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

A DFT study on protic solvent assisted tautomerization of heterocyclic thiocarbonyls

Jayasree, E. G., & Sreedevi, S. (2019) · Chemical Physics · doi:10.1016/j.chemphys.2019.110650 · Cited by 14

Ab initio description of nanodiamonds: A DFT and TDDFT benchmark

López‐Carballeira, D., & Polcar, T. (2020) · Diamond and Related Materials · doi:10.1016/j.diamond.2020.107959 · Cited by 14

Stabilization mechanism of arsenic-sulfide slag by density functional theory calculation of arsenic-sulfide clusters

Liu, F., Xu, Q., Liang, H., Wang, H., Zhong, C., Min, X., & Zhang, L. (2020) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2020.124567 · Cited by 14

A guide to benchmarking enzymatically catalysed reactions: the importance of accurate reference energies and the chemical environment

Wappett, D. A., & Goerigk, L. (2021) · Theoretical Chemistry Accounts · doi:10.1007/s00214-021-02770-9 · Cited by 14

Mechanism of Guanosine Triphosphate Hydrolysis by the Visual Proteins Arl3-RP2: Free Energy Reaction Profiles Computed with Ab Initio Type QM/MM Potentials

Khrenova, M. G., Bulavko, E. S., Mulashkin, F. D., & Nemukhin, A. V. (2021) · Molecules · doi:10.3390/molecules26133998 · Cited by 14

Challenges for the theoretical description of the mechanism and kinetics of reactions catalyzed by zeolites

der Mynsbrugge, J. V., & Bell, A. T. (2021) · Journal of Catalysis · doi:10.1016/j.jcat.2021.08.048 · Cited by 14

Effective Two-Body Interactions

Mackie, C. J., Zech, A., & Head‐Gordon, M. (2021) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.1c05677 · Cited by 14

First-principles study of Ti-doped sapphire. I. Formation and optical transition properties of titanium pairs

Jing, W., Liu, M., Wen, J., Ning, L., Yin, M., & Duan, C. (2021) · Physical review. B./Physical review. B · doi:10.1103/physrevb.104.165103 · Cited by 15

Comprehensive DFT analysis of structural, optoelectronic, and thermoelectric properties of ZnGa2X4 (X = S, Se, and Te) defect chalcopyrites for energy applications

Benaadad, M., Labrag, A., Bghour, M., & Ouaddi, H. E. (2025) · Optical and Quantum Electronics · doi:10.1007/s11082-025-08252-7 · Cited by 13

Uncertainty Quantification for In Silico Chemistry

Frömbgen, T., Surzhikova, E., Dölz, J., Proppe, J., Kirchner, B., & Jacob, C. R. (2026) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00931 · Cited by 18

Pathologies related to abnormal deposits in dermatology: a physico-chemical approach

Colboc, H., Moguelet, P., Letavernier, E., Frochot, V., Bernaudin, J., Weil, R., Rouzière, S., Senet, P., Bachmeyer, C., Laporte, N., et al. (2022) · Comptes Rendus Chimie · doi:10.5802/crchim.153 · Cited by 13

Understanding Primary Charge Separation in the Heliobacterial Reaction Center

Brütting, M., Foerster, J. M., & Kümmel, S. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c00377 · Cited by 13

Stabilizing hydroperoxyflavin intermediate formation via a peptide appendage: a neutral flavoenzyme model

Mouli, M. S. S. V., Mondal, D., Kumari, K., Singh, S. K., & Mishra, A. K. (2023) · Organic & Biomolecular Chemistry · doi:10.1039/d3ob00125c · Cited by 13

Kohn–Sham time-dependent density functional theory with Tamm–Dancoff approximation on massively parallel GPUs

Kim, I., Jeong, D., Son, W., Hyung-Jin, K., Rhee, Y. M., Jung, Y., Choi, H., Yim, J., Jang, I., & Kim, D. S. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01041-4 · Cited by 13

Compilation of Ionic Clusters with the Rock Salt Structure: Accurate Benchmark Thermochemical Data, Assessment of Quantum Chemistry Methods, and the Convergence Behavior of Lattice Energies

Chan, B. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c01880 · Cited by 13

Exchange correlation potentials from full configuration interaction in a Slater orbital basis

Tribedi, S., Dang, D., Kanungo, B., Gavini, V., & Zimmerman, P. M. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0157942 · Cited by 13

A density functional theory study on interactions in water-bridged dimeric complexes of lignin

Lange, J., Secchi, A. R., & Tavares, F. W. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp00312h · Cited by 13

Sorting drug conformers in enzyme active sites: the XTB way

Chan, B., Dawson, W., & Nakajima, T. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp00930d · Cited by 13

Big data benchmarking: how do DFT methods across the rungs of Jacob's ladder perform for a dataset of 122k CCSD(T) total atomization energies?

