Use cases

High-throughput studies push a single computational method across thousands of materials at once — far more calculations than any researcher could launch, babysit, and bookkeep by hand. AiiDA submits the jobs, retries the ones that fail, and records every input, code, and result in a queryable provenance graph, so a campaign of this size stays reproducible from the first structure to the last.

In one recent screening, researchers searched the Materials Cloud three-dimensional structure database (MC3D) of experimentally known inorganic compounds for conventional, phonon-mediated superconductors. AiiDA orchestrated the full pipeline — electronic-structure relaxations, phonon calculations, electron–phonon coupling, and critical-temperature estimates — across more than 4,500 candidate metals. The search recovered known superconductors and surfaced 24 previously unreported ones with predicted critical temperatures above 10 K.

AiiDA is not limited to simulations: the same engine that manages computational jobs can also drive physical instruments. That makes it possible to plan, run, and track a laboratory experiment with exactly the reproducibility and provenance researchers expect from a calculation.

The Aurora platform pairs a robotic line that assembles and cycles coin-cell batteries with AiiDA. Each real-world experiment is submitted and monitored like a computational job, and AiiDA records complete provenance for every cell — linking protocols, raw measurements, and analysis into a single graph. Live monitoring lets the platform stop underperforming cells early, and legacy measurements are folded into the same digital record, bridging trust in the data with hands-off control of the lab.