<scp>MRSF</scp> ‐ <scp>TDDFT</scp> : A new tool in quantum chemistry for better understanding molecules and materials
Bibliographic record
Abstract
Abstract Quantum chemical theories are essential tools for predicting the properties of complex quantum systems without the need for prior empirical data. While traditional theories have long dominated the field, their applicability is often limited in complex scenarios, particularly for systems involving excited states. Mixed‐Reference Spin‐Flip Time‐Dependent Density Functional Theory (MRSF‐TDDFT) addresses these challenges, offering a robust, accurate, and computationally efficient framework for studying both ground and excited states of large molecular systems. MRSF‐TDDFT achieves predictive accuracy on par with much more computationally intensive quantum chemical methods. Notably, it successfully describes the doubly excited states, a limitation of conventional TDDFT, by naturally incorporating key doubly excited configurations within its response space. This capability also enables MRSF‐TDDFT to accurately reproduce the correct asymptotic behavior of bond‐breaking potential energy surfaces. Furthermore, it resolves critical photochemical features, such as the conical intersections, which elude both TDDFT and Complete Active Space Self‐Consistent Field (CASSCF) methods. Despite its advanced predictive power, MRSF‐TDDFT retains computational efficiency comparable to traditional TDDFT. With the development of custom‐tailored functionals, its accuracy can be further enhanced, extending its potential applications. This innovation represents a significant advancement, empowering researchers to uncover intricate molecular behaviors and facilitate the design of novel materials with unprecedented precision.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.011 |
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".