Inhomogeneous Electron Gas
Open accessAbstract
This paper deals with the ground state of an interacting electron gas in an external potential $v(\mathrm{r})$. It is proved that there exists a universal functional of the density, $F[n(\mathrm{r})]$, independent of $v(\mathrm{r})$, such that the expression $E\ensuremath{\equiv}\ensuremath{\int}v(\mathrm{r})n(\mathrm{r})d\mathrm{r}+F[n(\mathrm{r})]$ has as its minimum value the correct ground-state energy associated with $v(\mathrm{r})$. The functional $F[n(\mathrm{r})]$ is then discussed for two situations: (1) $n(\mathrm{r})={n}_{0}+\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{n}(\mathrm{r})$, $\frac{\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{n}}{{n}_{0}}\ensuremath{\ll}1$, and (2) $n(\mathrm{r})=\ensuremath{\phi}(\frac{\mathrm{r}}{{r}_{0}})$ with $\ensuremath{\phi}$ arbitrary and ${r}_{0}\ensuremath{\rightarrow}\ensuremath{\infty}$. In both cases $F$ can be expressed entirely in terms of the correlation energy and linear and higher order electronic polarizabilities of a uniform electron gas. This approach also sheds some light on generalized Thomas-Fermi methods and their limitations. Some new extensions of these methods are presented.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Approximate methods including density functional theory can be inaccurate and fail dramatically in many situations of interest, like modeling the transition state of a reaction.
“Although many approximate methods exist, including the deep and broad literature concerning density functional theory [137,138], they can be inaccurate and even fail dramatically in many situations of interest, like modeling the transition state of a reaction [139].”
From The prospects of quantum computing in computational molecular biology (2021) · cites this paper as [137]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
On the Interaction of Electrons in Metals
Wigner, E. P. (1934) · Physical Review · doi:10.1103/physrev.46.1002 · Cited by 2,919 · see citing works
Correlation Energy of an Electron Gas at High Density
Gell‐Mann, M., & Brueckner, K. A. (1957) · Physical Review · doi:10.1103/physrev.106.364 · Cited by 1,456 · see citing works
XIV. The distribution of electrons round impurities in monovalent metals
Friedel, J. (1952) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440208561086 · Cited by 1,070 · see citing works
Correlation Energy of a Free Electron Gas
Nozières, P., & Pines, D. (1958) · Physical Review · doi:10.1103/physrev.111.442 · Cited by 613 · see citing works
The Thomas-Fermi approximation in quantum mechanics
March, N. H. (1957) · Advances In Physics · doi:10.1080/00018735700101156 · Cited by 462 · see citing works
The shielding of a fixed charge in a high-density electron gas
Langer, J. S., & Vosko, S. H. (1960) · Journal of Physics and Chemistry of Solids · doi:10.1016/0022-3697(60)90037-8 · Cited by 289 · see citing works
The Collective Treatment of a Fermi Gas: II
GASKELL, T. F. (1961) · Proceedings of the Physical Society · doi:10.1088/0370-1328/77/6/312 · Cited by 99 · see citing works
Green's Function Method for Quantum Corrections to the Thomas-Fermi Model of the Atom
Baraff, G. A., & Borowitz, S. (1961) · Physical Review · doi:10.1103/physrev.121.1704 · Cited by 82 · see citing works
The Collective Treatment of Many-body Systems: III
GASKELL, T. F. (1962) · Proceedings of the Physical Society · doi:10.1088/0370-1328/80/5/307 · Cited by 53 · see citing works
Fermi-Thomas Model with Correlations
Lewis, H. W. (1958) · Physical Review · doi:10.1103/physrev.111.1554 · Cited by 40 · see citing works
