Neb Irc Activation Energy

NEB-IRC activation energy pipeline

Overview

Computes a reaction activation energy end to end — optimize reactant and product, find the minimum energy path with climbing-image NEB, refine the transition state with a Hessian, and confirm it with IRC — using GFN2-xTB through pysisyphus. Outputs a verified TS geometry, the barrier (ΔE‡; ΔG‡ after thermal corrections), an IRC energy profile (irc_energy_profile.png), and an animated TS imaginary-mode viewer (ts_imaginary_mode.html). Verification is required, not optional: a converged TS is meaningless until its single imaginary mode and IRC endpoints are checked.

When to Use

• Finding the transition state for an elementary reaction step and its activation energy
• Computing a reaction barrier (ΔE‡ or ΔG‡) to rank a series of related reactions
• Running a climbing-image NEB / minimum energy path between a reactant and product
• Verifying a candidate TS with IRC — does it connect the intended reactant and product?
• Screening barriers at a cheap semi-empirical level before committing DFT time

Reach for DFT on a multi-core node instead when you need quantitative agreement with experiment; GFN2-xTB barriers are semi-quantitative (see references/energetics.md). For a 2D scheme figure of the reaction, use the rdkit-chemdraw-cdxml skill instead.

Prerequisites

• Tools: xtb (GFN2-xTB engine), pysisyphus (pysis CLI for the path pipeline)
• Python: matplotlib for the IRC plot (the TS animation HTML needs no packages)
• Input: reactant.xyz and product.xyz with identical atom ordering (see Step 1)
• Environment: single core suffices; set OMP_NUM_THREADS to match physical cores

Work in a local scratch dir (e.g. /tmp/rxn/), not a mounted/networked workspace: pysisyphus creates and deletes symlinks and throws PermissionError mid-run on s3fs/FUSE. Copy results out at the end.

Materialize the bundled scripts into the scratch dir first. They can't be run in place from the skill directory, so use your file tools to read each one and save it into your working dir before running it. The scripts live in this skill's scripts/ folder (next to this SKILL.md):

• scripts/setup_env.sh
• scripts/pipeline.yaml
• scripts/check_result.py
• scripts/plot_irc.py

The TS imaginary-mode animation is not produced here — read the molecular-visualization-3dmol skill and use its mol_viewer.py (Step 6).

Check for the tools; install only if missing (inside pixi/conda, invoke via pixi run xtb):

cd /tmp/rxn
command -v xtb && command -v pysis || bash setup_env.sh   # xtb binary + pysisyphus, ~2-3 min
source "${ROOT:-${HOME:-/tmp}/xtbenv}/env.sh"              # re-source in every new shell

Workflow

Step 1: Prepare reactant and product geometries

Most failures originate here, not in the NEB. Build the product by editing a copy of the reactant so atom ordering is identical — a permuted order gives a path that is geometrically valid and chemically meaningless. Align non-reacting groups so a spectator conformational change does not fold into the barrier. For bimolecular reactions use a pre-reaction complex as the reactant, not separated fragments (NEB converges poorly from infinite separation, and the reference state changes the reported barrier — record it).

# Build product from a copy of the reactant, moving only the reacting atoms.
from pathlib import Path

lines = Path("reactant.xyz").read_text().splitlines()
natoms = int(lines[0])
atoms = [ln.split() for ln in lines[2:2 + natoms]]   # [symbol, x, y, z] per atom
# ... edit ONLY the coordinates of atoms that move; keep order + symbols ...
Path("product.xyz").write_text("\n".join([str(natoms), "product"] + [" ".join(a) for a in atoms]) + "\n")

Step 2: Check the endpoints sit in different basins

If the reacting groups start too close, preoptimization carries the reactant over the barrier and both endpoints relax to the same structure; the NEB then returns a flat profile and the TS search aborts. This looks like success in the log until it fails minutes later, so compare the two pre-optimized endpoints on the key reacting bond.

