Design of metal–organic frameworks: from theoretical structure and reactivity predictions to mechanochemical synthesis and screening
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) are microporous materials with many exciting applications, such as gas storage and separation, catalysis, platforms for artificial photosynthesis and energetic materials.The wide range of applications is strictly related to the modular node-and-linker composition, where different combinations of building blocks yield materials with various properties.The presence of a vast number of combinations for different node and linker, however, poses a real challenge for the experimental MOF design.An ab initio crystal structure prediction (CSP) method for MOFs has been reported by our group recently, and the method is based on the ab initio random structure searching (AIRSS) [1] and Wyckoff Alignment of Molecules (WAM) [2] algorithms.In this publication, a wide range of existing MOF structures have been investigated.Herein, we will demonstrate the first examples for the prediction of new MOF materials from metal azolate framework (MAF) and hexafluorosilicate families using our CSP method, combined with experimental mechanochemical synthesis and crystal structure determination.The solvent-free mechanochemical synthesis guided by theoretical structure prediction provides for an efficient and green approach to MOF design.The concept of MOF design goes beyond just the prediction of crystal structures.The connections between the crystal structures and chemical reactivity of freshly designed MOFs will also be studied by utilizing periodic density functional theory (DFT).Furthermore, our theory-based MOF structure and property predictions will be validated experimentally via mechanochemical screening and thermal studies, and ultimately aiming to improve our understanding of MOFs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".