Transition State Search

ARC can automatically search for and validate transition states (TSs) for a wide range of reaction types, from fast heuristic builders to machine-learning and reaction-path approaches. This section describes the currently supported TS-search methods and how to use them through ARC input files.

All methods are registered as TS adapters and configured via the ts_adapters list in the ARC input file. ARC tries each adapter in order and collects all resulting TS guesses for downstream optimization and validation (energy, frequency, and IRC).

ARC-native methods

These methods are implemented directly inside ARC and do not require any external package beyond the standard ARC environment.

Heuristics adapter

ARC includes an internal TS-guess adapter named heuristics. The implementation lives in arc/job/adapters/ts/heuristics.py and runs incore; it generates candidate TS geometries directly from the mapped reactant and product wells and the RMG reaction-family template rather than submitting a separate external TS-search program. It does not perform any electronic-structure calculation; TS construction is purely geometric.

The current heuristic adapter supports:

• H_Abstraction reactions

• carbonyl_based_hydrolysis reactions

• ether_hydrolysis reactions

• nitrile_hydrolysis reactions

Use it by listing heuristics under ts_adapters:

ts_adapters:
  - heuristics

How the heuristic TS adapter fits into an ARC run.

Hydrogen abstraction heuristic TS search

For RMG H_Abstraction reactions, the heuristic adapter constructs TS guesses for reactions of the form:

R1-H + R2 <=> R1 + R2-H

The algorithm identifies the transferred hydrogen and the two reacting centers from the mapped reaction, places the abstracted hydrogen between the donor and acceptor heavy atoms at Pauling partial-bond distances, combines reactant/product geometries, stretches the forming and breaking H bonds, and scans the approach dihedral at a configurable increment to generate multiple rotamer guesses. The dihedral_increment keyword controls the rotational scan resolution (default 30°; smaller values yield more guesses). Duplicate and colliding geometries are filtered before ARC stores the remaining guesses as TSGuess(method='Heuristics') entries.

At minimum, the reaction must have:

• A recognized H_Abstraction family assignment.

• 3D coordinates for all reactant and product wells.

• Atom mapping between reactants and products, including the transferred H.

• heuristics enabled in ts_adapters.

Example input pattern:

project: h_abstraction_example

ts_adapters:
  - heuristics

species:
  - label: methane
    smiles: C
  - label: OH
    smiles: "[OH]"
  - label: methyl
    smiles: "[CH3]"
  - label: water
    smiles: O

reactions:
  - label: methane + OH <=> methyl + water
    reactants:
      - methane
      - OH
    products:
      - methyl
      - water

ARC then uses the heuristic guesses as normal TS candidates: it optimizes them, checks for a single meaningful imaginary frequency, optionally runs IRCs, and uses the successful TS in kinetics processing.

Neutral hydrolysis TS search

ARC supports automated TS generation and validation for neutral hydrolysis reactions. This capability is designed to start from a high-level reaction definition (e.g., SMILES-defined reactants/products) and proceed through TS generation, optimization, and validation without requiring manual TS construction.

Supported sub-families

The current implementation supports the following neutral hydrolysis sub-families:

• Ester hydrolysis

• Amide hydrolysis

• Acyl halide hydrolysis

• Ether hydrolysis

• Nitrile hydrolysis

How it is used

To run neutral hydrolysis TS search, define the reacting species and the overall reaction in the input file (see ARC’s examples folder for an input file that executes a neutral hydrolysis TS search). At minimum, specify:

• The participating species (e.g., SMILES, xyz, InChI, or adjacency list)

• A reaction string connecting the species labels (e.g., A + H2O <=> products)

• The TS generation adapter(s) under ts_adapters (in this case, use: ['heuristics'])

• The electronic structure levels used for optimization/validation (e.g., opt_level, freq_level, irc_level)

What ARC does

For neutral hydrolysis reactions, ARC performs the following general steps:

1. Identify the relevant reactive atoms based on the reaction family definition.

2. Generate one or more chemically reasonable TS guesses for the hydrolysis transformation.

3. Optimize the TS candidates that pass internal filtration.

4. Validate the TS using vibrational frequency and IRC calculations.

Linear interpolation adapter

The linear adapter is an in-core adapter that generates TS guess geometries by interpolating internal coordinates (Z-matrices) between reactant and product. It handles both isomerization (A ⇌ B) and addition/dissociation (A ⇌ B + C) reactions, covering many families that heuristics does not support.

