Experimentally validated <i>ab initio</i> crystal structure prediction of novel metal–organic framework materials
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) are crystalline materials with a broad range of applications, including gas storage and separation, catalysis, drug delivery and energetic materials [1].The diversity of MOF applications is closely related to their modular node-and-linker composition of which various desired MOF properties can be designed.Currently, MOF design is principally based on empirical experimental approaches where geometry and chemical intuition are the main considerations when choosing possible nodes and linkers.In contrast, the development of ab initio computational methods for predicting structures and properties of MOFs could provide new, unexpected opportunities for a more general design and synthesis of such microporous materials.Recently, we have reported [2] the first-principles crystal structure prediction (CSP) method for MOFs, based on the ab initio random structure searching (AIRSS) [3] and Wyckoff Alignment of Molecules (WAM) algorithms, where several classes of existing MOF structures have been successfully reproduced.Herein, I will present the first examples for predicting three new rare zeolitic imidazolate frameworks (ZIFs) based on Cu 2+ ion nodes with imidazolate linkers containing unsaturated side substituents.Our predicted ZIF structures matched the subsequently synthesized materials, presenting the first example of MOFs being designed with the aid of CSP.Moreover, our specific focus on the unsaturated substituted imidazolate linkers with vinyl and acetylene moieties yield ZIFs that exhibit hypergolic behaviour [4,5].The experimentally-observed ZIFs revealed excellent hypergolic ignition properties, and more importantly, the high energy densities were derived from CSP calculations, showing our ability to predict not only structures but also properties of the ab initio designed MOFs.Overall, this study demonstrates how our ab initio approach to MOF design can lead to major advances in MOF development, while also improving our understanding of MOF polymorphism and structure-property relationships.Figure 1.(A) Chemical diagram of copper(II)-based ZIFs.(B) The three 2-substituted imidazole linkers used in this work.(C) Calculated CSP energy landscape of Cu(AIm)2.Each dot is representing a unique crystal structure coloured by its Cu coordination geometry index (τ4).The structure of the global energy minimum α-Cu(AIm)2 is shown while the red dot is representing the β-Cu(AIm)2 structure generated via perturbation analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".