Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields

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

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

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

Density functional theory with London dispersion corrections

Grimme, S. (2011) · WIREs Computational Molecular Science · doi:10.1002/wcms.30 · Cited by 2,829

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

Computational Thermochemistry: Scale Factor Databases and Scale Factors for Vibrational Frequencies Obtained from Electronic Model Chemistries

Alecu, I. M., Zheng, J., Zhao, Y., & Truhlar, D. G. (2010) · Journal of Chemical Theory and Computation · doi:10.1021/ct100326h · Cited by 1,667

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

Minimally augmented Karlsruhe basis sets

Zheng, J., Xu, X., & Truhlar, D. G. (2011) · Theoretical Chemistry Accounts · doi:10.1007/s00214-010-0846-z · Cited by 1,067

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00099 · Cited by 911

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

Elucidating reaction mechanisms on quantum computers

Reiher, M., Wiebe, N., Svore, K. M., Wecker, D., & Troyer, M. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619152114 · Cited by 745

Semiempirical quantum mechanical methods for noncovalent interactions for chemical and biochemical applications.

Christensen, A. S., Kubař, T., Cui, Q., & Elstner, M. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00584 · Cited by 446

An sp-hybridized all-carboatomic ring, cyclo[18]carbon: Bonding character, electron delocalization, and aromaticity

Liu, Z., Lu, T., & Chen, Q. (2020) · Carbon · doi:10.1016/j.carbon.2020.04.099 · Cited by 429

The Adsorption of Perfluorooctane Sulfonate onto Sand, Clay, and Iron Oxide Surfaces

Johnson, R. L., Anschutz, A. J., Smolen, J. M., Simcik, M. F., & Penn, R. L. (2007) · Journal of Chemical & Engineering Data · doi:10.1021/je060285g · Cited by 389

The Nonlocal Kernel in van der Waals Density Functionals as an Additive Correction: An Extensive Analysis with Special Emphasis on the B97M-V and ωB97M-V Approaches

Najibi, A., & Goerigk, L. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00842 · Cited by 320

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

Janet, J. P., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b08750 · Cited by 289

A quantitative uncertainty metric controls error in neural network-driven chemical discovery

Janet, J. P., Duan, C., Yang, T., Nandy, A., & Kulik, H. J. (2019) · Chemical Science · doi:10.1039/c9sc02298h · Cited by 243

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

The S66x8 benchmark for noncovalent interactions revisited: explicitly correlated ab initio methods and density functional theory

Brauer, B., Kesharwani, M. K., Kozuch, S., & Martin, J. M. L. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp00688d · Cited by 238

Machine learning meets volcano plots: computational discovery of cross-coupling catalysts

Meyer, B., Sawatlon, B., Heinen, S., von Lilienfeld, O. A., & Corminboeuf, C. (2018) · Chemical Science · doi:10.1039/c8sc01949e · Cited by 209

A uniform approach to the description of multicenter bonding

Szczepanik, D. W., Andrzejak, M., Dyduch, K., Żak, E., Makowski, M., Mazur, G., & Mrozek, J. (2014) · Physical Chemistry Chemical Physics · doi:10.1039/c4cp02932a · Cited by 207

Accurate molecular polarizabilities with coupled cluster theory and machine learning

Wilkins, D. M., Grisafi, A., Yang, Y., Lao, K. U., DiStasio, R. A., & Ceriotti, M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816132116 · Cited by 204

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

Janet, J. P., Chan, L., & Kulik, H. J. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00170 · Cited by 200

Predicting electronic structure properties of transition metal complexes with neural networks

Janet, J. P., & Kulik, H. J. (2017) · Chemical Science · doi:10.1039/c7sc01247k · Cited by 199

Hydration of the fluoride anion: structures and absolute hydration free energy from first-principles electronic structure calculations

Zhan, C.-G., & Dixon, D. A. (2004) · The Journal of Physical Chemistry A · doi:10.1021/jp0311512 · Cited by 191

Mechanism of Ring-Opening Polymerization of 1,5-Dioxepan-2-one and l -Lactide with Stannous 2-Ethylhexanoate. A Theoretical Study

Ryner, M., Stridsberg, K., Albertsson, A.-C., von Schenck, H., & Svensson, M. (2001) · Macromolecules · doi:10.1021/ma002096n · Cited by 182

Global aromaticity at the nanoscale

Rickhaus, M., Jirasek, M., Tejerina, L., Gotfredsen, H., Peeks, M. D., Haver, R., Jiang, H.-W., Claridge, T. D. W., & Anderson, H. L. (2020) · Nature Chemistry · doi:10.1038/s41557-019-0398-3 · Cited by 175

