Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals

Generalized Gradient Approximation Made Simple

Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824

Density-functional thermochemistry. III. The role of exact exchange

Becke, A. D. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464913 · Cited by 103,767

Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density

Lee, C., Yang, W., & Parr, R. G. (1988) · Physical Review B · doi:10.1103/physrevb.37.785 · Cited by 100,874

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

A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu

Grimme, S., Antony, J., Ehrlich, S., & Krieg, H. (2010) · The Journal of Chemical Physics · doi:10.1063/1.3382344 · Cited by 59,686

Density-functional exchange-energy approximation with correct asymptotic behavior

Becke, A. D. (1988) · Physical Review A · doi:10.1103/physreva.38.3098 · Cited by 54,100

Inhomogeneous Electron Gas

Hohenberg, P., & Kohn, W. (1964) · Physical Review · doi:10.1103/physrev.136.b864 · Cited by 53,541

Semiempirical GGA‐type density functional constructed with a long‐range dispersion correction

Grimme, S. (2006) · Journal of Computational Chemistry · doi:10.1002/jcc.20495 · Cited by 31,214

The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals

Zhao, Y., & Truhlar, D. G. (2007) · Theoretical Chemistry Accounts · doi:10.1007/s00214-007-0310-x · Cited by 30,666

Effect of the damping function in dispersion corrected density functional theory

Grimme, S., Ehrlich, S., & Goerigk, L. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21759 · Cited by 28,221

Selective adsorption of arsenic over phosphate by transition metal cross-linked chitosan

Pincus, L. N., Petrović, P. V., Gonzalez, I. S., Stavitski, E., Fishman, Z. S., Rudel, H. E., Anastas, P. T., & Zimmerman, J. B. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.128582 · Cited by 100

Finding chemical concepts in the Hilbert space: Coupled cluster analyses of noncovalent interactions

Bistoni, G. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1442 · Cited by 97

A Polymer with Mechanochemically Active Hidden Length

Tian, Y., Cao, X., Li, X., Zhang, H., Sun, C., Xu, Y., Weng, W., Zhang, W., & Boulatov, R. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c09220 · Cited by 97

Computational approaches to dissociative chemisorption on metals: towards chemical accuracy

Kroes, G. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d1cp00044f · Cited by 96

The Local Vibrational Mode Theory and Its Place in the Vibrational Spectroscopy Arena

Kraka, E., Quintano, M. M., Force, H. L., Antonio, J. J., & Freindorf, M. (2022) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.2c05962 · Cited by 97

Atomic structures and orbital energies of 61,489 crystal-forming organic molecules

Stuke, A., Künkel, C., Golze, D., Todorović, M., Margraf, J. T., Reuter, K., Rinke, P., & Oberhofer, H. (2020) · Scientific Data · doi:10.1038/s41597-020-0385-y · Cited by 100

Accurate prediction of the properties of materials using the CAM‐B3LYP density functional

Li, M., Reimers, J. R., Ford, M. J., Kobayashi, R., & Amos, R. D. (2021) · Journal of Computational Chemistry · doi:10.1002/jcc.26558 · Cited by 97

Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations

Miksch, A. M., Morawietz, T., Kästner, J., Urban, A., & Artrith, N. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abfd96 · Cited by 96

Computational Studies of Rubber Ozonation Explain the Effectiveness of 6PPD as an Antidegradant and the Mechanism of Its Quinone Formation

Rossomme, E. C., Hart‐Cooper, W., Orts, W. J., McMahan, C. M., & Head‐Gordon, M. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.2c08717 · Cited by 94

Site-Specific Bioconjugation through Enzyme-Catalyzed Tyrosine–Cysteine Bond Formation

Lobba, M. J., Fellmann, C., Marmelstein, A. M., Maza, J. C., Kissman, E. N., Robinson, S. A., Staahl, B. T., Urnes, C., Lew, R. J., Mogilevsky, C. S., et al. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00940 · Cited by 97

