Halving Computational Cost: ChemOntology Encodes Human Intuition to Accelerate Reaction Path Searches

Background and Motivation

Reaction‑mechanism analysis requires locating local minima and intermediates on the potential‑energy surface (PES). Conventional intrinsic reaction coordinate (IRC) methods rely on pre‑defined paths and can miss unconventional channels. Automated methods such as Artificial Force‑Induced Reaction (AFIR) generate many configurations, but the large number of energy evaluations makes the approach computationally expensive.

Chemical Ontology as a Knowledge Structure

Chemical ontologies encode entities, attributes, relationships, and rules in a machine‑readable format. Existing ontologies (e.g., RXNO) have shown value for annotating reaction pathways. By formalizing this knowledge, ChemOntology reduces reliance on massive training datasets and enables rule‑based reasoning.

ChemOntology Framework

The ChemOntology workflow consists of six steps:

User inputs : specification of reaction components.

Process chemical information : PubChem provides standardized structures, names, and identifiers for each component.

Construct reaction paths using ERPOs : Elementary Reaction Pathway Operators (ERPOs) describe modular elementary steps such as coordination, oxidative addition, alkene insertion, and β‑hydrogen elimination.

Hybridization filter : a hybridization‑based filter discards structures that exceed allowed geometric changes.

Run & control AFIR : AFIR serves as the quantum‑chemical engine, with GFN2‑xTB providing semi‑empirical geometry evolution.

Analysis of reaction nodes & paths : generated nodes are evaluated for chemical plausibility and organized into reaction networks.

Experimental Validation on the Heck Reaction

The classic Heck reaction (iodobenzene + styrene, Pd catalyst, triphenylphosphine ligand, triethylamine base) proceeds through oxidative addition, alkene insertion, migratory insertion, β‑hydrogen elimination, and base‑mediated steps, yielding trans‑stilbene as the major product.

Comparison of Search Strategies

Three parallel AFIR‑based strategies were evaluated:

AFIR_DEFAULT : unguided exhaustive search.

AFIR_TARGET : partial constraints on reaction centers.

AFIR_ChemOntology : automatic identification of chemical roles and dynamic guidance via ERPOs.

AFIR_DEFAULT generated many chemically irrelevant nodes. AFIR_TARGET reduced redundancy but still produced many extraneous structures. AFIR_ChemOntology quickly highlighted the main reaction channel, focusing computation on chemically reasonable paths.

Energy Profile and Path Discrimination

Energy analysis showed that only AFIR_ChemOntology could fully distinguish pathways leading to the major product from those leading to minor side products. The β‑hydrogen elimination step exhibited strong interactions in productive pathways and weaker stability in side‑product routes, explaining their lower probability.

Computational Efficiency

AFIR_ChemOntology achieved comparable effective results to the full AFIR_TARGET search while exploring only about half the number of paths, reducing overall computational cost by nearly 50 %.

Broader Impact

Embedding chemical ontology into automated path searches provides a scalable, data‑light methodology that bridges theoretical chemistry and practical applications, enabling more predictive and efficient catalyst design and green synthesis development.

Reference: ChemOntology: A Reusable Explicit Chemical Ontology‑Based Method to Expedite Reaction Path Searches, ACS Catalysis. DOI: 10.1021/acscatal.5c06298