Divergent strategies and convergent outcomes in autonomous materials discovery

Abstract:Scientific agents are mostly evaluated on whether they complete tasks or recover known results; we instead study variation across repeated open-ended campaigns. Sixteen separately initialized sessions of one model-harness configuration received a frozen database of 12,499 metal-organic frameworks, a methane-storage objective, a pinned protocol and a one-week budget. Strategies diverged into four approaches spanning 100--5,000 screened structures, and eight built 2,253 hypothetical structures. Yet the agents recovered the same materials frontier near 200 cm^3/cm^3, and an independent calculation of the database's porous region found its nine best structures all among their reports. Enforced checks on half the agents raised fresh-run reproduction from one of eight to eight of eight but could not detectably improve conclusion validity, because fifteen of sixteen agents selected the same audit-excluded entry, an incomplete structure whose missing anions created artificial pore volume. Replicated agents thus reveal both robust conclusions and common-mode errors from shared inputs.

Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci)

arXiv:2609.23957 [cs.AI]

(or arXiv:2609.23957v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.23957 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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