Intrinsic charge trapping in amorphous oxide films: status and challenges

Strand, J. W., Kaviani, M., Gao, D. Z., El-Sayed, A.-M. B., Afanas'ev, V. V., & Shluger, A. L. (2018) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/aac005 · Cited by 92

Mechanisms and Dynamics of Reactions Involving Entropic Intermediates

Yang, Z. J., Jamieson, C. S., Xue, X., Garcia‐Borràs, M., Benton, T. R., Dong, X., Liu, F., & Houk, K. N. (2019) · Trends in Chemistry · doi:10.1016/j.trechm.2019.01.009 · Cited by 90

Accurate quantum chemical energies for 133 000 organic molecules

Narayanan, B., Redfern, P. C., Assary, R. S., & Curtiss, L. A. (2019) · Chemical Science · doi:10.1039/c9sc02834j · Cited by 91

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model

Duan, C., Du, Y., Jia, H., & Kulik, H. J. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00563-7 · Cited by 97

Noncovalent interactions: A brief account of a long history

Schneider, H. (2022) · Journal of Physical Organic Chemistry · doi:10.1002/poc.4340 · Cited by 89

Benchmarking Magnetizabilities with Recent Density Functionals

Lehtola, S., Dimitrova, M., Fliegl, H., & Sundholm, D. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c01190 · Cited by 88

Multidimensional Lewis Acidity: A Consistent Data Set of Chloride, Hydride, Methide, Water and Ammonia Affinities for 183 p‐Block Element Lewis Acids

Erdmann, P., & Greb, L. (2021) · ChemPhysChem · doi:10.1002/cphc.202100150 · Cited by 88

BH9, a New Comprehensive Benchmark Data Set for Barrier Heights and Reaction Energies: Assessment of Density Functional Approximations and Basis Set Incompleteness Potentials

Prasad, V. K., Pei, Z., Edelmann, S., Otero‐de‐la‐Roza, A., & DiLabio, G. A. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00694 · Cited by 91

Self-assembled dendrimer polyamide nanofilms with enhanced effective pore area for ion separation

Yuan, B., Zhang, Y., Qi, P., Yang, D., Hu, P., Zhao, S., Zhang, K., Zhang, X., Meng, Y., Cui, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44530-2 · Cited by 88

Controlling Stereoselectivity with Noncovalent Interactions in Chiral Phosphoric Acid Organocatalysis

Betinol, I. O., Kuang, Y., Mulley, B. P., & Reid, J. P. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00869 · Cited by 88

Density-Corrected DFT Explained: Questions and Answers

Song, S., Vuckovic, S., Sim, E., & Burke, K. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01045 · Cited by 87

Extension of the D3 and D4 London dispersion corrections to the full actinides series

Wittmann, L., Gordiy, I., Friede, M., Helmich‐Paris, B., Grimme, S., Hansen, A., & Bursch, M. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp01514b · Cited by 91

Range-Separated Double-Hybrid Functional from Nonempirical Constraints

Brémond, É. A. G., Savarese, M., Pérez-Jiménez, Á. J., Sancho-Garcı́a, J. C., & Adamo, C. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00261 · Cited by 88

Low-Order Scaling G 0 W 0 by Pair Atomic Density Fitting

Förster, A., & Visscher, L. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00693 · Cited by 90

Performance of curing epoxy resin as potential anticorrosive coating for carbon steel in 3.5% NaCl medium: Combining experimental and computational approaches

Hsissou, R., Benhiba, F., Echihi, S., Benzidia, B., Cherrouf, S., Haldhar, R., Alvi, P. A., Kaya, S., Serdaroğlu, G., & Zarrouk, A. M. (2021) · Chemical Physics Letters · doi:10.1016/j.cplett.2021.139081 · Cited by 86

Deorbitalization strategies for meta-generalized-gradient-approximation exchange-correlation functionals

Mejı́a-Rodrı́guez, D., & Trickey, S. B. (2017) · Physical Review A · doi:10.1103/physreva.96.052512 · Cited by 86

