InvDesMobility ALIGNN Mobility Acquisition Ranker
This repository contains the ALIGNN mobility acquisition/ranking models used to
prioritize generated 2D candidate structures before first-principles validation.
Paper and Repositories
Files
baseline/: seed mobility ranker trained before closed-loop feedback.rounds/round_XX/: feedback-updated rankers for closed-loop rounds 01, 02, 03, 04, 06, 07, 08, and 09.
Each directory contains:
best_model.pt: checkpoint used by the ranking/screening scripts.config.json: required ALIGNN architecture and graph configuration.Train_results.json, Val_results.json, Test_results.json: retained evaluation outputs.history_train.json, history_val.json: compact training-history records.ids_train_val_test.json: split identifiers.
Files such as current_model.pt, last_model.pt, W&B logs, cached graphs, and
temporary data ranges are intentionally omitted from this minimal reproducibility
release.
Intended Use
These models are acquisition/ranking models. They estimate a mobility-related
score for generated candidates so that promising structures can be selected for
DFT-scale validation. The model output is not a final trusted mobility label.
Training Data
The baseline model was trained on the seed mobility dataset. Closed-loop models
were retrained with trusted feedback records extracted from completed
2d-mobility first-principles validation runs. The corresponding dataset
manifests and feedback CSV files are packaged in DreamLufei/invDesMobility-data.
Training Parameters
The included config.json files contain the exact ALIGNNAtomWise model settings.
For the round-09 model, key settings include:
- ALIGNNAtomWise with 4 ALIGNN layers and 4 GCN layers.
- Hidden dimension 256, embedding dimension 64.
- k-nearest graph construction, cutoff 8.0, max neighbors 12.
- AdamW optimizer, learning rate 5e-4, batch size 8.
- MSE loss, one-cycle scheduler, early stopping patience 30.
Evaluation
Round-09 retained evaluation artifacts:
Train_results.json: 32 prediction records.Val_results.json: 4 prediction records.Test_results.json: 32 prediction records.
The JSON files are included for auditability rather than summarized away.
Limitations
The ranker is trained on a small, feedback-biased set of trusted DFT mobility
records and is intended only for candidate prioritization. It may extrapolate
poorly outside the distribution of the seed and feedback structures. Final
claims require deterministic VASP-based mobility calculations from 2d-mobility.