Correlation and Quantum Corrections in the Thomas-Fermi Model of the Atom
Baraff, G. A. (1961) · Physical Review · doi:10.1103/physrev.123.2087 · Cited by 17 · see citing works
Quasi-Classical Theory of Electron Correlations in Atoms
DuBois, D. F., & Kivelson, M. G. (1962) · Physical Review · doi:10.1103/physrev.127.1182 · Cited by 17 · see citing works
Flagged first, then by citations
Research works printed but not linked: 8
- [1]1. J. Friedel, Dr. R. Balian, and Dr. C. De Dominicis for valuable discussions. PHYSICAL REVIEW VOLUME 136, NUM BER 3 B 9 iVOVEM B ER 1964 Scattering of a High-Intensity, Low-Frequency Electromagnetic Wave by an Unbound Electron* ZOLTAN FRIED)
- [3]3. IosEPII H. EszRLvf. U. S. Naval Ordnance Laboratory, Silver Spring, Maryland (Received 15 June 1964) "Thomson" scattering of a high-intensity, low-frequency, circularly-polarized electromagnetic wave by a free electron is considered. We find that by neglecting radiative corrections and pair e6ects, the Feynman- Dyson perturbation expansion is summable, and the sum can be analytically continued in the form of a sum of continued fractions. By imposing the boundary conditions that at t = & ~the photons and target electron propagate as free particles, we obtain results which differ from those reported by Brown and Kibble and by Goldman. In particular our results dier in two aspects. The 6rst difference is in the kinematics; namely, we find no intensity-dependent frequency shift in the scattered photon. The second difference is in the dynamics; that is, we obtain a different expression for the scattering amplitude. Both of these changes originate in the choice of boundary conditions. Instead of treating the asymptotic radiation 6eld classically, we choose our states as linear combinations of occupation-number states. Finally, contact is made with the results of Brown and Kibble and of Goldman using a mixed set of classical and quantum boundary values. I. INTRODUCTION 'HE advent of masers and lasers has stimulated a great deal of interest in the interaction of intense electromagnetic 6elds with matter. This activity has been focused on three different aspects of the subject. First, a great deal of attention has been devoted to the dynamics of production of high-intensity light. A *A preliminary version of this work was presented at the Pasadena Meeting of the American Physical Society, Bull. Am. Phys. Soc. 8, 615 (1963).
- [4]4. ' J. R. Singer, %users (John Wiley R Sons, Inc. , New York, 1900);
- [5]5. F. Schwabl and W. Thirring (to be published); W. E. Lamb, Jr., Lecture Notes, Enrico Fermi International School of Physics, Varenna, 1963 (unpublished). second area of concentration is the question of proper description of the electromagnetic radiation emanating from a laser; i.e. , questions of coherence and correla- tion. ' And finally, the problem of interaction of laser light with matter has attracted considerable interest. ' It is this latter question to which we are devoting our- selves in this paper. The particular problem of immediate interest is the effect of the presence of the high-intensity field on the Compton (Thomson) scattering amplitude. Recall that the Thomson amplitude describes the scattering of a ' R. Glauber, Phys. Rev. 130, 2529 (1963);
- [6]6. E. C. G. Sudarshan, Phys. Rev. Letters 10, 277 (1963);
- [7]7. E. Wolf, Proc. Phys. Soc. (London) 80, 1269 (1962).
- [8]8. ~J. A. Armstrong, N. Bloembergen, J. Ducuing, and P. S. Pershan, Phys. Rev. 127, 1918 (1962);
- [9]9. Z. Fried s.nd W. M. Frank, Nuovo Cimento 27, 218 (1963).