A review on non‐relativistic, fully numerical electronic structure calculations on atoms and diatomic molecules

Lehtola, S. (2019) · International Journal of Quantum Chemistry · doi:10.1002/qua.25968 · Cited by 93

Chemical Bond Energies of 3d Transition Metals Studied by Density Functional Theory

Moltved, K. A., & Kepp, K. P. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00143 · Cited by 85

Mechanistic Insights into Co and Fe Quaterpyridine-Based CO 2 Reduction Catalysts: Metal–Ligand Orbital Interaction as the Key Driving Force for Distinct Pathways

Loipersberger, M., Cabral, D. G. A., Chu, D. B. K., & Head‐Gordon, M. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.0c09380 · Cited by 84

Machine learning activation energies of chemical reactions

Lewis‐Atwell, T., Townsend, P. A., & Grayson, M. N. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1593 · Cited by 85

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Aldossary, A., Campos-Gonzalez-Angulo, J. A., Pablo‐García, S., Leong, S. X., Rajaonson, E. M., Thiede, L., Tom, G., Wang, A., Avagliano, D., & Aspuru‐Guzik, A. (2024) · Advanced Materials · doi:10.1002/adma.202402369 · Cited by 92

Meta-GGA performance in solids at almost GGA cost

Mejı́a-Rodrı́guez, D., & Trickey, S. B. (2020) · Physical review. B./Physical review. B · doi:10.1103/physrevb.102.121109 · Cited by 83

Evaluation of density functional theory for a large and diverse set of organic and inorganic equilibrium structures

Karton, A., & Spackman, P. R. (2021) · Journal of Computational Chemistry · doi:10.1002/jcc.26698 · Cited by 83

Density Functional Theory for Molecule–Metal Surface Reactions: When Does the Generalized Gradient Approximation Get It Right, and What to Do If It Does Not

Gerrits, N., Smeets, E. W. F., Vuckovic, S., Powell, A. D., Doblhoff-Dier, K., & Kroes, G. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02452 · Cited by 81

Accurate Reduced-Cost CCSD(T) Energies: Parallel Implementation, Benchmarks, and Large-Scale Applications

Gyevi‐Nagy, L., Kállay, M., & Nagy, P. R. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c01077 · Cited by 85

Molecular design of redox carriers for electrochemical CO 2 capture and concentration

Barlow, J. M., Clarke, L. E., Zhang, Z., Bím, D., Ripley, K. M., Zito, A., Brushett, F. R., Alexandrova, A. N., & Yang, J. Y. (2022) · Chemical Society Reviews · doi:10.1039/d2cs00367h · Cited by 85

Computational Studies of Aflatoxin B1 (AFB1): A Review

Martínez, J. O., Rodríguez, M. H., Méndez‐Albores, A., Téllez‐Isaías, G., Jiménez, E. M., Nicolás‐Vázquez, M. I., & Miranda, R. (2023) · Toxins · doi:10.3390/toxins15020135 · Cited by 81

On the Computational Characterization of Charge-Transfer Effects in Noncovalently Bound Molecular Complexes

Mao, Y., Ge, Q., Horn, P. R., & Head‐Gordon, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b01256 · Cited by 81

Integral-Direct and Parallel Implementation of the CCSD(T) Method: Algorithmic Developments and Large-Scale Applications

Gyevi‐Nagy, L., Kállay, M., & Nagy, P. R. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00957 · Cited by 83

Accuracy and Reliability in the Simulation of Vibrational Spectra: A Comprehensive Benchmark of Energies and Intensities Issuing From Generalized Vibrational Perturbation Theory to Second Order (GVPT2)

Yang, Q., Mendolicchio, M., Barone, V., & Bloino, J. (2021) · Frontiers in Astronomy and Space Sciences · doi:10.3389/fspas.2021.665232 · Cited by 80

Assessment of Initial Guesses for Self-Consistent Field Calculations. Superposition of Atomic Potentials: Simple yet Efficient

Lehtola, S. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b01089 · Cited by 88