import numpy as np

def load_xyz(fn):
    lines = open(fn).read().splitlines()
    return np.array([[float(v) for v in ln.split()[1:4]] for ln in lines[2:2 + int(lines[0])]])

r, p = load_xyz("first_pre_opt.xyz"), load_xyz("last_pre_opt.xyz")   # written by preopt
i, j = 0, 5                                     # indices of the atoms whose bond changes
dr, dp = np.linalg.norm(r[i] - r[j]), np.linalg.norm(p[i] - p[j])
assert abs(dr - dp) > 0.3, "endpoints nearly identical: move the reacting fragment further out"

Step 3: Run the pipeline

The template chains preopt → IDPP interpolation → CI-NEB → RS-I-RFO TS optimization with Hessian → IRC both directions → endpoint reoptimization. Set charge and mult in pipeline.yaml before running — the default charge: 0 is wrong for any ion and converges silently to a meaningless TS. Add alpb: <solvent> for solution reactions. Whatever you set here must match every standalone xtb call in Step 5. Raise max_cycles to 150–200 above ~50 atoms.

pysis pipeline.yaml > pipeline.log 2>&1
tail -40 pipeline.log            # confirm it reached the endopt/IRC stage

ΔE‡ and the reaction energy come from the pipeline's | BARRIERS | block, referenced to the reactant endpoint (check_result.py in Step 4 reports the same numbers):

  Left:     0.00 kJ mol-1      # reactant endpoint
    TS:   107.84 kJ mol-1      # dE‡ = TS - Left = 107.84 kJ/mol
 Right:    10.19 kJ mol-1      # dE_rxn = Right - Left = 10.19 kJ/mol

Step 4: Verify the transition state

Never report a barrier before this passes. The checker applies all three gates (see Key Concepts) and exits 0 only if every one passes.

python3 check_result.py pipeline.log
# [PASS] imaginary frequencies  exactly 1 at -621.8 cm-1
# [PASS] IRC endpoints          forward and backward matched distinct inputs
# [PASS] NEB profile            elementary, single barrier, span 107.5 kJ/mol
#   electronic barrier dE‡ (GFN2-xTB): 107.8 kJ/mol = 25.77 kcal/mol

Step 5: Compute thermochemistry and report the barrier

Report ΔE‡ and the level of theory; for comparison against experimental rates, report ΔG‡, which needs Hessians on the TS and reactant. Every standalone xtb call must use the same --chrg, --uhf, and solvent as pipeline.yaml, on the pipeline's optimized geometries — not the raw input. A gas-phase Hessian against a solvated barrier, or a missing --chrg, yields a nonsensical (often negative) ΔG‡.

# flags must match pipeline.yaml (here: charge -1, aqueous)
xtb forward_end_final_geometry.xyz --hess --gfn 2 --alpb water --chrg -1 --uhf 0 > r_hess.log 2>&1
xtb ts_final_geometry.xyz          --hess --gfn 2 --alpb water --chrg -1 --uhf 0 > ts_hess.log 2>&1
# dG‡ = G(TS) - G(reactant), from the "TOTAL FREE ENERGY" lines
grep -i "TOTAL FREE ENERGY" r_hess.log ts_hess.log

Step 6: Deliver the two visuals

IRC energy profile — plot_irc.py reads gfn/charge/mult/solvent from pipeline.yaml and recomputes each IRC frame's energy at that exact level (the *_irc.trj comment lines carry none). Override with --charge/--mult/--alpb/--gfn only if you edited the pipeline after running.

python3 plot_irc.py               # -> irc_energy_profile.png

TS imaginary-mode animation — produced by the molecular-visualization-3dmol skill, not here. Read that skill and run its mol_viewer.py on the ts_imaginary_mode_000.trj that tsopt: do_hess: True wrote (grab the frequency from check_result.py for the label):

# after materializing mol_viewer.py per the molecular-visualization-3dmol skill
python3 mol_viewer.py ts_imaginary_mode_000.trj --mode trajectory \
    --title "Transition-state mode" --subtitle "imaginary mode -621.8 cm-1" --out ts_imaginary_mode.html

Key Parameters

Set in pipeline.yaml unless noted.

