Synthesizability: The Open Question in Digital MOF Discovery
Abstract
The growing number of hypothetical metal–organic frameworks (MOFs) has created a need for reliable methods to identify candidates that can be synthesized experimentally. This Viewpoint examines current computational approaches to the evaluation of MOF synthesizability, grouping them into thermodynamic, strain- and mechanics-based, and data-driven methods. Thermodynamic studies evaluate formation enthalpies or free energies, stability relative to competing phases, and can include solvent effects. Structural approaches quantify linker deformation and mechanical stability as a proxy for viability of a hypothetical structure. Data-driven machine learning models use geometric, topological, or chemical relationships to known materials as a way to estimate synthesizability. We review the recent advances of these approaches, their strengths and their limitations, noting that at the present time, there is no silver bullet to solve this open challenge. Future progress will require combining complementary methods and using consistent experimental data to refine computational models and allow the identification of candidates for synthesis in screening studies.
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BibTeX
@article{coudert2026synthesizability,
title = {{Synthesizability: The Open Question in Digital MOF Discovery}},
author = {François-Xavier Coudert},
journal = {ACS Materials Letters},
year = {2026},
month = sep,
doi = {10.1021/acsmaterialslett.6c00911},
url = {https://hal.science/hal-05755473v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Synthesizability: The Open Question in Digital MOF Discovery
AU - François-Xavier Coudert
PY - 2026
DA - 2026/09/18
JO - ACS Materials Letters
DO - 10.1021/acsmaterialslett.6c00911
UR - https://hal.science/hal-05755473v1
AB - The growing number of hypothetical metal–organic frameworks (MOFs) has created a need for reliable methods to identify candidates that can be synthesized experimentally. This Viewpoint examines current computational approaches to the evaluation of MOF synthesizability, grouping them into thermodynamic, strain- and mechanics-based, and data-driven methods. Thermodynamic studies evaluate formation enthalpies or free energies, stability relative to competing phases, and can include solvent effects. Structural approaches quantify linker deformation and mechanical stability as a proxy for viability of a hypothetical structure. Data-driven machine learning models use geometric, topological, or chemical relationships to known materials as a way to estimate synthesizability. We review the recent advances of these approaches, their strengths and their limitations, noting that at the present time, there is no silver bullet to solve this open challenge. Future progress will require combining complementary methods and using consistent experimental data to refine computational models and allow the identification of candidates for synthesis in screening studies.
SN - 2639-4979
ER -