Microbial Cleavage of C–F Bonds in Two C6 Per- and Polyfluorinated Compounds via Reductive Defluorination

Yu, Y., Zhang, K., Li, Z., Ren, C., Chen, J., Lin, Y.-H., Liu, J., & Men, Y. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c04483 · Cited by 167

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Nandy, A., Duan, C., Janet, J. P., Gugler, S., & Kulik, H. J. (2018) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.8b04015 · Cited by 161

Mechanistic Basis for High Stereoselectivity and Broad Substrate Scope in the (salen)Co(III)-Catalyzed Hydrolytic Kinetic Resolution

Ford, D. D., Nielsen, L. P. C., Zuend, S. J., Musgrave, C. B., & Jacobsen, E. N. (2013) · Journal of the American Chemical Society · doi:10.1021/ja408027p · Cited by 138

Localized high concentration electrolyte behavior near a lithium–metal anode surface

Zheng, Y., Soto, F. A., Ponce, V., Seminario, J. M., Cao, X., Zhang, J.-G., & Balbuena, P. B. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta08935g · Cited by 123

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

Janet, J. P., Liu, F., Nandy, A., Duan, C., Yang, T., Lin, S., & Kulik, H. J. (2019) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.9b00109 · Cited by 114

Assignment of the lowest-lying THz absorption signatures in biotin and lactose monohydrate by solid-state density functional theory

Allis, D. G., Fedor, A. M., Korter, T. M., Bjarnason, J. E., & Brown, E. R. (2007) · Chemical Physics Letters · doi:10.1016/j.cplett.2007.04.032 · Cited by 107

Reductive defluorination of branched per-and polyfluoroalkyl substances with cobalt complex catalysts

Liu, J., Van Hoomissen, D. J., Liu, T., Maizel, A., Huo, X., Fernández, S. R., Ren, C., Xiao, X., Fang, Y., Schaefer, C. E., et al. (2018) · Environmental Science & Technology Letters · doi:10.1021/acs.estlett.8b00122 · Cited by 98

Mechanisms of Organocatalytic Amidation and Trans-Esterification of Aromatic Esters As a Model for the Depolymerization of Poly(ethylene) Terephthalate

Horn, H. W., Jones, G. O., Wei, D. S., Fukushima, K., Lecuyer, J. M., Coady, D. J., Hedrick, J. L., & Rice, J. E. (2012) · The Journal of Physical Chemistry A · doi:10.1021/jp304212y · Cited by 97

Machine Learning Protocol for Surface-Enhanced Raman Spectroscopy

Hu, W., Ye, S., Zhang, Y., Li, T., Zhang, G., Luo, Y., Mukamel, S., & Jiang, J. (2019) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.9b02517 · Cited by 92

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation

Nandy, A., Zhu, J., Janet, J. P., Duan, C., Getman, R. B., & Kulik, H. J. (2019) · ACS Catalysis · doi:10.1021/acscatal.9b02165 · Cited by 91

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

Duan, C., Janet, J. P., Liu, F., Nandy, A., & Kulik, H. J. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00057 · Cited by 87

Computational and experimental investigations of one-step conversion of poly(carbonate)s into value-added poly(aryl ether sulfone)s

Jones, G. O., Yuen, A., Wojtecki, R. J., Hedrick, J. L., & García, J. M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1600924113 · Cited by 85

Update to the General Amber Force Field for Small Solutes with an Emphasis on Free Energies of Hydration

Jämbeck, J. P. M., & Lyubartsev, A. P. (2014) · The Journal of Physical Chemistry B · doi:10.1021/jp4111234 · Cited by 73

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

Taylor, M. G., Yang, T., Lin, S., Nandy, A., Janet, J. P., Duan, C., & Kulik, H. J. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c01458 · Cited by 81

How do the Hückel and Baird Rules Fade away in Annulenes?

Casademont-Reig, I., Ramos-Cordoba, E., Torrent-Sucarrat, M., & Matito, E. (2020) · Molecules · doi:10.3390/molecules25030711 · Cited by 71

Metal-free photochemical silylations and transfer hydrogenations of benzenoid hydrocarbons and graphene

Papadakis, R., Li, H., Bergman, J., Lundstedt, A., Jorner, K., Ayub, R., Haldar, S., Jahn, B. O., Denisova, A., Zietz, B., et al. (2016) · Nature Communications · doi:10.1038/ncomms12962 · Cited by 67

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

Probing the Role of Backbone Hydrogen Bonds in Protein–Peptide Interactions by Amide-to-Ester Mutations

