Codex UI Prediction CSV Test
QA autograded prediction CSV workflow Codex UI Prediction CSV Test QA autograded prediction CSV workflow Overview Summarize the materials science problem, target property or task, and the expected submission format. Description Explain the scientific background, why this problem is important, and what participants should build or predict. Evaluation Describe the evaluation metric, whether highe...
Overview
Codex UI Prediction CSV Test
QA autograded prediction CSV workflow
Overview
Summarize the materials science problem, target property or task, and the expected submission format.
Description
Explain the scientific background, why this problem is important, and what participants should build or predict.
Evaluation
Describe the evaluation metric, whether higher or lower is better, public/private split if used, and how ties are handled.
Timeline
- Launch:
- Submission deadline:
- Final evaluation:
- Winner announcement:
Prizes
Describe prizes, awards, recognition, invited talks, or community badges.
Code Requirements
State whether code is required, preferred repository format, license expectations, and reproducibility requirements.
Upgraded Accelerators
Describe any special compute resources, GPU/CPU limits, runtime expectations, or optional accelerator access.
Citation
List required dataset, benchmark, paper, and ChemistryAtlas citations.
Data
Data
Add dataset links, columns, train/test split details, baseline files, and submission format.
Data resources
- [Dataset URL](https://example.org/materials-atlas-qa-dataset)
- [sample_submission.csv](/api/files/e0a0e6c630b44adbbdd74961efc59672)
Rules
Rules
Explain eligibility, allowed data, evaluation metric, deadlines, and citation expectations.