Origin of the Immiscibility of Alkanes and Perfluoroalkanes

Pollice, R., & Chen, P. (2019) · Journal of the American Chemical Society · doi:10.1021/jacs.8b10745 · Cited by 79

Accurate and Efficient ab Initio Calculations for Supramolecular Complexes: Symmetry-Adapted Perturbation Theory with Many-Body Dispersion

Carter-Fenk, K., Lao, K. U., Liu, K., & Herbert, J. M. (2019) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.9b01156 · Cited by 79

Binuclear Pd(I)–Pd(I) Catalysis Assisted by Iodide Ligands for Selective Hydroformylation of Alkenes and Alkynes

Zhang, Y., Torker, S., Sigrist, M., Bregović, N., & Dydio, P. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c09254 · Cited by 79

Twenty Years of Auxiliary-Field Quantum Monte Carlo in Quantum Chemistry: An Overview and Assessment on Main Group Chemistry and Bond-Breaking

Lee, J., Pham, H. Q., & Reichman, D. R. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00802 · Cited by 79

Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes

Simm, G. N. C., & Reiher, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00504 · Cited by 82

The Lewis electron-pair bonding model: the physical background, one century later

Zhao, L., Schwarz, W. H. E., & Frenking, G. (2019) · Nature Reviews Chemistry · doi:10.1038/s41570-018-0052-4 · Cited by 78

Point-defect-optimized electron distribution for enhanced electrocatalysis: Towards the perfection of the imperfections

Jiao, S., Fu, X., Zhang, L., Zeng, Y. J., & Huang, H. (2020) · Nano Today · doi:10.1016/j.nantod.2019.100833 · Cited by 78

Consistent inclusion of continuum solvation in energy decomposition analysis: theory and application to molecular CO 2 reduction catalysts

Mao, Y., Loipersberger, M., Kron, K. J., Derrick, J. S., Chang, C. J., Sharada, S. M., & Head‐Gordon, M. (2020) · Chemical Science · doi:10.1039/d0sc05327a · Cited by 78

New organosulfur metallic compounds as potent drugs: synthesis, molecular modeling, spectral, antimicrobial, drug likeness and DFT analysis

Hassan, A. U., Sumrra, S. H., Zafar, M. N., Nazar, M. F., Mughal, E. U., Zafar, M. N., & Iqbal, M. (2021) · Molecular Diversity · doi:10.1007/s11030-020-10157-4 · Cited by 78

Electronic structure of cesium-based photocathode materials from density functional theory: performance of PBE, SCAN, and HSE06 functionals

Saßnick, H., & Cocchi, C. (2021) · Electronic Structure · doi:10.1088/2516-1075/abfb08 · Cited by 78

Building a Toolbox for the Analysis and Prediction of Ligand and Catalyst Effects in Organometallic Catalysis

Durand, D. J., & Fey, N. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00807 · Cited by 77

Revisiting the structural, electronic and photocatalytic properties of Ti and Zr based perovskites with meta-GGA functionals of DFT

Zulfiqar, W., Alay-e-Abbas, S. M., Abbas, G., Laref, A., Larsson, J. A., & Shaukat, A. (2021) · Journal of Materials Chemistry C · doi:10.1039/d0tc05964a · Cited by 77

The quaternary ammonium salts as corrosion inhibitors for X65 carbon steel under sour environment in NACE 1D182 solution: Experimental and computational studies

Iravani, D., Esmaeili, N., Бериша, А., Akbarinezhad, E., & Aliabadi, M. H. (2022) · Colloids and Surfaces A Physicochemical and Engineering Aspects · doi:10.1016/j.colsurfa.2022.130544 · Cited by 77

First-principles calculations of hybrid inorganic–organic interfaces: from state-of-the-art to best practice

Hofmann, O. T., Zojer, E., Hörmann, L., Jeindl, A., & Maurer, R. J. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp06605b · Cited by 75