Karton, A. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp00387j · Cited by 13

Insights into the optoelectronic and thermoelectric properties of defect chalcopyrites XAl2Se4 (X = Zn, Cd, and Hg): A density functional theory approach

Benaadad, M., Bghour, M., & Labrag, A. (2024) · Physica B Condensed Matter · doi:10.1016/j.physb.2024.416316 · Cited by 13

Unveiling the pH-Responsive Mechanisms of the Carbon Dot–Proximicin-A Peptide Conjugate for Targeted Cancer Therapy Using Density Functional Theory

Ahamed, A., Samaranayake, P., de Silva, V., Kooh, M. R. R., Wickramage, N. M., Rajapaksha, I. G., & Thotagamuge, R. (2025) · Molecules · doi:10.3390/molecules30040896 · Cited by 13

Design of Fluorine‐Free Weakly Coordinating Electrolyte Solvents with Enhanced Oxidative Stability

Wichmann, L., Aboobacker, A., Heuvel, S., Pfeiffer, F., Hinz, R., Glorius, F., Cekić-Lasković, I., Diddens, D., Winter, M., & Brunklaus, G. (2025) · Angewandte Chemie International Edition · doi:10.1002/anie.202506826 · Cited by 14

Ground-State Energy Estimation on Current Quantum Hardware through the Variational Quantum Eigensolver: A Practical Study

Belaloui, N. E., Tounsi, A., Khamadja, A. R., Louamri, M. M., Benslama, A., Bernal, D. E., & Rouabah, M. T. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01657 · Cited by 23

Influence of DFT Functionals and Solvation Models on the Prediction of Far-Infrared Spectra of Pt-Based Anticancer Drugs: Why Do Different Complexes Require Different Levels of Theory?

Gwee, E. S. H., Seeger, Z. L., Appadoo, D., Wood, B. R., & Izgorodina, E. I. (2019) · ACS Omega · doi:10.1021/acsomega.8b03455 · Cited by 13

Microhydration of a Carbonyl Group: How does the Molecular Electrostatic Potential (MESP) Impact the Formation of (H2O)n:(R2C═O)Complexes?

Zins, É. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b09992 · Cited by 13

Comparison of Molecular Recognition of Trimethyllysine and Trimethylthialysine by Epigenetic Reader Proteins

Hintzen, J. C. J., Poater, J., Kumar., K., V, Temimi, A. H. K. A., Pieters, B. J. G. E., Paton, R. S., Bickelhaupt, F. M., & Mecinović, J. (2020) · Molecules · doi:10.3390/molecules25081918 · Cited by 13

Bonding in nitrile photo-dissociating ruthenium drug candidates—A local vibrational mode study

McCutcheon, M., Freindorf, M., & Kraka, E. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0094567 · Cited by 13

Thermochemistry of per‐ and polyfluoroalkyl substances

Melin, T. R. L., Harell, P., Ali, B., Loganathan, N., & Wilson, A. K. (2022) · Journal of Computational Chemistry · doi:10.1002/jcc.27023 · Cited by 13

Sparsity of the electron repulsion integral tensor using different localized virtual orbital representations in local second-order Møller–Plesset theory

Wang, Z., Aldossary, A., & Head‐Gordon, M. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0134764 · Cited by 13

Quantum chemical exploration of the binding motifs and binding energies of neutral molecules, radicals and ions with small water clusters: characterisation and astrochemical implications

Hendrix, J., Bera, P. P., Lee, T. J., & Head‐Gordon, M. (2023) · Molecular Physics · doi:10.1080/00268976.2023.2252100 · Cited by 13

Theoretical modelling of the adsorption of neutral and charged sulphur-bearing species on to olivine nanoclusters

Perrero, J., Beitia-Antero, L., Fuente, A., Ugliengo, P., & Rimola, A. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3896 · Cited by 13

Are we still chasing molecules that were never there? The role of quantum chemical simulations of NMR parameters in structural reassignment of natural products

de Albuquerque, A. C. F., Martorano, L. H., & dos Santos, F. M. (2024) · Frontiers in Natural Products · doi:10.3389/fntpr.2023.1321043 · Cited by 13

Development and application of a 2-electron reduced density matrix approach to electron transport via molecular junctions

