ml-property-predict-scd

ml-property-predict-scd

Goal

Use SelfConditionedDenoisingAtoms for four related workflows:

• apply a frozen SCD checkpoint as a live atomistic encoder
• train a lightweight head on top of a frozen SCD backbone
• fine-tune an entire pretrained SCD checkpoint on a new property task
• pretrain a new SCD model or add a new dataset adapter

First Checks

1. Use the scd-agent environment from conda-envs/scd-agent/.
2. Confirm the upstream repo exists at ../SelfConditionedDenoisingAtoms relative to AtomisticSkills, or create it with conda-envs/scd-agent/install.sh.
3. Read the upstream README.md and examples.ipynb.
4. Then read the local references in this skill:
   • references/repo-map.md
   • references/transfer-recipes.md
   • references/config-recipes.md
   • references/dataset-contract.md if a new dataset is involved

Checkpoint Selection

• Use ct-scd-pcq for molecule property prediction, molecule embeddings, and molecule-side transfer learning.
• Use ct-scd-amp for materials property prediction, periodic materials embeddings, and materials-side transfer learning.

Do not swap these by default. The public checkpoints were pretrained on different domains.

Instructions

1. Frozen backbone embeddings

Default to out["mol_emb"] for graph-level downstream ML.

• Use return_atom_embs=True only when the downstream task needs atom- or site-level features.
• Keep the checkpoint frozen and in eval() mode.
• Disable the denoising head for this workflow to avoid wasted compute.
• Pass graph_batch=batch only when allow_periodic or noise_in_loader is enabled. Do not force graph_batch on the fast molecular path.
• Reuse templates/extract_embeddings.py as the starting point. It keeps the model live and returns embeddings on demand instead of defaulting to a frozen feature dump.

2. Lightweight training with a frozen SCD backbone

Use templates/train_lightweight_head.py for three lightweight options:

1. scalar_head Appropriate for invariant scalar regression targets. This path trains only the model's native scalar_head using pretrained backbone weights.
2. atom_emb_mlp Pools atom_embs with sum or mean, then trains a 1- or 2-layer MLP head.
3. mol_emb_mlp Uses mol_emb directly, then trains a 1- or 2-layer MLP head.