Skip to content
Agentmart
Submit a tool

chem-ts-optimization

Optimize non-periodic molecular TS guesses and verify first-order saddle point from vibrational modes.

Skill by learningmatter-mit

IndexedMIT★ 183
Write a review

Chalk Labs

Need help deploying chem-ts-optimization?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/learningmatter-mit-chem-ts-optimization

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

Optimize non-periodic molecular TS guesses and verify first-order saddle point from vibrational modes.

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found
Runs as
Python package

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/chem-ts-optimization

What's inside

Skill name
chem-ts-optimization
Description
Optimize non-periodic molecular TS guesses and verify first-order saddle point from vibrational modes.
License
MIT
Instructions
101 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (10)

  • SKILL.md
  • README.md · examples/acetonitrile/
  • ts_opt.traj · examples/acetonitrile/output/
  • ts_optimization_results.json · examples/acetonitrile/output/
  • ts_optimized.xyz · examples/acetonitrile/output/
  • product_ch3nc.xyz · examples/acetonitrile/
  • reactant_ch3cn.xyz · examples/acetonitrile/
  • run_example.sh · examples/acetonitrile/
  • ts_guess.xyz · examples/acetonitrile/
  • optimize_ts_sella.py · scripts/

SKILL.md (first lines)

# 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

```bash
${CLAUDE_SKILL_DIR}/../../venv/run mlip python ${CLAUDE_SKILL_DIR}/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)

```bash
${CLAUDE_SKILL_DIR}/../../venv/run fairchem python ${CLAUDE_SKILL_DIR}/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.

`ts_optimization_results.json` fields include:
- run/model metadata
- convergence (`sella_converged`, `optimization_steps`, `max_force_eV_per_A`)
- vibrational data (`all_frequencies_cm1`, `imaginary_modes`)
- classification (`n_imag_below_cutoff`, `is_first_order_saddle`)

## TS Pass Criterion

A structure is accepted as first-order saddle only if:
- exactly one frequency satisfies `frequency < imag_cutoff_cm1`

Default criterion: exactly one mode below `-50 cm^-1`.

## Model Guidance

- Recommended for molecules:
  - `MACE-OFF23-small` / `MACE-OFF23-medium`
  - `uma-s-1p1` with `--task_name omol`
- Use the same backend/model/head across reactant/product/TS optimization and TS validation.

## Prerequisites And Constraints

- Run in `mlip` (MACE) or `fairchem` (FairChem) depending on backend (`venv/run <venv> ...`).
- Script enforces `pbc=False` (non-periodic only).
- TS guess quality matters; poor guesses can converge to minima or higher-order saddles.

## Examples

See `examples/` directory for sample inputs and outputs.
---

**Author:** Juno Nam
**Contact:** [GitHub @recisic](https://github.com/recisic)

From learningmatter-mit/AtomisticSkills, MIT.

More from learningmatter-mit/AtomisticSkills

Signals

Stars
183
Forks
31
Open issues
0
Last commit (this folder)
Oct 5, 2026 (27 days ago)
Last release
Oct 6, 2026 (26 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from learningmatter-mit

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get chem-ts-optimization running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying chem-ts-optimization?

CallGet help
Cite this page

chem-ts-optimization skill. Agent Mart. https://agentmart.dev/skills/learningmatter-mit-chem-ts-optimization (last updated 2026-10-24)