Eildal, J. N. N., Hultqvist, G., Balle, T., Stuhr-Hansen, N., Padrah, S., Gianni, S., Strømgaard, K., & Jemth, P. (2013) · Journal of the American Chemical Society · doi:10.1021/ja402875h · Cited by 61

Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening

Liu, F., Duan, C., & Kulik, H. J. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02288 · Cited by 58

Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design

Janet, J. P., Gani, T. Z. H., Steeves, A. H., Ioannidis, E. I., & Kulik, H. J. (2017) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.7b00808 · Cited by 56

The effect of the sixth sulfur ligand in the catalytic mechanism of periplasmic nitrate reductase

Cerqueira, N. M. F. S. A., Gonzalez, P. J., Brondino, C. D., Romão, M. J., Romão, C. C., Moura, I., & Moura, J. J. G. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21280 · Cited by 56

Light‐induced cytotoxicity of 16 polycyclic aromatic hydrocarbons on the US EPA priority pollutant list in human skin HaCaT keratinocytes: Relationship between phototoxicity and excited state properties

Wang, S., Sheng, Y., Feng, M., Leszczynski, J., Wang, L., Tachikawa, H., & Yu, H. (2007) · Environmental Toxicology · doi:10.1002/tox.20241 · Cited by 51

Mechanistic Investigations of the Stereoselective Rare Earth Metal-Mediated Ring-Opening Polymerization of β-Butyrolactone

Altenbuchner, P. T., Kronast, A., Kissling, S., Vagin, S. I., Herdtweck, E., Pöthig, A., Deglmann, P., Loos, R., & Rieger, B. (2015) · Chemistry – A European Journal · doi:10.1002/chem.201501156 · Cited by 46

Is coronene better described by Clar's aromatic π‐sextet model or by the AdNDP representation?

Kumar, A., Duran, M., & Solà, M. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24801 · Cited by 46

Patterns of π-electron delocalization in aromatic and antiaromatic organic compounds in the light of Hückel's 4n + 2 rule

Feixas, F., Matito, E., Solà, M., & Poater, J. (2010) · Physical Chemistry Chemical Physics · doi:10.1039/b924972a · Cited by 44

Enumeration of de novo inorganic complexes for chemical discovery and machine learning

Gugler, S., Janet, J. P., & Kulik, H. J. (2020) · Molecular Systems Design & Engineering · doi:10.1039/c9me00069k · Cited by 43

A computational and experimental study of O-glycosylation. Catalysis by human UDP-GalNAc polypeptide:GalNAc transferase-T2

Gómez, H., Rojas, R., Patel, D., Tabak, L. A., Lluch, J. M., & Masgrau, L. (2014) · Org. Biomol. Chem. · doi:10.1039/c3ob42569j · Cited by 41

Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles

Duan, C., Chen, S., Taylor, M. G., Liu, F., & Kulik, H. J. (2021) · Chemical Science · doi:10.1039/d1sc03701c · Cited by 40

Toward Atomistic Modeling of Irreversible Covalent Inhibitor Binding Kinetics

Yu, H. S., Gao, C., Lupyan, D., Wu, Y., Kimura, T., Wu, C., Jacobson, L., Harder, E., Abel, R., & Wang, L. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00268 · Cited by 39

Theoretical study on the mechanism and kinetics of ring-opening polymerization of cyclic esters initiated by tin(II) n-butoxide

Sattayanon, C., Sontising, W., Jitonnom, J., Meepowpan, P., Punyodom, W., & Kungwan, N. (2014) · Computational and Theoretical Chemistry · doi:10.1016/j.comptc.2014.06.008 · Cited by 36

Understanding Water–Zeolite Interactions: On the Accuracy of Density Functionals

Stanciakova, K., Louwen, J. N., Weckhuysen, B. M., Bulo, R. E., & Göltl, F. (2021) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.1c04270 · Cited by 35

Ring opening polymerization of rac-lactide by group 4 tetracarbamato complexes: Activation, propagation and role of the metal

Marchetti, F., Pampaloni, G., Pinzino, C., Renili, F., Repo, T., & Vuorinen, S. (2013) · Dalton Trans. · doi:10.1039/c2dt31264f · Cited by 34

The Conversion among Various B4C Clusters: A Density Functional Theoretical Study

Liu, C., Tang, M., & Wang, H. (2007) · The Journal of Physical Chemistry A · doi:10.1021/jp0664115 · Cited by 25

A chiral rhenium complex with predicted high parity violation effects: synthesis, stereochemical characterization by VCD spectroscopy and quantum chemical calculations