Cited by
Sorted by citations
Projector augmented-wave method
Blöchl, P. E. (1994) · Physical Review B · doi:10.1103/physrevb.50.17953 · Cited by 92,732 · see citing works
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135 · see citing works
Self-citationComputer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945 · see citing works
Self-interaction correction to density-functional approximations for many-electron systems
Perdew, J. P., & Zunger, A. (1981) · Physical Review B · doi:10.1103/physrevb.23.5048 · Cited by 20,887 · see citing works
A new mixing of Hartree–Fock and local density-functional theories
Becke, A. D. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464304 · Cited by 16,515 · see citing works
Long-range corrected hybrid density functionals with damped atom–atom dispersion corrections
Chai, J.-D., & Head-Gordon, M. (2008) · Physical Chemistry Chemical Physics · doi:10.1039/b810189b · Cited by 14,648 · see citing works
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094 · see citing works
Density-Functional Theory for Time-Dependent Systems
Runge, E., & Gross, E. K. U. (1984) · Physical Review Letters · doi:10.1103/physrevlett.52.997 · Cited by 8,914 · see citing works
Advanced capabilities for materials modelling with Q <scp>uantum</scp> ESPRESSO
Giannozzi, P., Andreussi, O., Brumme, T., Bunau, O., Buongiorno Nardelli, M., Calandra, M., Car, R., Cavazzoni, C., Ceresoli, D., Cococcioni, M., et al. (2017) · Journal of Physics: Condensed Matter · doi:10.1088/1361-648x/aa8f79 · Cited by 8,361 · see citing works
Electronic properties of two-dimensional systems
Ando, T., Fowler, A. B., & Stern, F. (1982) · Reviews of Modern Physics · doi:10.1103/revmodphys.54.437 · Cited by 7,455 · see citing works
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968 · see citing works
Universality in Oxygen Evolution Electrocatalysis on Oxide Surfaces
Man, I. C., Su, H., Calle‐Vallejo, F., Hansen, H. A., Martínez, J. I., Inoglu, N. G., Kitchin, J., Jaramillo, T. F., Nørskov, J. K., & Rossmeisl, J. (2011) · ChemCatChem · doi:10.1002/cctc.201000397 · Cited by 4,769 · see citing works
Electronic excitations: density-functional versus many-body Green’s-function approaches
Onida, G., Reining, L., & Rubio, A. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.601 · Cited by 4,155 · see citing works
Theoretical surface science and catalysis—calculations and concepts
Hammer, B., & Nørskov, J. K. (2000) · Advances in Catalysis · doi:10.1016/s0360-0564(02)45013-4 · Cited by 3,972 · see citing works
Electron correlation in semiconductors and insulators: Band gaps and quasiparticle energies
Hybertsen, M. S., & Louie, S. G. (1986) · Physical Review B · doi:10.1103/physrevb.34.5390 · Cited by 3,963 · see citing works
Nanoalloys: From Theory to Applications of Alloy Clusters and Nanoparticles
Ferrando, R., Jellinek, J., & Johnston, R. L. (2008) · Chemical Reviews · doi:10.1021/cr040090g · Cited by 3,690 · see citing works
Optimized norm-conserving Vanderbilt pseudopotentials
Hamann, D. R. (2013) · Physical Review B · doi:10.1103/physrevb.88.085117 · Cited by 3,379 · see citing works
An Evaluation of Harmonic Vibrational Frequency Scale Factors
Merrick, J. P., Moran, D., & Radom, L. (2007) · The Journal of Physical Chemistry A · doi:10.1021/jp073974n · Cited by 2,735 · see citing works
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312 · see citing works
Mardirossian, N., & Head-Gordon, M. (2017) · Molecular Physics · doi:10.1080/00268976.2017.1333644 · Cited by 2,307 · see citing works
Kreuer, K.-D., Paddison, S. J., Spohr, E., & Schuster, M. (2004) · Chemical Reviews · doi:10.1021/cr020715f · Cited by 2,187 · see citing works