Hoy, E. P., Mazziotti, D. A., & Seideman, T. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4986804 · Cited by 13

Fluorine substituent effect on the stereochemistry of catalyzed and non-catalyzed Diels–Alder reactions. The case of R-butenone with cyclopentadiene: a computational assessment of the mechanism

Merzoud, L., Saal, A., Moussaoui, R., Ouamerali, O., Morell, C., & Chermette, H. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp00985f · Cited by 13

Independent amplitude approximations in coupled cluster valence bond theory: Incorporation of 3-electron-pair correlation and application to spin frustration in the low-lying excited states of a ferredoxin-type tetrametallic iron-sulfur cluster

Small, D. W., & Head‐Gordon, M. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5046318 · Cited by 13

Multigenerational Theoretical Study of Isoprene Peroxy Radical 1–5-Hydrogen Shift Reactions that Regenerate HOx Radicals and Produce Highly Oxidized Molecules

Piletic, I. R., Howell, R., Bartolotti, L. J., Kleindienst, T. E., Kaushik, S. M., & Edney, E. O. (2018) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.8b09738 · Cited by 13

Generation of Tautomers Using Micro-pKa’s

Watson, M. A., Yu, H. S., & Bochevarov, A. D. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00955 · Cited by 13

Testing one-parameter hybrid exchange functionals in confined atomic systems

Duarte-Alcaráz, F.-A., Martínez‐Sánchez, M., Rivera‐Almazo, M., Vargas, R., Rosas, R., & Garza, J. (2019) · Journal of Physics B Atomic Molecular and Optical Physics · doi:10.1088/1361-6455/ab233b · Cited by 13

How To Extract Quantitative Information on Electronic Transitions from the Density Functional Theory “Black Box”

Migliore, A. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00518 · Cited by 13

Density functional approximations for consistent spin and oxidation states of oxoiron complexes

Vlahović, F. Ž., Gruden, M., Stepanovic, S., & Swart, M. (2019) · International Journal of Quantum Chemistry · doi:10.1002/qua.26121 · Cited by 13

First-Principles Calculation of Transition Metal Hyperfine Coupling Constants with the Strongly Constrained and Appropriately Normed (SCAN) Density Functional and its Hybrid Variants

Pantazis, D. A. (2019) · Magnetochemistry · doi:10.3390/magnetochemistry5040069 · Cited by 13

Investigation on the impact of solvent on the photochemical properties of the photoactive anticancer drug Vemurafenib: A computational study

Bani‐Yaseen, A. D. (2020) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2020.114900 · Cited by 13

Beyond Chemical Accuracy for Alkane Thermochemistry: The DHthermo Approach

Li, H., Tirri, B., Brémond, É. A. G., Sancho-Garcı́a, J. C., & Adamo, C. (2021) · The Journal of Organic Chemistry · doi:10.1021/acs.joc.1c00058 · Cited by 13

Benchmarking of Density Functionals for Z -Azoarene Half-Lives via Automated Transition State Search

Adrion, D., Kaliakin, D. S., Neal, P., & Lopez, S. A. (2021) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.1c01695 · Cited by 13

Disclosure of Ground‐State Zimmerman‐Möbius Aromaticity in the Radical Anion of [6]Helicene and Evidence for 4π Periodic Aromatic Ring Currents in a Molecular “Metallic” Möbius Strip

Mauksch, M., & Tsogoeva, S. B. (2021) · Chemistry - A European Journal · doi:10.1002/chem.202102230 · Cited by 13

Erratum to “Toward a Quantum-Chemical Benchmark Set for Enzymatically Catalyzed Reactions: Important Steps and Insights”

Wappett, D. A., & Goerigk, L. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c00425 · Cited by 13

Beyond MD17: the reactive xxMD dataset

Pengmei, Z., Liu, J., & Shu, Y. (2024) · Scientific Data · doi:10.1038/s41597-024-03019-3 · Cited by 13

Extending Density-Corrected Density Functional Theory to Large Molecular Systems

Kim, Y., Sim, M., Lee, M., Kim, S. H., Song, S., Burke, K., & Sim, E. (2025) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c02852 · Cited by 12

Machine Learning Transition State Geometries and Applications in Reaction Property Prediction

Beaglehole, I. W., Pemberton, M. J., Farrar, E. H. E., & Grayson, M. N. (2025) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70025 · Cited by 13

Contemporary DFT: learning from traditional and recent trends for the development and assessment of accurate exchange–correlation functionals

