What is recorded here?
A-Lab combined source-derived recipes, machine learning, active learning and robotics for inorganic synthesis. The corrected article reports 36 compounds realized from 57 targets during a 17-day campaign.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The January 2026 correction narrowed the outcome and clarified that new to the prediction platform does not mean new to science. Reanalysis retained 36 of 40 reported successes and found four inconclusive. WRS records this correction without claiming an independent synthesis or diffraction review.
Read the original work
- An autonomous laboratory for the accelerated synthesis of inorganic materials
- An autonomous laboratory for the accelerated synthesis of inorganic materials — deposited bibliography
- Corrected original A-Lab article
- Author Correction (19 January 2026)
- Author-hosted correction, read in full
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Nathan J. Szymanski; Bernardus Rendy; Yuxing Fei; Rishi E. Kumar; Tanjin He; David Milsted; Matthew J. McDermott; Max Gallant; Ekin Dogus Cubuk; Amil Merchant; Haegyeom Kim; Anubhav Jain; Christopher J. Bartel; Kristin Persson; Yan Zeng; Gerbrand Ceder
Source paper byline retained, including repeated label positions. Names are not verified unique identities or measurements of individual effort.
A-Lab · A-Lab research collaboration · Recipe models and active learning; exact per-run model identity not disclosed
Proposed and optimized recipes and interpreted outcomes in a human-designed robotic laboratory. Source-reported attribution; provider attestation is unavailable.
Research locations and affiliations
Documented mapping · Included in country comparison
Documented countries / regions: United States, United Kingdom
Documented research locations or paper affiliations for this contribution. Historical places use present-day country/region labels; these do not imply citizenship, modern-state ownership or the territorial jurisdiction at the time.
English catalogue narrative
A-Lab autonomous synthesis, with corrected outcomes
A-Lab combined source-derived recipes, machine learning, active learning and robotics for inorganic synthesis. The corrected article reports 36 compounds realized from 57 targets during a 17-day campaign.
Scientific scope
The January 2026 correction narrowed the outcome and clarified that new to the prediction platform does not mean new to science. Reanalysis retained 36 of 40 reported successes and found four inconclusive. WRS records this correction without claiming an independent synthesis or diffraction review.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Nathan J. Szymanski; Bernardus Rendy; Yuxing Fei; Rishi E. Kumar; Tanjin He; David Milsted; Matthew J. McDermott; Max Gallant; Ekin Dogus Cubuk; Amil Merchant; Haegyeom Kim; Anubhav Jain; Christopher J. Bartel; Kristin Persson; Yan Zeng; Gerbrand Ceder
Source paper byline retained, including repeated label positions. Names are not verified unique identities or measurements of individual effort.
A-Lab / A-Lab research collaboration / Recipe models and active learning; exact per-run model identity not disclosed: Proposed and optimized recipes and interpreted outcomes in a human-designed robotic laboratory (source reported).