Quantum spin Hall effect in two-dimensional transition metal dichalcogenides
Qian, X., Liu, J., Fu, L., & Li, J. (2014) · Science · doi:10.1126/science.1256815 · Cited by 2,053 · see citing works
First-principles Phonon Calculations with Phonopy and Phono3py
Togo, A. (2023) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.92.012001 · Cited by 1,865 · see citing works
Structural properties of nanoclusters: Energetic, thermodynamic, and kinetic effects
Baletto, F., & Ferrando, R. (2005) · Reviews of Modern Physics · doi:10.1103/revmodphys.77.371 · Cited by 1,811 · see citing works
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2720168 · Cited by 1,752 · see citing works
Pseudopotentials for high-throughput DFT calculations
Garrity, K. F., Bennett, J. W., Rabe, K. M., & Vanderbilt, D. (2014) · Computational Materials Science · doi:10.1016/j.commatsci.2013.08.053 · Cited by 1,644 · see citing works
Reproducibility in density functional theory calculations of solids
Lejaeghere, K., Bihlmayer, G., Björkman, T., Blaha, P., Blügel, S., Blum, V., Caliste, D., Castelli, I. E., Clark, S. J., Dal Corso, A., et al. (2016) · Science · doi:10.1126/science.aad3000 · Cited by 1,649 · see citing works
<i>Colloquium</i> : Topological band theory
Bansil, A., Lin, H., & Das, T. (2016) · Reviews of Modern Physics · doi:10.1103/revmodphys.88.021004 · Cited by 1,587 · see citing works
Quantum Chemistry in the Age of Quantum Computing
Cao, Y., Romero, J., Olson, J. P., Degroote, M., Johnson, P. D., Kieferová, M., Kivlichan, I. D., Menke, T., Peropadre, B., Sawaya, N. P. D., et al. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00803 · Cited by 1,560 · see citing works
Quantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017) · Nature Communications · doi:10.1038/ncomms13890 · Cited by 1,445 · see citing works
Optimization algorithm for the generation of ONCV pseudopotentials
Schlipf, M., & Gygi, F. (2015) · Computer Physics Communications · doi:10.1016/j.cpc.2015.05.011 · Cited by 1,300 · see citing works
Materials discovery and design using machine learning
Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276 · see citing works
<i>Ab initio</i> random structure searching
Pickard, C. J., & Needs, R. J. (2011) · Journal of Physics: Condensed Matter · doi:10.1088/0953-8984/23/5/053201 · Cited by 1,302 · see citing works
Unsupervised word embeddings capture latent knowledge from materials science literature
Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Persson, K. A., Ceder, G., & Jain, A. (2019) · Nature · doi:10.1038/s41586-019-1335-8 · Cited by 1,123 · see citing works
The Variational Quantum Eigensolver: A review of methods and best practices
Tilly, J., Chen, H., Cao, S., Picozzi, D., Setia, K., Li, Y., Grant, E., Wossnig, L., Rungger, I., Booth, G. H., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2022.08.003 · Cited by 1,031 · see citing works
Wu, Z.-Y., Chen, F.-Y., Li, B., Yu, S.-W., Finfrock, Y. Z., Meira, D. M., Yan, Q.-Q., Zhu, P., Chen, M.-X., Song, T.-W., et al. (2023) · Nature Materials · doi:10.1038/s41563-022-01380-5 · Cited by 1,004 · see citing works
Second-order optical response in semiconductors
Sipe, J. E., & Shkrebtii, A. I. (2000) · Physical Review B · doi:10.1103/physrevb.61.5337 · Cited by 910 · see citing works
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852 · see citing works
Machine learning unifies the modeling of materials and molecules
Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. (2017) · Science Advances · doi:10.1126/sciadv.1701816 · Cited by 764 · see citing works
