Prediction and synthesis of novel metal-organic polyhedra in The World Avatar
6 affiliations
- 1CMPG - Computational Modelling Pirmasens GmbH, Delaware Avenue 1–3, Pirmasens, Germany, 66953
- 2Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, United Kingdom, CB3 0AS
- 3School of Chemistry Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore, Singapore, 637459
- 4CARES - Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, Singapore, Singapore, 138602
- 5Institute of Physical and Theoretical Chemistry, Graz University of Technology, Stremayrgasse 9, Graz, Austria, 8010
- 6Chemical Engineering, MIT, 77 Massachusetts Avenue, Cambridge, MA, United States, 02139
Abstract
Metal-organic polyhedra (MOPs) offer a modular route to porous molecular materials, but their discovery is limited by the difficulty of translating digital designs into experimentally accessible compounds. Here, we demonstrate an integrated workflow for predicting and synthesising novel MOPs in The World Avatar. To our knowledge, this is the first reticular material discovery workflow executed and persisted entirely in a shared machine-readable knowledge representation, without human-specified initial reaction conditions. This domain’s small literature corpus hinders data-driven synthesis prediction, yet its high symmetry provides exploitable structural analogies. We therefore encode auditable chemical reasoning as explicit rules rather than learning it statistically. MOP candidates are linked to synthesis knowledge, so procedures for new targets are inferred by analogy to known materials and rendered as human-readable instructions and machine-executable protocols. Candidates are assessed through hierarchical geometry optimisation, electronic-structure evaluation, crystal structure prediction, and simulated powder X-ray diffraction. We evaluate the workflow retrospectively against reported zirconium MOP syntheses and prospectively by synthesising a previously unreported Zr-EDB-MOP. Infrared spectroscopy, high-resolution mass spectrometry, and powder X-ray diffraction support formation of the targeted cage in agreement with the predicted model. This establishes a proof of concept for knowledge-integrated discovery in sparse, highly structured materials domains.
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BibTeX
@misc{rihm2026prediction,
title = {{Prediction and synthesis of novel metal-organic polyhedra in The World Avatar}},
author = {Simon D. Rihm and Patrick Butler and Kumaran Elumalai and Ari Fischer and Pei Chong Lim and Xu Rong and Tej S. Choksi and Aleksandar Kondinski and Markus Kraft},
howpublished = {ChemRxiv},
year = {2026},
month = aug,
doi = {10.26434/chemrxiv.15007397/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15007397/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Prediction and synthesis of novel metal-organic polyhedra in The World Avatar
AU - Simon D. Rihm
AU - Patrick Butler
AU - Kumaran Elumalai
AU - Ari Fischer
AU - Pei Chong Lim
AU - Xu Rong
AU - Tej S. Choksi
AU - Aleksandar Kondinski
AU - Markus Kraft
PY - 2026
DA - 2026/08/13
PB - ChemRxiv
DO - 10.26434/chemrxiv.15007397/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15007397/v1
AB - Metal-organic polyhedra (MOPs) offer a modular route to porous molecular materials, but their discovery is limited by the difficulty of translating digital designs into experimentally accessible compounds. Here, we demonstrate an integrated workflow for predicting and synthesising novel MOPs in The World Avatar. To our knowledge, this is the first reticular material discovery workflow executed and persisted entirely in a shared machine-readable knowledge representation, without human-specified initial reaction conditions. This domain’s small literature corpus hinders data-driven synthesis prediction, yet its high symmetry provides exploitable structural analogies. We therefore encode auditable chemical reasoning as explicit rules rather than learning it statistically. MOP candidates are linked to synthesis knowledge, so procedures for new targets are inferred by analogy to known materials and rendered as human-readable instructions and machine-executable protocols. Candidates are assessed through hierarchical geometry optimisation, electronic-structure evaluation, crystal structure prediction, and simulated powder X-ray diffraction. We evaluate the workflow retrospectively against reported zirconium MOP syntheses and prospectively by synthesising a previously unreported Zr-EDB-MOP. Infrared spectroscopy, high-resolution mass spectrometry, and powder X-ray diffraction support formation of the targeted cage in agreement with the predicted model. This establishes a proof of concept for knowledge-integrated discovery in sparse, highly structured materials domains.
ER -