Materials data and quality
The data under materials AI is shakier than the models assume. These articles cover auditing database entries, working with the Materials Project API, and building data pipelines you can trust.
Related: Trust Benchmark · Materials data consulting
Do materials databases agree with each other? I measured it
I compared 344 structure-matched materials across the Materials Project and OQMD. Only 26 percent of formation energies agree within the conventional threshold.
Materials Project API: common mp-api errors and fixes
Fixes for the Materials Project mp-api errors people hit: get_dos_by_material_id returning CompleteDos, 403 Forbidden, API keys, and legacy vs new client.
Materials data engineering, explained
Why materials data is messy, and how data engineering (pipelines, dbt, testing, orchestration) turns scattered scientific data into something you can model.
Scientific data quality: why it decides your model
Scientific data is less trustworthy than the models built on it assume. Why quality, leakage, and provenance decide your result, and how to check them.
Materials Project API tutorial: mp-api, keys, Python
A beginner Materials Project API tutorial: get your API key, install mp-api, run your first Python request, and dodge the Cloudflare 403 gotcha.
How to audit a materials database entry: a six-check list
A practical checklist for deciding whether a value from the Materials Project deserves your trust, with the reasoning behind each check.
Properties belong to structures, not formulas
Diamond and graphite share a formula and agree on almost nothing else. Why polymorphism breaks naive materials data, and how to handle it.
Automatic tests for scientific data: a dbt primer
How to attach rules to a materials dataset, no negative band gaps, one entry per polymorph, that run on every refresh and fail loudly.
The data engineering stack, translated for materials science
What ETL, SQL, dbt, Airflow, Kafka and Spark actually do, each explained with a concrete materials use case, and the two worth learning first.