Power of data in quantum machine learning
Huang, H.-Y., Broughton, M., Mohseni, M., Babbush, R., Boixo, S., Neven, H., & McClean, J. R. (2021) · Nature Communications · doi:10.1038/s41467-021-22539-9 · Cited by 896 · see citing works
Liu, S., Li, C., Zachman, M. J., Zeng, Y., Yu, H., Li, B., Wang, M., Braaten, J., Liu, J., Meyer, H. M., et al. (2022) · Nature Energy · doi:10.1038/s41560-022-01062-1 · Cited by 733 · see citing works
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651 · see citing works
Theoretical Insights into Heterogeneous (Photo)electrochemical CO<sub>2</sub>Reduction
Xu, S., & Carter, E. A. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00481 · Cited by 652 · see citing works
Jinnouchi, R., Lahnsteiner, J., Karsai, F., Kresse, G., & Bokdam, M. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.225701 · Cited by 635 · see citing works
A Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609 · see citing works
Analysis of separable potentials
Gonze, X., Stumpf, R., & Scheffler, M. (1991) · Physical Review B · doi:10.1103/physrevb.44.8503 · Cited by 605 · see citing works
FIDELITY APPROACH TO QUANTUM PHASE TRANSITIONS
GU, S.-J. (2010) · International Journal of Modern Physics B · doi:10.1142/s0217979210056335 · Cited by 663 · see citing works
Random-phase approximation and its applications in computational chemistry and materials science
Ren, X., Rinke, P., Joas, C., & Scheffler, M. (2012) · Journal of Materials Science · doi:10.1007/s10853-012-6570-4 · Cited by 610 · see citing works
Hautier, G., Fischer, C. C., Jain, A., Mueller, T., & Ceder, G. (2010) · Chemistry of Materials · doi:10.1021/cm100795d · Cited by 580 · see citing works
Machine learning for perovskite materials design and discovery
Tao, Q., Xu, P., Li, M., & Lu, W. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00495-8 · Cited by 542 · see citing works
Shukla, P. K., & Eliasson, B. (2011) · Reviews of Modern Physics · doi:10.1103/revmodphys.83.885 · Cited by 477 · see citing works
Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity
Babbush, R., Gidney, C., Berry, D. W., Wiebe, N., McClean, J., Paler, A., Fowler, A., & Neven, H. (2018) · Physical Review X · doi:10.1103/physrevx.8.041015 · Cited by 383 · see citing works
Time-Reversal-Breaking Weyl Fermions in Magnetic Heusler Alloys
Wang, Z., Vergniory, M. G., Kushwaha, S., Hirschberger, M., Chulkov, E. V., Ernst, A., Ong, N. P., Cava, R. J., & Bernevig, B. A. (2016) · Physical Review Letters · doi:10.1103/physrevlett.117.236401 · Cited by 374 · see citing works
Generation and electric control of spin–valley-coupled circular photogalvanic current in WSe2
Yuan, H., Wang, X., Lian, B., Zhang, H., Fang, X., Shen, B., Xu, G., Xu, Y., Zhang, S.-C., Hwang, H. Y., et al. (2014) · Nature Nanotechnology · doi:10.1038/nnano.2014.183 · Cited by 373 · see citing works
Twist-averaged boundary conditions in continuum quantum Monte Carlo algorithms
Lin, C., Zong, F. H., & Ceperley, D. M. (2001) · Physical Review E · doi:10.1103/physreve.64.016702 · Cited by 371 · see citing works
Quantum hydrodynamic model for the nonlinear electron dynamics in thin metal films
Crouseilles, N., Hervieux, P.-A., & Manfredi, G. (2008) · Physical Review B · doi:10.1103/physrevb.78.155412 · Cited by 346 · see citing works
Gate-count estimates for performing quantum chemistry on small quantum computers
Wecker, D., Bauer, B., Clark, B. K., Hastings, M. B., & Troyer, M. (2014) · Physical Review A · doi:10.1103/physreva.90.022305 · Cited by 296 · see citing works
Quantum Computation of Electronic Transitions Using a Variational Quantum Eigensolver
