Fine-tune MatGL machine learning interatomic potentials on custom datasets.
$ npx skills add learningmatter-mit/AtomisticSkills --skill ml-matgl-finetune -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-matgl-finetune --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ml-matgl-finetune .claude/skills/ml-matgl-finetune && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ml-matgl-finetune" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-matgl-finetune into .claude/skills/ml-matgl-finetune/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-matgl-finetune", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-matgl-finetuneType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add learningmatter-mit/AtomisticSkills --skill ml-matgl-finetune -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-matgl-finetune --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ml-matgl-finetune .agents/skills/ml-matgl-finetune && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ml-matgl-finetune" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-matgl-finetune into .agents/skills/ml-matgl-finetune/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-matgl-finetune", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add learningmatter-mit/AtomisticSkills --skill ml-matgl-finetune -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-matgl-finetune --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ml-matgl-finetune .cursor/skills/ml-matgl-finetune && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ml-matgl-finetune" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-matgl-finetune into .cursor/skills/ml-matgl-finetune/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-matgl-finetune", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/learningmatter-mit/AtomisticSkills.git --path skills/ml-matgl-finetune--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add learningmatter-mit/AtomisticSkills --skill ml-matgl-finetune -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-matgl-finetune --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ml-matgl-finetune .gemini/skills/ml-matgl-finetune && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ml-matgl-finetune" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-matgl-finetune into .gemini/skills/ml-matgl-finetune/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-matgl-finetune", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install learningmatter-mit/AtomisticSkills ml-matgl-finetuneInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add learningmatter-mit/AtomisticSkills --skill ml-matgl-finetune -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ml-matgl-finetune .github/skills/ml-matgl-finetune && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ml-matgl-finetune" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-matgl-finetune into .github/skills/ml-matgl-finetune/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-matgl-finetune", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add learningmatter-mit/AtomisticSkills --skill ml-matgl-finetune -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-matgl-finetune --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ml-matgl-finetune .opencode/skills/ml-matgl-finetune && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ml-matgl-finetune" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-matgl-finetune into .opencode/skills/ml-matgl-finetune/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-matgl-finetune", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
ml-matgl-finetuneFine-tune MatGL machine learning interatomic potentials on custom datasets.
ML Matgl Finetune is an agent skill from learningmatter-mit/AtomisticSkills. Fine-tune MatGL machine learning interatomic potentials on custom datasets.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts (for example `examples/matgl-wbm-finetune/README.md`, `examples/matgl-wbm-finetune/finetune_record.json` and `examples/matgl-wbm-finetune/training_history.json`).
It sits in AI & LLM Engineering, covering Fine-tuning and Machine learning. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.
2 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 6257444. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 3 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
github.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
ML Matgl Finetune loads about 1.4k tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 557 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from learningmatter-mit/AtomisticSkills at commit 6257444, republished under its MIT licence (© learningmatter-mit). 557 words, ~1,372 tokens.
.claude/skills/ml-matgl-finetune/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.To evaluate and improve the accuracy of a foundation MatGL potential (e.g., CHGNet, M3GNet, TensorNet) for a specific chemical system or physical property using the provided Python fine-tuning script.
/benchmark-finetuning workflow for details.scripts/prepare_matgl_data.py to process JSON structures and split into training and validation sets.scripts/train_matgl.py to begin fine-tuning natively on the GPU using PyTorch Lightning.register_model tool to register the newly fine-tuned model checkpoint into the local registry so future research tasks can discover and reuse it.Convert your dataset into the appropriate JSON format for MatGL training:
${CLAUDE_SKILL_DIR}/../../venv/run mlip python ${CLAUDE_SKILL_DIR}/scripts/prepare_matgl_data.py \
--data /path/to/training_data.json \
--model CHGNet-PES-MatPES-PBE-1M-2026.9 \
--val-split 0.1 \
--output-dir ./matgl_finetunedFine-tune the model using the prepared data:
${CLAUDE_SKILL_DIR}/../../venv/run mlip python ${CLAUDE_SKILL_DIR}/scripts/train_matgl.py \
--train-data ./matgl_finetuned/train_data.json \
--val-data ./matgl_finetuned/val_data.json \
--model CHGNet-PES-MatPES-PBE-1M-2026.9 \
--epochs 10 \
--lr 1e-3 \
--batch-size 4 \
--freeze-backbone \
--output-dir ./matgl_finetunedMatGL fine-tuning is divided into a data preparation step (formatting nested dictionaries and converting lists) and a native training run utilizing PyTorch Lightning.
