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Predict liquid-phase ¹H and ¹³C NMR chemical shifts from a SMILES string using NMRNet (deep learning, SE(3)-Transformer). Outputs per-atom shift values (ppm) and Lorentzian-broadened spectrum PNG files.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Extract structured chemical compound characterization data from chemistry supplementary material documents (PDF/Markdown). 从化学论文补充材料(PDF/Markdown)中提取结构化化合物表征数据。 Use when Kimi needs to extract compound properties including NMR spectra, HRMS, HPLC data, melting points, optical rotation, and yield information from chemistry research papers or supplementary materials. 支持提取NMR谱图、HRMS、HPLC数据、熔点、旋光度、产率等信息。 Supports both single compound extraction and batch extraction of all compounds. 支持单个化合物提取和批量提取所有化合物。
Convert Gaussian gjf input files to XYZ format. 将Gaussian gjf输入文件转换为XYZ格式。 Use when agent needs to convert molecular structure files from Gaussian input format (.gjf) to XYZ format for visualization or use with other computational chemistry software. 当智能体需要将Gaussian输入格式(.gjf)的分子结构文件转换为XYZ格式用于可视化或其他计算化学软件时使用。
Convert PDF files to Markdown using MinerU API. 使用MinerU API将PDF文件转换为Markdown格式。 Use when Kimi needs to extract structured text, images, tables, and formulas from PDF documents while preserving document layout and formatting. 适用于需要提取结构化文本、图片、表格和公式并保留文档布局的场景。 Supports batch conversion and outputs full.md with images/, JSON metadata, and other extracted assets. 支持批量转换，输出full.md、images/目录、JSON元数据等。 Now supports large PDFs (600+ pages) by automatic splitting and merging. 现已支持大文件(600+页)自动拆分和合并处理。
Extract DFT calculation coordinates from PDF files and generate Gaussian gjf files. 从PDF文件中提取DFT计算坐标并生成Gaussian gjf输入文件。 Supports batch processing with separate output folders for each PDF. 支持批量处理，每个PDF单独生成输出文件夹。
Predict and visualize MS/MS spectra from a single SMILES using the fioRa online app. Use when the user wants a mass spectrum, MGF/MSP output, or a plotted stick spectrum from SMILES, with optional custom Name, precursor type, collision energy, and instrument settings.
使用半经验方法 (xTB) 对分子三维结构进行几何优化，支持 SMILES 自动转 3D、XYZ 文件输入，输出优化后坐标、能量、收敛状态。
| name | nmr-prediction |
| description | Predict liquid-phase ¹H and ¹³C NMR chemical shifts from a SMILES string using NMRNet (deep learning, SE(3)-Transformer). Outputs per-atom shift values (ppm) and Lorentzian-broadened spectrum PNG files. |
Use this skill when the user provides a SMILES string and wants:
CCO for ethanol)--nucleus H | C | both (optional, default both)/tmp/chemclaw/nmr_1H_<smiles>.png — ¹H NMR spectrum/tmp/chemclaw/nmr_13C_<smiles>.png — ¹³C NMR spectrumnmr-prediction/
├── SKILL.md
├── nmr_prediction.py
├── requirements.txt
└── assets/
├── NMRNet/ ← NMRNet 精简推理代码 + `oc_limit_dict.txt`
└── Uni-Core/ ← Uni-Core 基础库（需要先 install）
模型权重（大文件，不放进 repo）存放于 /tmp/weights/，通过 --setup 自动下载。
# 将 NMRNet 放入 assets/（从 GitHub 下载 zip 后解压）
cp -r ~/Downloads/NMRNet-main nmr-prediction/assets/NMRNet
# 将 Uni-Core 放入 assets/ 并安装
cp -r ~/Downloads/Uni-Core-main nmr-prediction/assets/Uni-Core
cd nmr-prediction/assets/Uni-Core
python setup.py install # macOS 默认禁用 CUDA，直接执行
cd nmr-prediction
pip install -r requirements.txt
# 如果还没装 torch：pip install torch (CPU 版即可)
cd nmr-prediction
python nmr_prediction.py --setup
此命令通过 remotezip 从 Zenodo 仅提取所需文件：
/tmp/weights/finetune/liquid/.../注意：NMRNet 仓库自带的
demo/notebook/scaler/是固态 NMR scaler，不适用于液相预测。 当前 skill 只保留 NMRNet 的精简推理代码与oc_limit_dict.txt，不依赖demo/数据目录。
cd nmr-prediction
# 预测乙醇的 ¹H + ¹³C 谱
python nmr_prediction.py "CCO"
# 只预测苯的 ¹³C 谱
python nmr_prediction.py "c1ccccc1" --nucleus C
# 预测咖啡因的 ¹H 谱
python nmr_prediction.py "Cn1cnc2c1c(=O)n(c(=O)n2C)C" --nucleus H
SMILES
↓ RDKit: AddHs + EmbedMolecule + MMFFOptimize
3D 分子坐标 (atoms + coordinates)
↓ atoms_target_mask: 标记目标元素 (H 或 C) 为 1
NMRNet 数据记录 (dict)
↓ UniMatModel (SE(3)-Transformer, unimol_large 架构)
每原子预测化学位移 (scaled)
↓ TargetScaler.inverse_transform
化学位移 (ppm)
↓ Lorentzian 叠加
NMR 谱图 PNG
/tmp/weights/（重启后消失，需重新 --setup）assets/ 里，随 repo 一起走assets/NMRNet/oc_limit_dict.txt 为运行时所需字典文件，不能删除