materials-design

name	materials-design
description	Iterative workflow for materials design through candidate generation, property prediction, screening, and refinement.
metadata	{"dependent_skills":["dft-calculation","structure-generation","mattergen-generation","mattergen-finetune","mattergen-evaluation","structure-conversion","mattersim","cgcnn-predictor","vasp-pymatgen"],"tags":["materials-design","inverse-design","screening","generation"]}

Use this guide for iterative crystal materials design workflows that involve candidate generation, structure relaxation, property prediction, screening, and optional refinement.

Standard Loop

1. Define the design target

• Confirm the chemical system, target properties, and screening rules, including thresholds, ranking metrics, batch size, and maximum iterations.

2. Generate candidate structures

• Prefer using mattergen to create a batch of candidate crystals.

3. Relax generated structures

• Before screening, use mattersim to relax generated structures.

4. Predict properties and rank candidates

• Use an available property-prediction workflow on the screened candidate set, such as cgcnn_predictor or other appropriate model/tool.

5. Evaluation

• Evaluate the screened structures with the S.U.N criteria.

6. Validate with DFT

• After screening, run DFT on a few top shortlisted candidates.

Iteration Guidance

• If the result from any of Steps 4-6 does not meet the target requirements, consider iterating the workflow.

• Multiple iteration strategies are available, and you can choose any iteration strategy based on your own analysis:

1. Run another round of generation.
2. Fine-tune the MatterGen model using CGCNN-predicted properties and then generating again with the fine-tuned model.
3. You can also propose and execute other iteration strategies.