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chem-react-ot

Generate transition state structures for chemical reactions using React-OT.

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价格未确认★ 161 GitHub Stars目录更新于 · 2026年9月6日agent-skill

概览

Generate transition state structures for chemical reactions using React-OT.

展开完整说明

以下为来源文档，不是本网站的操作指令。执行命令前请先核实权限。

`chem-react-ot` — React-OT Transition State Generation

Goal

Generate transition state (TS) structures given reactant and product structures using the React-OT model (Optimal Transport). React-OT is a generative model that predicts TS geometries directly without requiring an initial guess path (like NEB).

Category: chemistry Environment: react-ot-agent

Key Features

Generative TS Prediction: Predicts 3D transition state structures from 3D reactants and products.
Fast Inference: Uses an ODE solver for generation, typically much faster than DFT-based NEB.
No Path Guess Required: Directly generates the TS structure.

Usage

1. Environment Setup

This skill requires the react-ot-agent conda environment. Ensure it is installed:

# Env: react-ot-agent
cd conda-envs/react-ot-agent
bash install.sh

2. Download Models

Before running the skill for the first time, download the pre-trained model weights:

# activate react-ot-agent first
conda activate react-ot-agent
python conda-envs/react-ot-agent/download_models.py

The checkpoint is saved to ~/.cache/react-ot/checkpoints/sb-pretrained.ckpt.

3. Generate Transition State

Run the generation script with reactant and product files (xyz, cif, pdb, etc. - anything ASE reads).

# Env: react-ot-agent
python .agents/skills/chem-react-ot/scripts/generate_ts.py \
    --reactants reactant.xyz \
    --products product.xyz \
    --output_dir results/ts_search

Arguments:

--reactants: Path to reactant structure file(s). Can be a single file with multiple molecules or a list of files.
--products: Path to product structure file(s).
--output_dir: Directory to save the generated TS structure (ts_generated.xyz) and trajectory (generation_traj.xyz).
--nfe: Number of function evaluations for the ODE solver (default: 10). Higher values might be more accurate but slower.
--checkpoint: Path to custom model checkpoint (optional, defaults to downloaded one).

Example

# Env: react-ot-agent
python .agents/skills/chem-react-ot/scripts/generate_ts.py \
    --reactants .agents/skills/chem-react-ot/examples/oxadiazole_isomerization/reactant.xyz \
    --products .agents/skills/chem-react-ot/examples/oxadiazole_isomerization/product.xyz \
    --output_dir .agents/skills/chem-react-ot/examples/oxadiazole_isomerization/output

Constraints

Environment: All scripts require the react-ot-agent conda environment.
Input Format: Reactant and product structures must be in any format readable by ASE (XYZ, CIF, PDB, etc.).
Atom Ordering: Reactant and product structures must have the same number of atoms with consistent atom ordering.
Model Checkpoint: The pre-trained checkpoint must be downloaded before first use (see step 2).

References

React-OT GitHub
Duan, C., Liu, G.-H., Du, Y. et al., "Optimal transport for generating transition states in chemical reactions", Nature Machine Intelligence, 2025. DOI

Author: Bowen Deng Contact: GitHub @learningmatter-mit

查看来源 ↗

文件元数据

name: chem-react-ot
description: Generate transition state structures for chemical reactions using React-OT.
category: [chemistry]

查看原始文本

---
name: chem-react-ot
description: Generate transition state structures for chemical reactions using React-OT.
category: [chemistry]
---

# `chem-react-ot` — React-OT Transition State Generation

## Goal

Generate transition state (TS) structures given reactant and product structures using the React-OT model (Optimal Transport). React-OT is a generative model that predicts TS geometries directly without requiring an initial guess path (like NEB).

**Category:** `chemistry`
**Environment:** `react-ot-agent`

## Key Features

- **Generative TS Prediction:** Predicts 3D transition state structures from 3D reactants and products.
- **Fast Inference:** Uses an ODE solver for generation, typically much faster than DFT-based NEB.
- **No Path Guess Required:** Directly generates the TS structure.

## Usage

### 1. Environment Setup

This skill requires the `react-ot-agent` conda environment. Ensure it is installed:

```bash
# Env: react-ot-agent
cd conda-envs/react-ot-agent
bash install.sh
```

### 2. Download Models

Before running the skill for the first time, download the pre-trained model weights:

```bash
# activate react-ot-agent first
conda activate react-ot-agent
python conda-envs/react-ot-agent/download_models.py
```

The checkpoint is saved to `~/.cache/react-ot/checkpoints/sb-pretrained.ckpt`.