For isomerization reactions, a strategy pipeline is executed for each reaction path identified from the RMG template:

Strategy	Description
Ring scission	Folds the reactant chain into a ring, then stretches breaking bonds. Used for ring-opening reactions discovered in reverse.
Direct contraction	Moves a terminal group toward its forming-bond partner. Useful for radical ring-closure reactions (e.g., Intra_R_Add_Exocyclic).
Ring closure	Rotates backbone torsions to close a forming bond into a ring.
Z-matrix interpolation	The core method. Builds two Z-matrix chimeras (Type R from the reactant topology, Type P from the product topology), blends them at the interpolation weight, and converts back to Cartesian coordinates. Only coordinates referencing reactive atoms are interpolated; spectator coordinates are kept from the source geometry.
3-center shift	Repositions a migrating atom (e.g., halogen, sulfur) between its donor and acceptor for 1,2-shift reactions.

For addition/dissociation reactions, the adapter starts from the unimolecular species and:

1. Identifies which bonds to cut using the RMG template or combinatorial fragmentation.

2. Stretches the fragments apart to Pauling TS-estimate distances.

3. Migrates atoms (typically H) between fragments when the product composition requires it.

4. For concerted multi-bond eliminations (e.g., XY_elimination producing C=C + H₂ + CO₂), a concerted builder simultaneously stretches breaking bonds and contracts forming bonds.

Dedicated family builders:

• XY elimination hydroxyl — builds a 6-membered ring TS by folding the molecule through three dihedral rotations, then setting element-specific Pauling distances (H–H short, H–O shorter than H–C, C–C long).

Post-processing: every guess goes through family-specific post-processing (forming-bond triangulation for H-transfer, donor H staggering, umbrella inversion for migrating groups, reactive-bond distance adjustment, H orientation correction) and validation (collision detection, detached-atom checks, fragment counting, backbone drift, family-specific motif filters).

Set ts_adapters: ['heuristics', 'linear'] to run both native adapters. The linear adapter is complementary to heuristics.

External-package methods

These methods rely on external packages that must be installed separately. See Installation for setup instructions.

AutoTST ('autotst')

Uses the AutoTST package to generate TS guesses from RMG reaction templates. AutoTST performs systematic conformer searches of the TS using distance-geometry embedding and RDKit force-field optimization, guided by the reaction family template distances.

Runs as a subprocess. Requires the autotst conda environment.

KinBot ('kinbot')

Uses the KinBot package, which performs automated reaction discovery and TS search using semiempirical or DFT methods. KinBot explores the potential energy surface starting from a given species and locates TS geometries for elementary reactions.

Runs as a subprocess. Requires the kinbot_env conda environment (created by devtools/install_kinbot.sh).

TS-GCN ('gcn')

Uses a graph-convolutional neural network (TS-GCN) trained on DFT-optimized TS geometries to predict 3D TS structures directly from the reactant and product graphs. This is the fastest external method but is limited to the atom types and reaction classes in its training data.

Runs as a subprocess. Requires the ts_gcn conda environment.

xTB-GSM ('xtb_gsm')

Uses the Growing String Method (GSM) with the GFN2-xTB semiempirical method to locate approximate TS geometries along the minimum-energy path between reactant and product. This is a reaction-path method rather than a guess-based method, so it tends to produce higher-quality initial TS geometries at the cost of longer compute time.

Runs as a subprocess. Requires xtb and gsm executables.

ORCA NEB ('orca_neb')

Uses ORCA’s nudged elastic band (NEB) implementation to find the minimum-energy path and locate the TS as the highest-energy image. This is a DFT-level reaction-path method and produces high-quality TS geometries, but is significantly more expensive than the heuristic methods.

Requires a configured ORCA installation and server access.

General workflow

Regardless of which adapter(s) are used, ARC follows the same general workflow for each reaction:

1. TS guess generation — each adapter produces one or more candidate TS geometries.

2. Clustering — near-duplicate guesses are removed.

3. Optimization — each surviving guess is optimized at the specified level of theory.

4. Validation — frequency analysis confirms exactly one meaningful imaginary frequency, and IRC calculations verify that the TS connects the correct reactant and product wells.

Multiple adapters can be combined (e.g., ts_adapters: ['heuristics', 'linear', 'gcn', 'kinbot']) to maximize coverage across reaction families.

Outputs and validation

Validated TS results are reported in the project output (log files and generated artifacts), together with the supporting calculations (optimization, frequency, and IRC). ARC does not require TS geometries to be isomorphic with a stored 2D adjacency list, since a TS does not have a single strict graph representation. Instead, TS validation relies on TS-specific checks such as the imaginary frequency, normal mode displacement analysis, IRC results, and energetic consistency.

References

[1] C. Pieters, A. Grinberg Dana, “Learning Rates: Predicting Rate Coefficients for Hydrogen Abstraction Reactions”, Digital Discovery 2026.

[2] L. Fahoum, A. Grinberg Dana, “Automated reaction transition state search for bimolecular liquid-phase reactions using internal coordinates: a test case for neutral hydrolysis”, Digital Discovery 2026, 5, 1372-1387, DOI: 10.1039/D5DD00506J.