed
framework.