Mapping the crystallization landscape of rare earth MOFs: a high-throughput investigation of structure, kinetics, and selectivity

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Chem. Sci., 2026, Advance ArticleDOI: 10.1039/D5SC09992G, Edge Article Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Madeleine A. Gaidimas, Gyu-Hee Kim, Zi-Ming Ye, Julian S. Magdalenski, Na…

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Mapping the crystallization landscape of rare earth MOFs: a high-throughput investigation of structure, kinetics, and selectivity

* Corresponding authors

a Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
E-mail: [email protected]

b International Institute for Nanotechnology, Northwestern University, Evanston, IL 60208, USA

c Department of Computer Science, University of Toronto, Toronto, Canada

d Acceleration Consortium, Toronto, Canada

e Department of Chemistry, University of Toronto, Toronto, Canada

f Materials Discovery Research Institute, UL Research Institutes, Skokie, IL 60077, USA

g Vector Institute for Artificial Intelligence, Toronto, Canada

h Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada

i Department of Materials Science and Engineering, University of Toronto, Toronto, Canada

j Senior Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, Canada

k NVIDIA, Toronto, Canada

l Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA

m Paula M. Trienens Institute for Sustainability and Energy, Northwestern University, Evanston, IL 60208, USA

Abstract

Systematically exploring the multidimensional parameter space of metal–organic framework (MOF) crystallization remains challenging due to limited adoption of high-throughput (HT), automated experimental workflows. MOF development is dominated by manual synthesis and characterization methods and a trial-and-error approach, and the integration of HT MOF synthesis with HT characterization and analysis is uncommon. Here, we present a practical HT MOF discovery workflow that combines automated solvothermal synthesis with three scalable characterization methods. First, we characterize bulk structure using HT powder X-ray diffraction (PXRD) and rapid matching of PXRD data to reported MOF crystal structures. We also employ a custom machine-learning based computer vision (CV) model to identify MOF formation rates from images of sample vials. Finally, we develop a HT X-ray fluorescence (XRF) method to quantify elemental ratios in bimetallic MOF samples. As a case study, we investigate the crystallization of rare earth (RE) MOFs, systematically probing the effects of reaction conditions such as metal identity, linker structure, temperature, and acid concentration. We then leverage these insights to demonstrate a proof-of-concept selective crystallization from a mixed RE solution. Using our HT workflow, we performed 1488 unique MOF crystallization reactions and characterized the resulting samples through the collection of >800 PXRD patterns, CV analysis of >13 000 images, and elemental analysis measurements of 144 bimetallic crystallization reactions. We identified 5 previously unreported rare earth MOFs (NU-2501–NU-2505) and characterized their structures with single-crystal X-ray diffraction (SCXRD) and microcrystal electron diffraction (MicroED). Our HT approach enabled us to construct phase diagrams mapping out the crystallization preferences and formation kinetics for 18 distinct RE-MOF products. By unifying automated MOF synthesis with multimodal characterization, we demonstrate the efficient exploration of a complex synthetic landscape, generating insights into MOF structure, crystallization kinetics, and composition.

• This article is part of the themed collections: 2026 ChemSci Pick of the Week Collection and 2026 Chemical Science HOT Article Collection