Brémond, É. A. G., Pérez-Jiménez, Á. J., Adamo, C., & Sancho-Garcı́a, J. C. (2025) · Physical Chemistry Chemical Physics · doi:10.1039/d5cp03373j · Cited by 12

On scientific foundation models: Rigorous definitions, key applications, and a comprehensive survey

Menon, S. S., Mondal, T., Brahmachary, S., Panda, A., Joshi, S. M., Kalyanaraman, K., & Jagtap, A. D. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.108567 · Cited by 15

Machine learning estimation of reaction energy barriers

Ji, H., Rágyanszki, A., & Fournier, R. (2023) · Computational and Theoretical Chemistry · doi:10.1016/j.comptc.2023.114332 · Cited by 13

Revisiting the Paradigm of Reaction Optimization in Flow with a Priori Computational Reaction Intelligence

Bianchi, P., & Monbaliu, J. M. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202311526 · Cited by 12

Development of a machine learning finite-range nonlocal density functional

Chen, Z., & Yang, W. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0179149 · Cited by 14

Bridging the gap between high-level quantum chemical methods and deep learning models

Prasad, V. K., Otero‐de‐la‐Roza, A., & DiLabio, G. A. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad27e1 · Cited by 13

Performance of Density Functionals for Excited-State Properties of Isolated Chromophores and Exciplexes: Emission Spectra, Solvatochromic Shifts, and Charge-Transfer Character

Patra, A., Pipim, G. B., Krylov, A. I., & Sharada, S. M. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00005 · Cited by 12

Novel quinoline photoinitiators for dimethacrylate monomer photopolymerization under UV and blue light

Moreno, V. F., Barboza, B. H., Martins, L. M., Gaglieri, C., Bannach, G., Batagin‐Neto, A., & da Silva‐Filho, L. C. (2024) · European Polymer Journal · doi:10.1016/j.eurpolymj.2024.113331 · Cited by 12

A Critical Evaluation of the Hybrid KS DFT Functionals Based on the KS Exchange-Correlation Potentials

Kumar, V. B., Śmiga, S., & Grabowski, I. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c01979 · Cited by 12

Smooth Dispersion Is Physically Appropriate: Assessing and Amending the D4 Dispersion Model

Tkachenko, N. V., Dittmer, L. B., Tomann, R., & Head‐Gordon, M. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c02653 · Cited by 12

Canonical coupled cluster binding benchmark for nanoscale noncovalent complexes at the hundred-atom scale

Lao, K. U. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0242359 · Cited by 12

What Is More Important When Calculating the Thermodynamic Properties of Organic Crystals, Density Functional, Supercell, or Energy Second-Order Derivative Method Choice?

Dubok, A. S., & Rychkov, D. A. (2025) · Crystals · doi:10.3390/cryst15030274 · Cited by 12

On the practical applicability of DM21 neural-network DFT functional for chemical calculations: Focus on geometry optimization

Kulaev, K., Ryabov, A. A., Medvedev, M. G., Burnaev, E. V., & Vanovskiy, V. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0266500 · Cited by 12

Mechanistic Insights into Copper-Catalyzed Carboxylations

Obst, M., Gevorgyan, A., Bayer, A., & Hopmann, K. H. (2020) · Organometallics · doi:10.1021/acs.organomet.9b00710 · Cited by 12

Modeling Dynamic Conformations of Organic Molecules: Alkyne Carotenoids in Solution

Streckaitė, S., Mačernis, M., Li, F., Trsková, E. K., Litvín, R., Yang, C., Pascal, A. A., Valkūnas, L., Robert, B., & Llansola‐Portoles, M. J. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b11536 · Cited by 12

Comparative study of Minnesota functionals performance on ferroelectric BaTiO3 and PbTiO3

Kingsland, M., Lynch, K. A., Lisenkov, S., He, X. D., & Ponomareva, I. (2020) · Physical Review Materials · doi:10.1103/physrevmaterials.4.073802 · Cited by 13

Decomplexation as a rate limitation in the thiol-Michael addition of N -acrylamides

Brown, J. S., Ruttinger, A. W., Vaidya, A. J., Alabi, C. A., & Clancy, P. (2020) · Organic & Biomolecular Chemistry · doi:10.1039/d0ob00726a · Cited by 13

Meta-Local Density Functionals: A New Rung on Jacob’s Ladder

Lehtola, S., & Marques, M. A. L. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c01147 · Cited by 12

Charge Transport and Optical Absorption Properties of Dibenzocoronene Tetracarboxdiimide Based Liquid Crystalline Molecules: A Theoretical Study