Parrish, R. M., Hohenstein, E. G., McMahon, P. L., & Martínez, T. J. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.230401 · Cited by 282 · see citing works
Chen, M., Zheng, L., Santra, B., Ko, H.-Y., DiStasio Jr, R. A., Klein, M. L., Car, R., & Wu, X. (2018) · Nature Chemistry · doi:10.1038/s41557-018-0010-2 · Cited by 283 · see citing works
Schuch, N., & Verstraete, F. (2009) · Nature Physics · doi:10.1038/nphys1370 · Cited by 260 · see citing works
Huang, B., & von Lilienfeld, O. A. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4964627 · Cited by 294 · see citing works
Jain, A., Hautier, G., Ong, S. P., & Persson, K. (2016) · Journal of Materials Research · doi:10.1557/jmr.2016.80 · Cited by 244 · see citing works
Computational chemistry for water-splitting electrocatalysis
Miao, L., Jia, W., Cao, X., & Jiao, L. (2024) · Chemical Society Reviews · doi:10.1039/d2cs01068b · Cited by 241 · see citing works
Theoretical Investigations of the Oxygen Reduction Reaction on Pt(111)
Keith, J. A., Jerkiewicz, G., & Jacob, T. (2010) · ChemPhysChem · doi:10.1002/cphc.201000286 · Cited by 233 · see citing works
The prospects of quantum computing in computational molecular biology
Outeiral, C., Strahm, M., Shi, J., Morris, G. M., Benjamin, S. C., & Deane, C. M. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1481 · Cited by 232 · see citing works
Bayesian Error Estimation in Density-Functional Theory
Mortensen, J. J., Kaasbjerg, K., Frederiksen, S. L., Nørskov, J. K., Sethna, J. P., & Jacobsen, K. W. (2005) · Physical Review Letters · doi:10.1103/physrevlett.95.216401 · Cited by 208 · see citing works
Selective Heterogeneous CO<sub>2</sub> Electroreduction to Methanol
Back, S., Kim, H., & Jung, Y. (2015) · ACS Catalysis · doi:10.1021/cs501600x · Cited by 201 · see citing works
Larson, P., Greanya, V. A., Tonjes, W. C., Liu, R., Mahanti, S. D., & Olson, C. G. (2002) · Physical Review B · doi:10.1103/physrevb.65.085108 · Cited by 196 · see citing works
Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide
Sivaraman, G., Krishnamoorthy, A. N., Baur, M., Holm, C., Stan, M., Csányi, G., Benmore, C., & Vázquez-Mayagoitia, Á. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00367-7 · Cited by 194 · see citing works
Chang, G., Xu, S.-Y., Zheng, H., Singh, B., Hsu, C.-H., Bian, G., Alidoust, N., Belopolski, I., Sanchez, D. S., Zhang, S., et al. (2016) · Scientific Reports · doi:10.1038/srep38839 · Cited by 191 · see citing works
Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
Liu, Y., Tan, X., Liang, J., Han, H., Xiang, P., & Yan, W. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202214271 · Cited by 174 · see citing works
Recent Progress in Quantum Chemistry Modeling on the Pyrolysis Mechanisms of Lignocellulosic Biomass
Hu, B., Zhang, B., Xie, W.-L., Jiang, X.-Y., Liu, J., & Lu, Q. (2020) · Energy & Fuels · doi:10.1021/acs.energyfuels.0c01948 · Cited by 161 · see citing works
Quantum Algorithms to Simulate Many-Body Physics of Correlated Fermions
Jiang, Z., Sung, K. J., Kechedzhi, K., Smelyanskiy, V. N., & Boixo, S. (2018) · Physical Review Applied · doi:10.1103/physrevapplied.9.044036 · Cited by 175 · see citing works
Theoretical foundations of quantum hydrodynamics for plasmas
Moldabekov, Z. A., Bonitz, M., & Ramazanov, T. S. (2018) · Physics of Plasmas · doi:10.1063/1.5003910 · Cited by 159 · see citing works
DPA-2: a large atomic model as a multi-task learner
Zhang, D., Liu, X., Zhang, X., Zhang, C., Cai, C., Bi, H., Du, Y., Qin, X., Peng, A., Huang, J., et al. (2023) · arXiv · doi:10.48550/arxiv.2312.15492 · Cited by 155
Computational tools to model halogen bonds in medicinal chemistry.