prepare_matgl_data.py)| Key | Type | Default | Description |
|---|---|---|---|
--data | str | (Required) | Path to JSON file containing ASE/pymatgen structure dictionaries |
--model | str | CHGNet-MatPES-PBE-2025... | Base model name or path to a checkpoint |
--output-dir | str | ./fine_tuning | Directory to save the processed data |
--val-split | float | 0.1 | Fraction of data to set aside for validation |
--seed | int | 42 | Random seed for splitting validation data |
--vasp-stress-conversion | flag | - | If set, multiplies stress values by -1/160.2x to convert VASP raw kB to eV/ų |
train_matgl.py)| Key | Type | Default | Description |
|---|---|---|---|
--train-data | str | (Required) | Path to JSON file containing training data |
--val-data | str | None | Path to JSON file containing validation data (optional) |
--model | str | CHGNet-MatPES-PBE-2025... | Base model name or path to a checkpoint |
--epochs | int | 10 | Number of training epochs |
--lr | float | 1e-3 | Learning rate |
--batch-size | int | 4 | Training batch size |
--device | str | auto | Target compute device (cuda or cpu) |
--output-dir | str | ./fine_tuning | Directory to save the fine-tuned model and logs |
--freeze-backbone | flag | - | Freeze backbone (interaction blocks); only readout heads are trained |
--reinit-head | flag | - | Re-initialize readout head weights |
--scheduler | str | CosineAnnealingLR | CosineAnnealingLR or ReduceLROnPlateau |
--patience | int | None | Early stopping patience (epochs) |
--energy-weight | float | 1.0 | Loss weight for energy |
--force-weight | float | 1.0 | Loss weight for forces |
--stress-weight | float | 0.1 | Loss weight for stress |
--freeze-backbone is strongly recommended to prevent catastrophic forgetting.element_refs): If your fine-tuning data is computed using the same DFT functional (e.g., PBE) as the foundation model's original training data, you should reuse the foundation model's original isolated atom reference energies instead of re-fitting them. This maintains thermodynamic compatibility across the periodic table.mlip environment (venv/run mlip ...). MatGL >= 4 uses PyTorch Geometric only; there is no DGL backend.eV/ų. Raw VASP stress obtained directly via some JSON files may be in kilo-Bar (kB). The Atomate2 MCP tool handles this conversion automatically when convert_units=True. However, if your JSON labels contain raw kB stress, you MUST pass the --vasp-stress-conversion flag to scripts/prepare_matgl_data.py to automatically scale them by -1/160.2x. For more details on unit standardization, see @[skills/general-property-units/SKILL.md].Author: Bowen Deng Contact: GitHub @learningmatter-mit
© learningmatter-mit, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 7 other files (scripts) in skills/ml-matgl-finetune of learningmatter-mit/AtomisticSkills.
Open the folder on GitHubat commit 6257444
ML Matgl Finetune next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| ML Matgl Finetune this skilllearningmatter-mit/AtomisticSkills | 176 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Unimoljinzhezenggroup/computational-chemistry-agent-skills | 148 | 1 repos | ~1.5k | Automated safety check: Pass | LGPL-3.0-or-later | |
| Adapting Transfer Learning Modelsjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Deepmd TrainHello-QM/catgo-LRG | 205 | 1 repos | ~1.1k | Automated safety check: Pass | AGPL-3.0 | |
| ML Research LabAnastasiyaW/codex-claude-code-config | 154 | — | ~794 | Automated safety check: Pass | MIT | |
| RuView Model Trainingruvnet/RuView | 97k | — | ~1.3k | Automated safety check: Notes | MIT |
jinzhezenggroup/computational-chemistry-agent-skills
A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in…
jeremylongshore/tons-of-skills-marketplace
Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques.
Hello-QM/catgo-LRG
Train DeePMD-kit machine learning potentials. An agent skill from Hello-QM/catgo-LRG.
AnastasiyaW/codex-claude-code-config
Machine-learning research loop for dataset curation, fine-tuning, evaluation, inference deployment, experiment tracking, and model explainability.
ruvnet/RuView
Trains and evaluates several WiFi-signal-based pose and sensing models, from unsupervised pose estimation to domain adaptation and publishing.
Leeroo-AI/superml
Produces ranked, evidence-grounded next steps when an ML experiment has stalled, drawing on a Leeroopedia knowledge base or on fetched docs and issues.
learningmatter-mit/AtomisticSkills
Define a docking search box (center coordinates + box dimensions in Angstroms) from a co-crystal ligand, binding-site residues, or a saved JSON specification.
learningmatter-mit/AtomisticSkills
Build a solvated, charge-neutralized protein-ligand complex for OpenMM molecular dynamics simulation.
learningmatter-mit/AtomisticSkills
Identify and rank ligandable pockets on a protein structure or model using geometry (fpocket) or an ML predictor (P2Rank).
learningmatter-mit/AtomisticSkills
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
learningmatter-mit/AtomisticSkills
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
learningmatter-mit/AtomisticSkills
Query multiple MOF databases (QMOF via MPContribs; ARC-MOF DB7/Majumdar et al.
Categories
Fine-tune MatGL machine learning interatomic potentials on custom datasets. ML Matgl Finetune is an agent skill from learningmatter-mit/AtomisticSkills. Fine-tune MatGL machine learning interatomic potentials on custom datasets.
ML Matgl Finetune fits situations like: tasks that involve Fine-tuning; tasks that involve Machine learning.
Run `npx skills add learningmatter-mit/AtomisticSkills --skill ml-matgl-finetune -a claude-code`. Or copy the skill folder (skills/ml-matgl-finetune in learningmatter-mit/AtomisticSkills) into .claude/skills/ml-matgl-finetune in your project. Claude Code loads it when a task matches its description.
Run `npx skills add learningmatter-mit/AtomisticSkills --skill ml-matgl-finetune -a codex`. Or copy the skill folder (skills/ml-matgl-finetune in learningmatter-mit/AtomisticSkills) into .agents/skills/ml-matgl-finetune in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add learningmatter-mit/AtomisticSkills --skill ml-matgl-finetune -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ml-matgl-finetune, .gemini/skills/ml-matgl-finetune, .github/skills/ml-matgl-finetune and .opencode/skills/ml-matgl-finetune in your project.
Going by SKILL.md and its folder, ML Matgl Finetune needs Python for the scripts in its folder. Our summary lists: Python 3.
SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
ML Matgl Finetune is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with ML Matgl Finetune: Unimol (jinzhezenggroup/computational-chemistry-agent-skills, 148 stars), Adapting Transfer Learning Models (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), Deepmd Train (Hello-QM/catgo-LRG, 205 stars) and ML Research Lab (AnastasiyaW/codex-claude-code-config, 154 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 176 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 7, 2026.
Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.