Pavithrakumar, M., Krishnan, S., & Senthilkumar, K. (2021) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.1c00790 · Cited by 12

GW approximation for open-shell molecules: a first-principles study

Mansouri, M., Casanova, D., Koval, P., & Sánchez‐Portal, D. (2021) · New Journal of Physics · doi:10.1088/1367-2630/ac1bf3 · Cited by 12

Structural and Energetic Properties of Amino Acids and Peptides Benchmarked by Accurate Theoretical and Experimental Data

Wang, P., Shu, C., Ye, H., & Biczysko, M. (2021) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.1c06504 · Cited by 12

An Experimental and Theoretical Investigation of the Efficacy of Pantoprazole as a Corrosion Inhibitor for Mild Steel in an Acidic Medium

Бериша, А. (2022) · Electrochem · doi:10.3390/electrochem3010002 · Cited by 12

DFT-Based Approach Enables Deliberate Tuning of Alloy Nanostructure Plasmonic Properties

Bubaš, M., & Sancho‐Parramon, J. (2021) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.1c05910 · Cited by 12

Inclusion of More Physics Leads to Less Data: Learning the Interaction Energy as a Function of Electron Deformation Density with Limited Training Data

Low, K., Coote, M. L., & Izgorodina, E. I. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01264 · Cited by 12

The transferability limits of static benchmarks

Weymuth, T., & Reiher, M. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp01725c · Cited by 12

How good are recent density functionals for ground and excited states of one-electron systems?

Schwalbe, S., Trepte, K., & Lehtola, S. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0120515 · Cited by 12

Experimental and Theoretical Studies of Pd Cation Reduction and Oxidation During NO Adsorption on and Desorption from Pd/H–CHA

Kim, P. M., der Mynsbrugge, J. V., Head‐Gordon, M., & Bell, A. T. (2022) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.2c06357 · Cited by 12

Efficient Method for the Computation of Frozen-Core Nuclear Gradients within the Random Phase Approximation

Drontschenko, V., Gräf, D., Laqua, H., & Ochsenfeld, C. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00774 · Cited by 12

Tuning Molecular Electron Affinities against Atomic Electronegativities by Spatial Expansion of a π‐System

Chulanova, E. A., Radiush, E. A., Semenov, N. A., Hupf, E., Irtegova, I. G., Kosenkova, Y. S., Bagryanskaya, I. Y., Shundrin, L. A., Beckmann, J., & Zibarev, A. V. (2023) · ChemPhysChem · doi:10.1002/cphc.202200876 · Cited by 12

The Good, the Bad, and the Ugly: Pseudopotential Inconsistency Errors in Molecular Applications of Density Functional Theory

Rossomme, E. C., Cunha, L. A., Li, W., Chen, K., McIsaac, A. R., Head-Gordon, T. L., & Head‐Gordon, M. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00089 · Cited by 12

How thermal fluctuations influence the function of the FeMo cofactor in nitrogenase enzymes

Li, W., Li, Y., Li, J., & Head-Gordon, T. L. (2023) · Chem Catalysis · doi:10.1016/j.checat.2023.100662 · Cited by 12

Approaching Coupled Cluster Accuracy with Density Functional Theory Using the Generalized Connectivity-Based Hierarchy

Raghavachari, K., Maier, S., Collins, E. M., Debnath, S., & Sengupta, A. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00301 · Cited by 12

Bader’s Topological Bond Path Does Not Necessarily Indicate Stabilizing Interaction—Proof Studies Based on the Ng@[3n]cyclophane Endohedral Complexes

Jabłoński, M. (2023) · Molecules · doi:10.3390/molecules28176353 · Cited by 12

GeoT: A Geometry-Aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning

Kwak, B., Park, J., Kang, T., Jo, J., Lee, B., & Yoon, S. (2023) · ACS Omega · doi:10.1021/acsomega.3c05753 · Cited by 12

DELFI: a computer oracle for recommending density functionals for excited states calculations

Avagliano, D., Skreta, M., Arellano-Rubach, S., & Aspuru‐Guzik, A. (2024) · Chemical Science · doi:10.1039/d3sc06440a · Cited by 12

Autobench V1.0: Benchmarking Automation for Electronic Structure Calculations

Cormanich, R. A., & da Silva, G. D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00250 · Cited by 12

Density Functionals from the Multiple-Radii Approach: Analysis and Recovery of the Kinetic Correlation Energy

Vuckovic, S. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00129 · Cited by 12