Ford, M. C., & Ho, P. S. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b00997 · Cited by 143 · see citing works
Metal–organic framework-inspired metal-containing clusters for high-resolution patterning
Xu, H., Sakai, K., Kasahara, K., Kosma, V., Yang, K., Herbol, H. C., Odent, J., Clancy, P., Giannelis, E. P., & Ober, C. K. (2018) · Chemistry of Materials · doi:10.1021/acs.chemmater.8b01573 · Cited by 129 · see citing works
First Principles Peierls-Boltzmann Phonon Thermal Transport: A Topical Review
Lindsay, L. (2016) · Nanoscale and Microscale Thermophysical Engineering · doi:10.1080/15567265.2016.1218576 · Cited by 129 · see citing works
Johnson, T. H., Clark, S. R., & Jaksch, D. (2014) · EPJ Quantum Technology · doi:10.1140/epjqt10 · Cited by 127 · see citing works
Urazhdin, S., Bilc, D., Mahanti, S. D., Tessmer, S. H., Kyratsi, T., & Kanatzidis, M. G. (2004) · Physical Review B · doi:10.1103/physrevb.69.085313 · Cited by 122 · see citing works
Metallic Sandwiched-Aerogel Hybrids Enabling Flexible and Stretchable Intelligent Sensor
Zhang, H., Han, W., Xu, K., Zhang, Y., Lu, Y., Nie, Z., Du, Y., Zhu, J., & Huang, W. (2020) · Nano Letters · doi:10.1021/acs.nanolett.0c00372 · Cited by 122 · see citing works
Cao, L., Raciti, D., Li, C., Livi, K. J. T., Rottmann, P. F., Hemker, K. J., Mueller, T., & Wang, C. (2017) · ACS Catalysis · doi:10.1021/acscatal.7b03107 · Cited by 122 · see citing works
Embedded-atom-method tantalum potential developed by the force-matching method
Li, Y., Siegel, D. J., Adams, J. B., & Liu, X.-Y. (2003) · Physical Review B · doi:10.1103/physrevb.67.125101 · Cited by 118 · see citing works
Water Formation on Pt and Pt‐based Alloys: A Theoretical Description of a Catalytic Reaction
Jacob, T., & Goddard, W. A. (2006) · ChemPhysChem · doi:10.1002/cphc.200500613 · Cited by 115 · see citing works
Quantum hydrodynamics for plasmas—<i>Quo vadis</i>?
Bonitz, M., Moldabekov, Z. A., & Ramazanov, T. S. (2019) · Physics of Plasmas · doi:10.1063/1.5097885 · Cited by 110 · see citing works
Competition between ferromagnetism and antiferromagnetism in FePt
Brown, G., Kraczek, B., Janotti, A., Schulthess, T. C., Stocks, G. M., & Johnson, D. D. (2003) · Physical Review B · doi:10.1103/physrevb.68.052405 · Cited by 86 · see citing works
Hydrogen Evolution Reaction Activity of Heterogeneous Materials: A Theoretical Model
Ekspong, J., Gracia-Espino, E., & Wågberg, T. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c05243 · Cited by 87 · see citing works
Lin, Z., Kohn, A. W., & Van Voorhis, T. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.9b08292 · Cited by 78 · see citing works
Prospects of quantum computing for molecular sciences
Liu, H., Low, G. H., Steiger, D. S., Häner, T., Reiher, M., & Troyer, M. (2022) · Materials Theory · doi:10.1186/s41313-021-00039-z · Cited by 73 · see citing works
Quantum-circuit design for efficient simulations of many-body quantum dynamics
Raeisi, S., Wiebe, N., & Sanders, B. C. (2012) · New Journal of Physics · doi:10.1088/1367-2630/14/10/103017 · Cited by 72 · see citing works
Computational complexity in electronic structure
