Computational Discovery for Crafting Multi-dimensional and Multi-functional Metal-Organic Framework Composites
Bibliographic record
Abstract
Rational design of multiple material components to create composite materials for synergistic enhancement is a crucial strategy in materials science. The combination of two-dimensional (2D) and three-dimensional (3D) metal-organic frameworks (MOFs) has great potential for creating multi-dimensional and multi-functional composites, expanding the material space for various applications. In this study, we developed a novel screening algorithm to construct 2D-MOF@3D-MOF composite structures using the intrinsic geometrical information of each MOF. Our algorithm was designed to prioritize synthesizability and identified several pairs of 2D-MOFs and 3D-MOFs. The screening results revealed that Ni-HHTP@UiO-66, a previously synthesized composite material, was among the potential candidates. Furthermore, the 2D-MOF@3D-MOF composite candidate that passed our algorithm exhibited superior mechanical strength compared to the mismatched composite. Our research advances the field of MOF by providing a practical screening algorithm for identifying suitable 2D-MOF@3D-MOF composite candidates and paves the way for the discovery of new materials with enhanced properties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".