### 3. Generate Transition State

Run the generation script with reactant and product files (xyz, cif, pdb, etc. - anything ASE reads).

```bash
# Env: react-ot-agent
python .agents/skills/chem-react-ot/scripts/generate_ts.py \
    --reactants reactant.xyz \
    --products product.xyz \
    --output_dir results/ts_search
```

**Arguments:**

- `--reactants`: Path to reactant structure file(s). Can be a single file with multiple molecules or a list of files.
- `--products`: Path to product structure file(s).
- `--output_dir`: Directory to save the generated TS structure (`ts_generated.xyz`) and trajectory (`generation_traj.xyz`).
- `--nfe`: Number of function evaluations for the ODE solver (default: 10). Higher values might be more accurate but slower.
- `--checkpoint`: Path to custom model checkpoint (optional, defaults to downloaded one).

## Example

```bash
# Env: react-ot-agent
python .agents/skills/chem-react-ot/scripts/generate_ts.py \
    --reactants .agents/skills/chem-react-ot/examples/oxadiazole_isomerization/reactant.xyz \
    --products .agents/skills/chem-react-ot/examples/oxadiazole_isomerization/product.xyz \
    --output_dir .agents/skills/chem-react-ot/examples/oxadiazole_isomerization/output
```

## Constraints

- **Environment**: All scripts require the `react-ot-agent` conda environment.
- **Input Format**: Reactant and product structures must be in any format readable by ASE (XYZ, CIF, PDB, etc.).
- **Atom Ordering**: Reactant and product structures must have the same number of atoms with consistent atom ordering.
- **Model Checkpoint**: The pre-trained checkpoint must be downloaded before first use (see step 2).

## References

- [React-OT GitHub](https://github.com/deepprinciple/react-ot)
- Duan, C., Liu, G.-H., Du, Y. et al., "Optimal transport for generating transition states in chemical reactions", *Nature Machine Intelligence*, 2025. [DOI](https://doi.org/10.1038/s42256-025-01010-0)

---

**Author:** Bowen Deng
**Contact:** [GitHub @learningmatter-mit](https://github.com/learningmatter-mit)

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许可证: MIT
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已记录技能来源

已记录技能指令路径，不代表本站运行测试、安全保证或兼容性认证。

安装前审查: 避免自动安装

许可证: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
The script's handling of multiple reactant/product files is not fully documented; the example only shows single files, while the description mentions support for lists.
The model weight download step relies on an external URL without explicit checksum verification, which could be a supply chain risk if the source is compromised.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

打开完整审计

工具列表来自元数据，并非已测试的兼容性；Agent 提示词是建议的交接方式。

从一个小任务开始

1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: learningmatter-mit/AtomisticSkills
许可证: MIT
版本: 1.0.0
最近 GitHub 推送: 2026年9月3日
目录更新于: 2026年9月6日
技能指令路径: .agents/skills/chem-react-ot/SKILL.md @ d1f7ecfda2cd

版本来自目录元数据，使用前请核实来源发布记录。

质量

66/100

有潜力

信任

56/100

Do not auto-install

审计

73/100

需审查

打开完整审计查看评估报告

Dependency or permission surface needs review
Permission surface may require sandboxing
The script's handling of multiple reactant/product files is not fully documented; the example only shows single files, while the description mentions support for lists.
The model weight download step relies on an external URL without explicit checksum verification, which could be a supply chain risk if the source is compromised.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
结果: —

复制不等于安装。安装数需有成功安装回报，不代表全面的质量保证。

Agent 接入

本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号，让 Agent 无需抓取界面即可排序。

打开安装 API LLM 文本格式打开 Manifest 自动解析计划

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创作者: learningmatter-mit
来源: learningmatter-mit/AtomisticSkills
收录方: OpenAgentSkill 社区索引

归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。

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所有者认领

认领此 Skill 页面

这条 Registry 收录列表归属于 learningmatter-mit，但尚未标记为官方。认领后可增加已验证所有者信号，使后续发布、安装和审计更新更值得信赖。

分享工具包

创作者外链工具包

将证据徽章加入你的 README

在开发者评估仓库的位置展示规范页面、当前信任与审计信号，以及真实的 Agent 验证证据。

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预览徽章打开审计创作者工具包

社区信号

告诉我们这个 Skill 是否对你的 Agent 工作流有帮助。汇总反馈会持续改善排序。

概览

chem-react-ot — React-OT Transition State Generation