Towards Ultra Low Cobalt Cathodes: A High Fidelity Computational Phase Search of Layered Li-Ni-Mn-Co Oxides

Houchins, G., & Viswanathan, V. (2019) · Journal of The Electrochemical Society · doi:10.1149/2.0062007jes · Cited by 12

The Calculated Infrared Spectra of Functionalized Hexamethylenetetramine (HMT) Molecules

Bera, P. P., Sandford, S. A., Lee, T. J., & Nuevo, M. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab3c4f · Cited by 12

Ozone-Induced Cleavage of Endocyclic C═C Double Bonds within Steroid Epimers Produces Unique Gas-Phase Conformations

Maddox, S. W., Caris, R. H. F., Baker, K. L., Burkus-Matesevac, A., Peverati, R., & Chouinard, C. D. (2019) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.9b00058 · Cited by 12

H-Abstraction from Dimethyl Sulfide in the Presence of an Excess of Hydroxyl Radicals. A Quantum Chemical Evaluation of Thermochemical and Kinetic Parameters Unveils an Alternative Pathway to Dimethyl Sulfoxide

Salta, Z., Lupi, J., Barone, V., & Ventura, O. N. (2020) · ACS Earth and Space Chemistry · doi:10.1021/acsearthspacechem.9b00306 · Cited by 12

Chemical constituents from Macleaya cordata (Willd) R. Br. and their phenotypic functions against a Parkinson’s disease patient-derived cell line

Nguyen, T., Iqbal, J., Han, J., Pierens, G. K., Wood, S. A., Mellick, G. D., & Feng, Y. (2020) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2020.115732 · Cited by 12

Electrostatically embedded molecules‐in‐molecules approach and its application to molecular clusters

Tripathy, V., Saha, A., & Raghavachari, K. (2021) · Journal of Computational Chemistry · doi:10.1002/jcc.26492 · Cited by 12

Data‐driven and constrained optimization of semi‐local exchange and nonlocal correlation functionals for materials and surface chemistry

Trepte, K., & Voss, J. (2022) · Journal of Computational Chemistry · doi:10.1002/jcc.26872 · Cited by 12

Dealing with Spin States in Computational Organometallic Catalysis

Swart, M. (2020) · Topics in organometallic chemistry · doi:10.1007/3418_2020_49 · Cited by 12

Physical machine learning outperforms "human learning" in Quantum ChemistryPreprint

Sinitskiy, A. V., & Pande, V. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.00971 · Cited by 14

The Case for Enzymatic Competitive Metal Affinity Methods

Reilley, D. J., Hennefarth, M. R., & Alexandrova, A. N. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04831 · Cited by 12

Multireference Methods for Chemistry and Materials Science: Automated Active Spaces, Efficient Dynamic Correlation, and Extended Systems

Wardzala, J. J., Hennefarth, M. R., Agarawal, V., Jangid, B., Seal, A., Hermes, M. R., King, D. W., & Gagliardi, L. (2026) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00866 · Cited by 11

Template effects on the pressure-dependent behavior of chabazite-type fluoroaluminophosphates: a computational approach

Fischer, M. (2018) · Physics and Chemistry of Minerals · doi:10.1007/s00269-018-1010-x · Cited by 11

Improving the accuracy of 31 P NMR chemical shift calculations by use of scaling methods

Hersh, W. H., & Chan, T.-Y. (2023) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.19.4 · Cited by 11

Influence of Transition Metal Electron Configuration on the Structure of Metal–EDTA Complexes

Foreman, M. M., Alessio, M., Krylov, A. I., & Weber, J. M. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.2c07996 · Cited by 11

A thorough benchmark of density functional methods for general main group thermochemistry, kinetics, and noncovalent interactions

Goerigk, L., & Grimme, S. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c0cp02984j · Cited by 2,036

ω B97M-V: A combinatorially optimized, range-separated hybrid, meta-GGA density functional with VV10 nonlocal correlation

Mardirossian, N., & Head‐Gordon, M. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4952647 · Cited by 1,348

Mapping the genome of meta-generalized gradient approximation density functionals: The search for B97M-V

Mardirossian, N., & Head‐Gordon, M. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4907719 · Cited by 446

Quest for a universal density functional: the accuracy of density functionals across a broad spectrum of databases in chemistry and physics

Peverati, R., & Truhlar, D. G. (2014) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2012.0476 · Cited by 756

Designing and assessing density functionals

Mardirossian, N. (2016) · eScholarship (California Digital Library)