New Quantum Chemistry Methods for Open-Shell Systems and Their Applications in Spin-Polarized Conceptual Density Functional Theory
Bibliographic record
Abstract
Motivated by our frustration with the lack of quantum chemistry methods for strongly-correlated open-shell systems, we develop quantitative methods for computing the electronic structure of such systems and qualitative tools for analyzing their chemical properties and reactivity. Specifically, we present a modern framework for performing sparse configuration interaction (CI) computations with arbitrary (Slater determinant) N-electron basis sets, using restricted or generalized spin-orbitals, and including computation of spin-polarized 1- and 2- electron reduced density matrices (RDMs). This framework is then used to implement the flexible ansätze for N-electron CI (FanCI) method more efficiently, via increased vectorization in the FanCI equations and use of sparse CI algorithms. We also extended the FanCI approach, including least-squares and stochastic optimization techniques, the computation of spin-polarized 1- and 2- electron RDMs, and transition energies (ionization potentials, electron affinities, and excitation energies). We use these tools to compare various open-shell CI methods and FanCI methods based on various antisymmetrized product of nonorthogonal geminals ansätze. To translate the vast amount of quantitative data present in the energies and (spin-polarized) density matrices of multiple open-shell states, we present a new, internally consistent and unambiguous framework for spin-polarized conceptual density-functional theory (SP-DFT) that reduces to a sensible formulation of spin-free CDFT in an appropriate limit. Using this framework, we were able to generalize the (non-spin-polarized) Parr function. We can also, using this framework, construct promolecules with proatoms having non-integer charges and multiplicities. Finally, we describe an equations-of-motion-based method for computing spin-polarized reactivity descriptors of a chemical system from only the ground state energy and the 1- and 2- electron RDMs from a single-point electronic structure computation, and show some benchmark computations for this method based on various CI and FanCI electronic structure methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".