Contribution to the corrosion inhibition of C38 steel in 1 M hydrochloric acid medium by a new epoxy resin PGEPPP

(2021) · International Journal of Corrosion and Scale Inhibition · doi:10.17675/2305-6894-2021-10-1-23 · Cited by 75

New epoxy composite polymers as a potential anticorrosive coatings for carbon steel in 3.5% NaCl solution: Experimental and computational approaches

Hsissou, R., Benhiba, F., Echihi, S., Benkhaya, S., Hilali, M., Бериша, А., Briche, S., Zarrouk, A. M., Nouneh, K., & Elharfi, A. (2020) · Chemical Data Collections · doi:10.1016/j.cdc.2020.100619 · Cited by 73

From NWChem to NWChemEx: Evolving with the Computational Chemistry Landscape

Kowalski, K., Bair, R. A., Bauman, N. P., Boschen, J. S., Bylaska, E. J., Daily, J. A., de Jong, W. A., Dunning, T. H., Govind, N., Harrison, R. J., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00998 · Cited by 78

High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions

Spiekermann, K., Pattanaik, L., & Green, W. H. (2022) · Scientific Data · doi:10.1038/s41597-022-01529-6 · Cited by 76

Energy-Screened Many-Body Expansion: A Practical Yet Accurate Fragmentation Method for Quantum Chemistry

Liu, K., & Herbert, J. M. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b01095 · Cited by 72

Piecewise linearity, freedom from self-interaction, and a Coulomb asymptotic potential: three related yet inequivalent properties of the exact density functional

Kronik, L., & Kümmel, S. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp02564j · Cited by 72

A first-principles roadmap and limits to design efficient supercapacitor electrode materials

Ali, B. A., & Allam, N. K. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp02614b · Cited by 71

A Systematic Study of DFT Performance for Geometry Optimizations of Ionic Liquid Clusters

Seeger, Z. L., & Izgorodina, E. I. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00549 · Cited by 73

Exploring Avenues beyond Revised DSD Functionals: I. Range Separation, with xDSD as a Special Case

Santra, G., Cho, M., & Martin, J. M. L. (2021) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.1c01294 · Cited by 73

Insight into the corrosion inhibition of new bis-quinolin-8-ols derivatives as highly efficient inhibitors for C35E steel in 0.5 M H2SO4

Faydy, M. E., About, H., Warad, I., Kerroum, Y., Бериша, А., Podvorica, F. I., Bentiss, F., Kaichouh, G., Lakhrissi, B., & Zarrouk, A. M. (2021) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2021.117333 · Cited by 71

Removal of cationic dye from coloured water by adsorption onto hematite-humic acid composite: Experimental and theoretical studies

Gaayda, J. E., Titchou, F. E., Oukhrib, R., Karmal, I., Oualid, H. A., Бериша, А., Zazou, H., Swanson, C. H., Hamdani, M., & Akbour, R. A. (2022) · Separation and Purification Technology · doi:10.1016/j.seppur.2022.120607 · Cited by 71

Exploiting π and Chalcogen Interactions for the β‐Selective Glycosylation of Indoles through Glycal Conformational Distortion

Guo, H. C., Kirchhoff, J., Carsten Strohmann, B., Grabe, B., & Loh, C. C. J. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202316667 · Cited by 71

Quantum chemical package Jaguar: A survey of recent developments and unique features

Cao, Y., Balduf, T., Beachy, M. D., Bennett, M. C., Bochevarov, A. D., Chien, A. D., Dub, P. A., Dyall, K. G., Furness, J. W., Halls, M. D., et al. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0213317 · Cited by 72

State-of-the-art local correlation methods enable affordable gold standard quantum chemistry for up to hundreds of atoms

Nagy, P. R. (2024) · Chemical Science · doi:10.1039/d4sc04755a · Cited by 71

An enzymatic Alder-ene reaction

Ōhashi, M., Jamieson, C. S., Cai, Y., Tan, D., Kanayama, D., Tang, M., Anthony, S. M., Chari, J. V., Barber, J. S., Picazo, E., et al. (2020) · Nature · doi:10.1038/s41586-020-2743-5 · Cited by 71

