Materealize-generated 100 Synthesizable Candidates with Detailed Synthesis Guidance

Zenodo (CERN European Organization for Nuclear Research) 2026 Dataset Open access

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Authors: Seongmin Kim, JAEHWAN CHOI
DOI: 10.5281/zenodo.18240692
Cited by: 0

Abstract

Materealize-generated 100 Synthesizable Candidates with Detailed Synthesis Guidance If you are interested in our work, see our preprint article, "Materealize: a multi-agent deliberation system for end-to-end material design and synthesis". (See also https://huggingface.co/spaces/SNU-MICC/Materealize for our web-demo)

Fields of Study & Subjects

Machine Learning in Materials Science Computational Drug Discovery Methods Material Selection and Properties

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
22 January 2026
Document type
Dataset