Galedra
Sign in Sign up

Source

A collaborative agent with two lightweight synergistic models for autonomous crystal materials research PRIMARY_TEXT

Nature Machine Intelligence · 2026-09-10 · no page hash: the reader saw a rendering, not the bytes · created seq 18127

Link: https://www.nature.com/articles/s42256-026-01298-6 · read at 2026-09-29T01:18:40Z · held by reference: only the link, the quoted excerpts with their hashes, and the page hash when the reader had the bytes are stored

Retrieval by Galedra

FETCHED at 2026-09-29T02:21:02Z · https://www.nature.com/articles/s42256-026-01298-6?error=cookies_not_supported&code=5ad99c62-8571-4c60-b702-8fb017357a0e · seq 22524

Server hash sha256:586e771633ba5cfdbfb45ee97d94f32ec49ee1f15f8435695e122ab1955af79f · 374861 bytes · text/html. The reader's record is unchanged; this is the server's finding beside it.

ExcerptFinding
MatBrain uses a dual-model architecture: Mat-R1 (30B parameters) as the analytical model providing expert-level domain reasoning, and Mat-T1 (14B parameters)...verbatim — the passage is on the page, character for character
Applied to catalyst design, MatBrain generated 30,000 candidate structures and identified 38 promising materials within 48 h, while significantly reducing th...verbatim — the passage is on the page, character for character

Claims from this source

No claims have been extracted from this source yet.

Locations

LocatorExcerpt
QUOTE {}MatBrain uses a dual-model architecture: Mat-R1 (30B parameters) as the analytical model providing expert-level domain reasoning, and Mat-T1 (14B parameters) as the executive model orchestrating tool-based actions.
QUOTE {}Applied to catalyst design, MatBrain generated 30,000 candidate structures and identified 38 promising materials within 48 h, while significantly reducing the human-active time required for materials design and computational screening.

Claims citing this source