DELFI: A computer oracle for recommending the best density functional for excited states calculations
Bibliographic record
Abstract
Density functional theory is known as the workhorse of computational quantum chemistry. One of its main limitations, if not the main one, is that choosing the right functional to employ is a non-trivial task left for human experts. The choice is particularly hard for excited states calculations, when using the time-dependent formulation of DFT (TD-DFT). This is not only due to the approximations and limitations of the method, but also because the photophysical properties of a molecule are defined by a manifold of states that all need to be properly described in a balanced manner to obtain an accurate photochemical picture. This includes not only the relative energy of the states, but also capturing the correct character, order and intensity of the transitions. In this work, we developed a scoring system to quantitatively define the accuracy of an excited states calculation by simultaneously considering at the same time all these properties of a manifold of states. The scoring system is generalizable to any level of theory, we here applied it to a large dataset of organic molecules, calculating 38 scores for as many common functionals of different type and rung, against a higher accuracy method. We used these scores to train a graph attention neural network that is used to predict the 38 scores for molecules represented as 2D graphs. We call this oracle DELFI (Data-driven EvaLuation of Functionals by Inference), which can be used to predict the ranking of functionals to calculate optical properties of organic molecules. A corresponding web application allows to easily run DELFI and analyze the results, alleviating the hurdle of choosing the right functional for TD-DFT calculations.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".