Fine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets.
$ npx skills add learningmatter-mit/AtomisticSkills --skill ml-fairchem-finetune -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-fairchem-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-fairchem-finetune .claude/skills/ml-fairchem-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-fairchem-finetune" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-fairchem-finetune into .claude/skills/ml-fairchem-finetune/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-fairchem-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-fairchem-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-fairchem-finetune -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-fairchem-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-fairchem-finetune .agents/skills/ml-fairchem-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-fairchem-finetune" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-fairchem-finetune into .agents/skills/ml-fairchem-finetune/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-fairchem-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-fairchem-finetune -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-fairchem-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-fairchem-finetune .cursor/skills/ml-fairchem-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-fairchem-finetune" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-fairchem-finetune into .cursor/skills/ml-fairchem-finetune/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-fairchem-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-fairchem-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-fairchem-finetune -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-fairchem-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-fairchem-finetune .gemini/skills/ml-fairchem-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-fairchem-finetune" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-fairchem-finetune into .gemini/skills/ml-fairchem-finetune/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-fairchem-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-fairchem-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-fairchem-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-fairchem-finetune .github/skills/ml-fairchem-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-fairchem-finetune" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-fairchem-finetune into .github/skills/ml-fairchem-finetune/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-fairchem-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-fairchem-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-fairchem-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-fairchem-finetune .opencode/skills/ml-fairchem-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-fairchem-finetune" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-fairchem-finetune into .opencode/skills/ml-fairchem-finetune/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-fairchem-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-fairchem-finetuneFine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets.
ML Fairchem Finetune is an agent skill from learningmatter-mit/AtomisticSkills. Fine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts (for example `examples/fairchem-wbm-finetune/README.md`, `examples/fairchem-wbm-finetune/_freeze_backbone_helper.py` and `examples/fairchem-wbm-finetune/dataset_metadata.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.
7 steps, taken from the first numbered list 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 and Shell), 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 Fairchem Finetune loads about 1.7k tokens when it runs. Until then it costs about 28 tokens; SKILL.md has 675 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). 675 words, ~1,701 tokens.
.claude/skills/ml-fairchem-finetune/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.To evaluate and improve the accuracy of a foundation Fairchem potential (e.g., UMA, ESEN) for a specific chemical system or physical property using the provided Python fine-tuning script.
/benchmark-finetuning workflow for details.scripts/prepare_fairchem_data.py to convert JSON structures to extxyz, generate native LMDB databases, compute dataset references, and configure a templated uma_sm_finetune_template.yaml.fairchem -c uma_sm_finetune_template.yaml job.run_dir=XXX natively.scripts/extract_fairchem_logs.py to extract curves and verify convergence against the benchmarked foundation metrics.register_model tool to register the newly fine-tuned model checkpoint into the local registry so future research tasks can discover and reuse it.Fairchem fine-tuning relies heavily on the fairchem CLI, which uses Hydra for configuration. The script scripts/prepare_fairchem_data.py bridges standard data into the complex Fairchem directory structure and generates .aselmdb dataset formats automatically.
| Key | Type | Default | Description |
|---|---|---|---|
--data | str | (Required) | Path to JSON file containing ASE/pymatgen structure dictionaries |
--val-data | str | None | Path to JSON file containing validation split. (Optional, otherwise --val-split is used) |
--val-split | float | 0.1 | Validation split if --val-data is not provided |
--seed | int | 42 | Random seed for data splitting and initialization |
--model | str | uma-s-1p1 | Base model name or path to a checkpoint |
--task-name | str | omat | The specific multi-task context to run against (omat, omol) |
--epochs | int | 10 | Number of training epochs |
--lr | float | 4e-4 | Peak learning rate for training |
--batch-size | int | 2 | Training batch size |
--freeze-backbone | flag | N/A | Add flag to mathematically freeze OCP/UMA interaction layers |
--weight-decay | float | 1e-3 | Weight decay parameter |
--warmup-factor | float | 0.2 | LR warmup factor |
--warmup-epochs | float | 0.01 | Epochs to perform LR warmup |
--lr-min-factor | float | 0.01 | Minimum LR factor after decay |
--clip-grad-norm | float | 100.0 | Gradient clipping threshold |
--evaluate-every-n-steps | int | 100 | Steps frequency for validation evaluation |
--checkpoint-every-n-steps | int | 1000 | Steps frequency for model checkpointing |
--ema-decay | float | 0.999 | Exponential moving average decay parameter |
--linref-coeff | str | None | JSON array of elemental energy linear references. If None, it auto-computes it over the data. |
--vasp-stress-conversion | flag | N/A | Add flag to automatically convert kB to eV/ų for VASP inputs |
--output-dir | str | ./fairchem_finetuning | Directory to save the lmdb_output intermediate data and run configs |
[!NOTE] If you have created a dedicated research directory for your current workflow (e.g. using the
create_research_dirtool), you should set the--output-dirargument to a folder within that active research directory to keep all artifacts and models organized. The data preparation script takes several minutes because it automatically creates.aselmdbcopies of all structural inputs mapping to specific index structures.
