uMBD: A Materials-Ready Dispersion Correction that Uniformly Treats Metallic, Ionic, Covalent, and van der Waals Bonding
Bibliographic record
Abstract
Materials design increasingly relies on first-principles calculations for screening important candidates and for understanding quantum mechanisms. Density functional theory (DFT) is by far the most popular first-principles approach due to its efficiency and accuracy. However, to accurately predict structures and thermodynamics, DFT must be paired with a van der Waals (vdW) dispersion correction. Therefore, such corrections have been the subject of intense scrutiny in recent years. Despite significant successes in organic molecules, no existing model can adequately cover the full range of common materials, from metals to ionic solids, hampering the applications of DFT for modern problems such as battery design. Here, we introduce a universally optimized vdW-corrected DFT method that demonstrates an unbiased reliability for predicting molecular, layered, ionic, metallic, and hybrid materials without incurring a large computational overhead. We use our method to accurately predict the intercalation potentials of layered electrode materials of a Li-ion battery system – a problem for which the existing state-of-the-art methods fail. Thus, we envisage broad use of our method in the design of chemo-physical processes of new materials.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".