Digital Discovery of Synthesizable Metal−Organic Frameworks via Molecular Dynamics‑Informed, High‑Fidelity Deep Learning (Adv. Funct. Mater. 14/2026)

Advanced Functional Materials 2026 Open access

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Authors: Xiaoyu Wu, Rui Zheng, Quan Liu, Jianwen Jiang
DOI: 10.1002/adfm.74277
Cited by: 1

Abstract

Synthesizable Metal–Organic Frameworks In a futuristic MOF factory, an AI scientist inspects frameworks flowing along a production line. Floating blueprints and free-energy maps represent a molecular-dynamics-informed TabPFN model, which screens vast hypothetical MOF libraries and highlights synthesizable, experimentally actionable candidates. More information can be found in the Research Article by Jianwen Jiang and co-workers (10.1002/adfm.202519565).

Fields of Study & Subjects

Metal-Organic Frameworks: Synthesis and Applications Machine Learning in Materials Science Nanocluster Synthesis and Applications

Publication details

Journal / source
Advanced Functional Materials
Publisher
Wiley
Volume
36
Issue
14
Publication date
1 February 2026
Language
EN