What is chem-ts-optimization?

A chemistry workflow for improving a guessed transition-state structure—the arrangement of atoms at a reaction's energy barrier—and checking that it is a first-order saddle point.

Optimise non-periodic molecular transition-state guesses with Sella, then use vibrational calculations to check for the required imaginary mode.

It helps distinguish a valid reaction transition state from a structure that only looks plausible.

chem-ts-optimization — Skill by learningmatter-mit

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/learningmatter-mit/atomisticskills/chem-ts-optimization

Any agent

npx skills add learningmatter-mit/AtomisticSkills --skill chem-ts-optimization

Clone the repo

git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills

Made for: Claude Code, Codex.

Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 917 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00023	$0.00917
Opus 5	$0.00012	$0.00458
Sonnet 5	$0.00005	$0.00183
Haiku 4.5	$0.00002	$0.00092

Measured 2d ago against content hash 88904f151386, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

chem-ts-optimization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (examples/acetonitrile/run_example.sh, scripts/optimize_ts_sella.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/chem-ts-optimization/SKILL.md · 108 lines

How it starts

The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.

TS Optimization with Sella

Optimize a transition-state guess and check whether it is a first-order saddle point.

Scope

Domain: molecular chemistry only (non-periodic systems).
Trigger: user has a TS guess and needs TS optimization plus frequency validation.
Exclusions: periodic diffusion/path workflows (use chem-neb-barrier instead).

Tool

`optimize_ts_sella.py`

Runs Sella TS optimization followed by finite-difference vibrations.

Use with MACE

# Env: mace-agent
python .agents/skills/chem-ts-optimization/scripts/optimize_ts_sella.py \
  --ts_guess ts_guess.xyz \
  --model_type mace \
  --model_name MACE-OFF23-small \
  --fmax 0.02 \
  --steps 500 \
  --imag_cutoff_cm1 -50.0 \
  --output_dir results/ts_opt

Use with FAIRChem (UMA)

# Env: fairchem-agent
python .agents/skills/chem-ts-optimization/scripts/optimize_ts_sella.py \
  --ts_guess ts_guess.xyz \
  --model_type fairchem \
  --model_name uma-s-1p1 \
  --task_name omol \
  --fmax 0.02 \
  --steps 500 \
  --imag_cutoff_cm1 -50.0 \
  --output_dir results/ts_opt

Arguments

--ts_guess: required TS guess geometry (XYZ supported by ASE I/O).
--model_type: required backend (mace or fairchem).
--model_name: optional model identifier/checkpoint.
--task_name: optional model head/task (for UMA molecular runs use omol).
--device: auto|cpu|cuda (default auto).
--fmax: Sella convergence threshold in eV/A (default 0.02).
--steps: maximum TS optimization steps (default 500).
--vib_delta: finite-difference displacement in A (default 0.01).
--vib_nfree: finite-difference stencil size (2 or 4, default 2).
--imag_cutoff_cm1: imaginary mode cutoff in cm^-1 (default -50.0).
--keep_vib_cache: optional flag to keep vibration cache files in output_dir/vib.
--output_dir: required output directory.

Outputs

ts_optimized.xyz: optimized TS geometry.
ts_opt.traj: TS optimization trajectory.
ts_opt.log: optimizer log.
ts_optimization_results.json: run summary and pass/fail decision.
vib/ cache files only when --keep_vib_cache is set.

Read the full file on GitHub · 108 lines

Files

What ships with it

9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2d ago First seen · 108 lines · 23 tokens per session scan A 88904f151386

Subscribe to this mod's changes

chem-ts-optimization is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (154 stars, last pushed 7d ago), licensed MIT. It adds 23 tokens to every session and 917 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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chem-ts-optimization