Unraveling Interactions in Molecular Crystals Using Dispersion Corrected Density Functional Theory: The Case of the Epoxydihydroarsanthrene Molecules
Bibliographic record
Abstract
Noncovalent interactions are prevalent in crystal packing and supramolecular chemistry. Directional noncovalent interactions such as donor-acceptor bonds (e.g., hydrogen, chalcogen, and pnictogen bonds) as well as nondirectional forces (such as dispersion) come together to stabilize supramolecular assemblies by striking a delicate energetic balance. Typically, a two-pronged approach employing experimental X-ray structures and gas phase quantum chemical modeling has been used to understand and design supramolecular architectures. Drawing from recent advances in molecular crystal modeling with dispersion corrected density functional theory (DFT), we propose in this article a combination of qualitative noncovalent index (NCI) analysis and periodic and gas phase DFT calculations on substitutional crystal analogues to unravel the dominant interactions in a particular crystal packing. We illustrate the possibilities of this approach by studying three crystal packings of epoxydihydroarsanthrene analogues that present a complex combination of donor-acceptor interactions including pnictogen-pnictogen, pnictogen-π, and pnictogen-chalcogen. We show that, in these crystals, the chalcogen-pnictogen interaction dominates over the pnictogen-pnictogen and pnictogen-π. In the latter, the role of donor and acceptor is reversed depending on the interacting moieties. Multiple chalcogen-pnictogen interactions necessitate larger donor atoms, such as sulfur. These observations explain and rationalize the experimentally observed crystal structures.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".