Modeling chemical reactions on surfaces: The roles of chemical bonding and van der Waals interactions

Su, G., Yang, S., Jiang, Y., Li, J., Li, S., Ren, J., & Liu, W. (2019) · Progress in Surface Science · doi:10.1016/j.progsurf.2019.100561 · Cited by 70

Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule Activation

Gu, G. H., Choi, C., Lee, Y., Situmorang, A. B., Noh, J., Kim, Y., & Jung, Y. (2020) · Advanced Materials · doi:10.1002/adma.201907865 · Cited by 72

On the top rung of Jacob's ladder of density functional theory: Toward resolving the dilemma of SIE and NCE

Zhang, I. Y., & Xu, X. (2020) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1490 · Cited by 71

CHAL336 Benchmark Set: How Well Do Quantum-Chemical Methods Describe Chalcogen-Bonding Interactions?

Mehta, N., Fellowes, T., White, J. M., & Goerigk, L. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00006 · Cited by 70

Development of an Advanced Force Field for Water Using Variational Energy Decomposition Analysis

Das, A. K., Urban, L., Leven, I., Loipersberger, M., Aldossary, A., Head‐Gordon, M., & Head-Gordon, T. L. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00478 · Cited by 70

Platinum, gold, and silver standards of intermolecular interaction energy calculations

Kodrycka, M., & Patkowski, K. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5116151 · Cited by 69

Recent advancements in the cathodic catalyst for the hydrogen evolution reaction in microbial electrolytic cells

Savla, N., Guin, M., Pandit, S., Malik, H. N., Khilari, S., Mathuriya, A. S., Gupta, P. K., Thapa, B. S., Rambabu, B., & Jung, S. P. (2022) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2022.03.058 · Cited by 69

Model Chemistry Recommendations for Scaled Harmonic Frequency Calculations: A Benchmark Study

Trujillo, J. C. Z., & McKemmish, L. K. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.2c06908 · Cited by 70

Modeling Corrosion with First-Principles Electrochemical Phase Diagrams

Huang, L., Scully, J. R., & Rondinelli, J. M. (2019) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070218-010105 · Cited by 69

Green’s Function Formulation of Quantum Defect Embedding Theory

Sheng, N., Vorwerk, C., Govoni, M., & Galli, G. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00240 · Cited by 74

Systematic Evaluation of Counterpoise Correction in Density Functional Theory

Gray, M., Bowling, P. E., & Herbert, J. M. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00883 · Cited by 68

The Energetic Origins of Pi–Pi Contacts in Proteins

Carter-Fenk, K., Liu, M., Pujal, L., Loipersberger, M., Tsanai, M., Vernon, R. M., Forman-Kay, J. D., Head‐Gordon, M., Heidar‐Zadeh, F., & Head-Gordon, T. L. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c09198 · Cited by 69

Efficient near-infrared organic light-emitting diodes with emission from spin doublet excitons

Cho, H., Gorgon, S., Londi, G., Giannini, S., Cho, C., Ghosh, P., Tonnelé, C., Casanova, D., Olivier, Y., Baikie, T. K., et al. (2024) · Nature Photonics · doi:10.1038/s41566-024-01458-3 · Cited by 68

Interaction between water and carbon nanostructures: How good are current density functional approximations?

Brandenburg, J. G., Zen, A., Alfè, D., & Michaelides, A. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5121370 · Cited by 68

Semilocal exchange-correlation potentials for solid-state calculations: Current status and future directions

Tran, F., Doumont, J., Kalantari, L., Huran, A. W., Marques, M. A. L., & Blaha, P. (2019) · Journal of Applied Physics · doi:10.1063/1.5118863 · Cited by 68

What Levels of Coupled Cluster Theory Are Appropriate for Transition Metal Systems? A Study Using Near-Exact Quantum Chemical Values for 3d Transition Metal Binary Compounds

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