This repository curates learning materials, links, and notes related to Density Functional Theory (DFT) and Machine Learning (ML) for materials / computational chemistry. The content is primarily sourced from the public webpage maintained by Tsuji Lab (IGSES-Tsuji) and reorganized here for easier navigation.

• Primary source (upstream): https://sites.google.com/view/igses-tsuji/home

Note: This repository is a curated compilation for learning purposes. If there is any discrepancy, please refer to the upstream page as the source of truth.

• DFT: Entry points to core concepts and typical workflows (structure optimization, band structure / DOS, surfaces & defects, and post-processing).
• Machine Learning: Entry points to ML for property prediction, feature engineering, model training/evaluation, and ML interatomic potentials (if applicable).
• Goal: Provide a clear, searchable structure that reduces the barrier to getting started and helps with reproducibility.

Most of the referenced materials come from:

• Tsuji Lab (IGSES-Tsuji): https://sites.google.com/view/igses-tsuji/home

Many thanks to Tsuji Lab for making these resources publicly available.

The layout below is a suggested structure. Adjust this section to match your actual repository.

• dft/ — DFT-related resources (concepts, tutorials, workflows, FAQs)
• ml/ — ML-related resources (models, data, features, evaluation, potentials)
• papers/ — Reading list and citations (recommended: store references/links rather than copyrighted PDFs)
• notes/ — Study notes and summaries
• scripts/ — Utility scripts (data processing, feature extraction, plotting, job automation, etc.)
• examples/ — Reproducible examples (input templates, minimal working cases)
• data/ — Dataset index and descriptions (for large files, consider Git LFS or external hosting)

1. Start with the upstream page for context and the full collection:
   https://sites.google.com/view/igses-tsuji/home
2. Browse this repository by topic:
   • For DFT: begin with dft/ and examples/
   • For ML: begin with ml/ and scripts/
3. When adding a reproducible workflow/result:
   • Create a dedicated subfolder (e.g., examples/<topic>/)
   • Document dependencies, inputs, commands, outputs, and reference links

• This repository aims to include links, bibliographic references, original notes, and original code/scripts.
• If you plan to copy/mirror substantial upstream text or files, please verify upstream licensing/permission requirements and clearly attribute the source here.
• For any third-party content, add per-directory LICENSE/NOTICE files or explicit source statements.

Recommendation: choose a license for your own contributions (e.g., MIT / Apache-2.0 / CC BY 4.0) and include a root-level LICENSE.

PRs are welcome, including:

• Fixing or adding links and short descriptions/tags
• Adding reproducible examples and utility scripts
• Adding FAQs / troubleshooting notes

If you want to add large datasets, prefer external download instructions + checksums, or use Git LFS.

For upstream resource ownership, corrections, takedown, or attribution requests, please refer to the upstream page:
https://sites.google.com/view/igses-tsuji/home