Saleh, N., Zrig, S., Roisnel, T., Guy, L., Bast, R., Saue, T., Darquié, B., & Crassous, J. (2013) · Physical Chemistry Chemical Physics · doi:10.1039/c3cp50199j · Cited by 26

Theoretical investigation on the mechanism and kinetics of the ring-opening polymerization of ε-caprolactone initiated by tin(II) alkoxides

Sattayanon, C., Kungwan, N., Punyodom, W., Meepowpan, P., & Jungsuttiwong, S. (2013) · Journal of Molecular Modeling · doi:10.1007/s00894-013-2026-2 · Cited by 25

Roadmap on methods and software for electronic structure based simulations in chemistry and materials

Blum, V., Asahi, R., Autschbach, J., Bannwarth, C., Bihlmayer, G., Blügel, S., Burns, L. A., Crawford, T. D., Dawson, W., de Jong, W. A., et al. (2024) · Electronic Structure · doi:10.1088/2516-1075/ad48ec · Cited by 24

Solvent water interactions within the active site of the membrane type I matrix metalloproteinase.

Decaneto, E., Vasilevskaya, T., Kutin, Y., Ogata, H., Grossman, M., Sagi, I., Havenith, M., Lubitz, W., Thiel, W., & Cox, N. (2017) · Physical Chemistry Chemical Physics · doi:10.1039/c7cp05572b · Cited by 23

Equilibrium and kinetic isotopic fractionation in the CO2 hydration and hydroxylation reactions: Analysis of the role of hydrogen-bonding via quantum mechanical calculations

Boettger, J. D., & Kubicki, J. D. (2021) · Geochimica et Cosmochimica Acta · doi:10.1016/j.gca.2020.09.019 · Cited by 20

From QM/MM to ML/MM: A new era in multiscale modeling

Grassano, J. S., Pickering, I., Roitberg, A. E., Estrin, D. A., & Semelak, J. A. (2025) · Chemical Physics Reviews · doi:10.1063/5.0260078 · Cited by 20

Surrogate Based Genetic Algorithm Method for Efficient Identification of Low-Energy Peptide Structures

Villard, J., Kılıç, M., & Rothlisberger, U. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01078 · Cited by 19

DFT benchmark study of the O–O bond dissociation energy in peroxides validated with high-level ab initio calculations

Carmona, D. J., Jaque, P., & Vöhringer-Martinez, E. (2020) · Theoretical Chemistry Accounts · doi:10.1007/s00214-020-02607-x · Cited by 18

Selective Adsorption of CO 2 over N 2 on Graphene: Insights from First-Principles Calculations

Faghihnasiri, M., & Branicio, P. S. (2025) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.5c00105 · Cited by 17

Tin (IV) alkoxide initiator design for poly (d-lactide) synthesis using DFT calculations

Lawan, N., Muangpil, S., Kungwan, N., Meepowpan, P., Lee, V. S., & Punyodom, W. (2013) · Computational and Theoretical Chemistry · doi:10.1016/j.comptc.2013.07.045 · Cited by 15

CO 2 on Graphene: Benchmarking Computational Approaches to Noncovalent Interactions

Ehlert, C., Piras, A., & Gryn’ova, G. (2023) · ACS Omega · doi:10.1021/acsomega.3c03251 · Cited by 15

Triplet State Baird Aromaticity in Macrocycles: Scope, Limitations, and Complications

Ayub, R., El Bakouri, O., Smith, J. R., Jorner, K., & Ottosson, H. (2021) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c08926 · Cited by 15

Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning

Platero-Rochart, D., Krivobokova, T., Gastegger, M., Reibnegger, G., & Sánchez-Murcia, P. A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00772 · Cited by 15

In-insect synthesis of oxygen-doped molecular nanocarbons

Usami, A., Kono, H., Austen, V., Phung, Q. M., Shudo, H., Kato, T., Yamada, H., Yagi, A., Amaike, K., Fujimoto, K. J., et al. (2025) · Science · doi:10.1126/science.adp9384 · Cited by 15

Mechanistic Insight into the Ring-Opening Polymerization of ε-Caprolactone and L-Lactide Using Ketiminate-Ligated Aluminum Catalysts

Lin, Y.-F., & Jheng, N.-Y. (2019) · Polymers · doi:10.3390/polym11091530 · Cited by 11

Multidimensional scattering of attosecond x-ray pulses detected by photon-coincidence

Biggs, J. D., Bennett, K., Zhang, Y., & Mukamel, S. (2014) · Journal of Physics B: Atomic, Molecular and Optical Physics · doi:10.1088/0953-4075/47/12/124037 · Cited by 10