Key Concepts

The three verification gates and what a failure means:

Barrier definitions differ by reference and corrections: ΔE‡ (electronic), ΔE‡+ZPE, ΔH‡, ΔG‡ (for kinetics). For bimolecular reactions the reference state (separated reactants vs pre-reaction complex) shifts the number — record which was used. Full table in references/energetics.md.

Submerged barriers. For an ion + neutral in the gas phase (e.g. anionic SN2) the TS can sit below the separated reactants — a negative barrier against separated reactants is real, not a bug. Reference to the pre-reaction complex (the pipeline's endpoint) and/or add alpb solvation, which lifts it to a positive, experiment-comparable value.

Common Recipes

Recipe: Split a multi-step reaction into elementary steps

When to use: check_result.py reports multiple NEB maxima — it names the intermediate image index(es). Extract that image from the NEB path and run the pipeline on each half.

from pathlib import Path

N = 5   # interior-minimum image index reported by check_result.py's NEB profile gate
lines = Path("final_geometries.trj").read_text().splitlines()   # one frame per NEB image
n = int(lines[0].split()[0])
Path("intermediate.xyz").write_text("\n".join(lines[N * (n + 2):(N + 1) * (n + 2)]) + "\n")
# then: pipeline on reactant.xyz + intermediate.xyz, and intermediate.xyz + product.xyz

Recipe: Refine the electronic energy with a DFT single point

When to use: the xTB geometry is fine but you need a better barrier. Keep the xTB geometry and thermal corrections; replace only the electronic energy on 3 structures (≤30 atoms).

from pyscf import gto, dft

def electronic_energy(xyz_fn, basis="def2-svp", xc="b3lyp"):
    mol = gto.M(atom=xyz_fn, basis=basis)     # xyz file path accepted directly
    mf = dft.RKS(mol); mf.xc = xc
    return mf.kernel()                          # Hartree

dE = (electronic_energy("ts_final_geometry.xyz") - electronic_energy("reactant.xyz")) * 2625.4996
print(f"DFT dE‡ = {dE:.1f} kJ/mol (add xTB G_corr for dG‡)")

Expected Outputs

• pipeline.log — full run log; parsed by check_result.py
• ts_final_geometry.xyz — the optimized transition state
• final_geometries.trj — the converged NEB path (per-image energies in comment lines)
• ts_imaginary_mode_000.trj — TS displaced along the imaginary mode (input to the animation)
• irc_energy_profile.png — from plot_irc.py; ts_imaginary_mode.html — from the molecular-visualization-3dmol skill (Step 6)
• A barrier: ΔE‡ from the BARRIERS block, ΔG‡ after Hessian thermal corrections

Troubleshooting

Bundled Resources

• scripts/setup_env.sh — installs xtb (GitHub release) + pysisyphus (PyPI), writes env.sh
• scripts/pipeline.yaml — full preopt→NEB→TSopt→IRC→endopt template with inline comments
• scripts/check_result.py — verification of the three gates (exit 0 = all pass); prints ΔE‡
• scripts/plot_irc.py — builds irc_energy_profile.png (recomputes IRC-frame energies at the pipeline level)
• references/feasibility.md — measured timings, atom-count sizing, what DFT can/can't do here
• references/energetics.md — ΔE‡/ΔH‡/ΔG‡ definitions, thermochemistry, reporting conventions

Related Skills

• molecular-visualization-3dmol — supplies mol_viewer.py, which renders the TS imaginary-mode animation and can play back the IRC/NEB path; materialize it alongside this skill's scripts
• rdkit-chemdraw-cdxml — draw the reaction as a 2D scheme figure

References

• xtb documentation — GFN2-xTB methods and CLI
• pysisyphus documentation — COS/NEB, TS optimizers, IRC
• Bannwarth, Ehlert, Grimme, J. Chem. Theory Comput. 2019, 15, 1652 — GFN2-xTB method paper
• Steinmetzer, Kupfer, Gräfe, Int. J. Quantum Chem. 2021, 121, e26550 — pysisyphus paper
• ASE / PySCF — geometry I/O and DFT single points