Recent Progress in Orbital-free Density Functional Theory
Bibliographic record
Abstract
Part 1: Density Functional for the Kinetic Energy and Its Applications in Orbital-Free DFT Simulations: From the Hohenberg-Kohn Theory to the Kohn-Sham Equations (Y A Wang & P Xiang) Accurate Computation of the Non-Interacting Kinetic Energy from Electron Densities (F A Bulat & W Yang) The Single-Particle Kinetic Energy of Many-Fermion Systems: Transcending the Thomas-Fermi plus Von Weizsacker Method (G G N Angilella & N H March) An Orbital Free ab initio Method: Applications to Liquid Metals and Clusters (A Aguado, D J Gonzalez, L E Gonzalez, J M Lopez, S Nunez & M J Stott) Electronic Structure Calculations at Macroscopic Scales Using Orbital-Free DFT (B G Radhakrishnan & V Gavini) Properties of Hot and Dense Matter by Orbital-Free Molecular Dynamics (F Lambert, J Clerouin, J-F Danel, L Kazandjian & S Mazevet) Shell-Correction and Orbital-Free Density-Functional Methods for Finite Systems (C Yannouleas & U Landman) Finite Element Approximations in Orbital-Free Density Functional Theory (H Chen & A Zhou) Part 2: The Functional for the Non-Additive Kinetic Energy and Its Applications in Numerical Simulations: Non-Additive Kinetic Energy and Potential in Analytically Solvable Systems and Their Approximated Counterparts (T A Wesolowski & A Savin) Towards the Description of Covalent Bonds in Subsystem Density-Functional Theory (Ch R Jacob & L Visscher) Orbital-Free Embedding Calculations of Electronic Spectra (J Neugebauer) On the Principal Difference Between the Exact and Approximate Frozen-Density Embedding Theory (O V Gritsenko) Part 3: Kinetic Energy Functional and Information Theory: Analytic Approach and Monte Carlo Sampling for Electron Correlations (L M Ghiringhelli & L Delle Site) Kinetic Energy and Fisher Information (A Nagy) Quantum Fluctuations, Dequantization, Information Theory and Kinetic-Energy Functionals (I P Hamilton, R A Mosna & L Delle Site) Part 4: Appendix: Semilocal Approximations for the Kinetic Energy (F Tran & T A Wesolowski).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".