Excited State Structure and Minimum Energy Conical Intersection Optimization Using DFT/MRCI
Bibliographic record
Abstract
The combined density functional theory and multi-reference configuration interaction (DFT/MRCI) method is a semi-empirical selected-CI electronic structure approach that is both computationally efficient and of predictive accuracy for the calculation of electronic excited states and simulation of electronic spectroscopies. However, given that the reference space is generated via selected-CI, a challenge arises in the construction of smooth potential energy surfaces. To address this issue, we treat the local discontinuities as noise within the Gaussian Progress Regression framework and learn the surfaces by explicitly optimizing a white-noise kernel. The characteristic polynomial coefficient surfaces, which are smooth functions of nuclear coordinates even at conical intersections, are learned in place of the adiabatic energy surfaces and are used to optimize the DFT/MRCI(2) minimum energy conical intersection geometries for representative intersection motifs in the molecules ethylene, butadiene, and fulvene. One consequence of explicitly treating the noise in the surfaces is that the energy difference cannot be made arbitrarily small at points of nominal intersection. Despite the limitations, however, we find the structures as well as the branching spaces to compare well with \textit{ab initio} MRCI and conclude that this approach is a viable method to learn a smooth representation of DFT/MRCI(2) surfaces.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".