[!WARNING] Stress Units: Fairchem expects stress in
eV/ų. Raw VASP stress obtained directly via some JSON files may be in kilo-Bar (kB), which is ~160x larger and will cause catastrophic training divergence. The Atomate2 MCP tool handles this conversion automatically whenconvert_units=True. However, if your JSON labels contain rawkBstress, you MUST pass the--vasp-stress-conversionflag toscripts/prepare_fairchem_data.pyto automatically scale them by-1/160.2x. For more details on unit standardization, see @[skills/general-property-units/SKILL.md].
Usage:
# 1. Prepare Data and Config
${CLAUDE_SKILL_DIR}/../../venv/run fairchem python ${CLAUDE_SKILL_DIR}/scripts/prepare_fairchem_data.py \
--data /path/to/training_data.json \
--model uma-s-1p1 \
--epochs 10 \
--lr 4e-4 \
--batch-size 2 \
--freeze-backbone \
--output-dir ./research/my_dir/fairchem_finetuning
# 2. Run Training
export PYTHONPATH=/path/to/research/my_dir/fairchem_finetuning/lmdb_output:$PYTHONPATH
cd /path/to/research/my_dir/fairchem_finetuning/lmdb_output
${CLAUDE_SKILL_DIR}/../../venv/run fairchem fairchem -c uma_sm_finetune_template.yaml job.run_dir=/path/to/research/my_dir/fairchem_finetuning/runs +job.timestamp_id=run_10ep
# 3. Extract Training Logs (Optional, to create standard training_history.json)
${CLAUDE_SKILL_DIR}/../../venv/run fairchem python ${CLAUDE_SKILL_DIR}/scripts/extract_fairchem_logs.py \
--log /path/to/research/my_dir/fairchem_finetuning/runs/run_10ep/logs/trainer.log \
--output-dir /path/to/research/my_dir/fairchem_finetuning/results--task-name for your datasets (omat vs omol).linref_coeff): 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 extract and pass the foundation model's original linref_coeff array instead of allowing the script to automatically re-fit it via Least Squares. This maintains thermodynamic scale compatibility across the periodic table.--debug parameter within the configuration file if training stalls completely.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 11 other files (scripts) in skills/ml-fairchem-finetune of learningmatter-mit/AtomisticSkills.
Open the folder on GitHubat commit 6257444
ML Fairchem 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 Fairchem Finetune this skilllearningmatter-mit/AtomisticSkills | 176 | — | ~1.7k | 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
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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.
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Categories
Fine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets. ML Fairchem Finetune is an agent skill from learningmatter-mit/AtomisticSkills. Fine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets.
ML Fairchem Finetune fits situations like: tasks that involve Fine-tuning; tasks that involve Machine learning.
Run `npx skills add learningmatter-mit/AtomisticSkills --skill ml-fairchem-finetune -a claude-code`. Or copy the skill folder (skills/ml-fairchem-finetune in learningmatter-mit/AtomisticSkills) into .claude/skills/ml-fairchem-finetune in your project. Claude Code loads it when a task matches its description.
Run `npx skills add learningmatter-mit/AtomisticSkills --skill ml-fairchem-finetune -a codex`. Or copy the skill folder (skills/ml-fairchem-finetune in learningmatter-mit/AtomisticSkills) into .agents/skills/ml-fairchem-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-fairchem-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-fairchem-finetune, .gemini/skills/ml-fairchem-finetune, .github/skills/ml-fairchem-finetune and .opencode/skills/ml-fairchem-finetune in your project.
Going by SKILL.md and its folder, ML Fairchem Finetune needs Python and a shell for the scripts in its folder. Our summary lists: Python 3; A Bash shell.
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 Fairchem 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 1.7k tokens (SKILL.md is roughly 6.8k 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 Fairchem 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.
