<i>Ab Initio</i> Prediction of Metal-Organic Framework Structures
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) have emerged as highly versatile materials with applications in gas storage and separation, solar light energy harvesting and photocatalysis. The design of new MOFs, however, has been hampered by the lack of computational methods for ab initio MOF structure prediction, which could be used to inspire and direct experimental synthesis. Here, we report the first ab initio method for the prediction of MOF structures and test it against a diverse set of known MOFs that were chosen for their differences in topology, metal coordination geometry, and ligand binding sites. In all cases, our calculations produced structures that match experiment using only the target composition and ligand molecular structure, proving the versatility of our procedure. The herein presented methodology utilizes the point group symmetry of ligands to enable, for the first time, prediction of MOF structures from first principles, without having to resort to empirical guidelines based on rigid connectivity of nodes and linkers, or to previously determined crystal structures and topologies of known MOFs. This advance provides the first tool to change MOF design from an empirically based process that is based on chemistʼs intuition rooted in literature- or database-established knowledge of node-and-linker connectivity to a more general and theory-driven materials development. This ab initio MOF structure prediction approach, which is here validated on a range of known MOF classes, provides a unique opportunity to explore the phase landscape of MOFs computationally and enables MOF research and development even in case of limited access to laboratory resources, as for example in case of a global pandemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.031 | 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".