Whitfield, J. D., Love, P. J., & Aspuru-Guzik, A. (2013) · Phys. Chem. Chem. Phys. · doi:10.1039/c2cp42695a · Cited by 71 · see citing works
Zenasni, H., Faraoun, H. I., & Esling, C. (2013) · Journal of Magnetism and Magnetic Materials · doi:10.1016/j.jmmm.2013.01.003 · Cited by 65 · see citing works
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
Kranz, J. J., Kubillus, M., Ramakrishnan, R., von Lilienfeld, O. A., & Elstner, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00933 · Cited by 63 · see citing works
Ultrahigh-printing-speed photoresists for additive manufacturing
Liu, T., Tao, P., Wang, X., Wang, H., He, M., Wang, Q., Cui, H., Wang, J., Tang, Y., Tang, J., et al. (2024) · Nature Nanotechnology · doi:10.1038/s41565-023-01517-w · Cited by 63 · see citing works
Hubbard-<i>U</i>band-structure methods
Albers, R. C., Christensen, N. E., & Svane, A. (2009) · Journal of Physics: Condensed Matter · doi:10.1088/0953-8984/21/34/343201 · Cited by 63 · see citing works
Exchange effects in Coulomb quantum plasmas: Dispersion of waves in 2D and 3D quantum plasmas
Andreev, P. A. (2014) · Annals of Physics · doi:10.1016/j.aop.2014.07.019 · Cited by 55 · see citing works
Chen, X., Wang, C., Li, Z., Hou, Z., & Yin, W.-J. (2020) · Science China Materials · doi:10.1007/s40843-019-1255-4 · Cited by 47 · see citing works
Quantifying the effect of gate errors on variational quantum eigensolvers for quantum chemistry
Dalton, K., Long, C. K., Yordanov, Y. S., Smith, C. G., Barnes, C. H. W., Mertig, N., & Arvidsson-Shukur, D. R. M. (2024) · npj Quantum Information · doi:10.1038/s41534-024-00808-x · Cited by 43 · see citing works
Exchange effects in plasmas: The case of low-frequency dynamics
Zamanian, J., Marklund, M., & Brodin, G. (2013) · Physical Review E · doi:10.1103/physreve.88.063105 · Cited by 41 · see citing works
Poudel, L., Podgornik, R., & Ching, W.-Y. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b04061 · Cited by 41 · see citing works
- Authors
- Hohenberg, P., & Kohn, W.
- Affiliations
- École Normale Supérieure - PSL (FR)
- Venue
- Physical Review
- Publisher
- American Physical Society (APS)
- Published
- 1964-11-09
- Volume
- 136
- Issue
- 3B
- Pages
- B864-B871
- ISSN
- 0031-899X (ISSN-L)
- OpenAlex
- W285749399
- Cited by
- 53,541
- References
- 12
- Citations by year
- 2026: 2271 · 2025: 2819 · 2024: 2811 · 2023: 2626 · 2022: 2663 · 2021: 2559 · 2020: 2663 · 2019: 2346 · 2018: 2274 · 2017: 2244 · 2016: 2068 · 2015: 1923 · 2014: 1875 · 2013: 1742 · 2012: 1594
- FWCI
- 4.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physical Review (publishedVersion)
- Defense Technical Information Center (DTIC) (submittedVersion)
- BSU Digital Library (Belarusian State University) (submittedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Atomic and Molecular Physics
- Physical Sciences › Engineering › Mechanical Engineering › Thermodynamic and Structural Properties of Metals and Alloys
- Keywords
- inhomogeneous electron gas, ground state energy, external potential, correlation energy, electronic polarizability, jellium model, Thomas-Fermi approximation, density functional theory
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review