Ab initio effective core potentials for molecular calculations. Potentials for the transition metal atoms Sc to Hg

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849

Surface Catalytic Coupling Reaction of p-Mercaptoaniline Linking to Silver Nanostructures Responsible for Abnormal SERS Enhancement: A DFT Study

Wu, D.-Y., Liu, X.-M., Huang, Y.-F., Ren, B., Xu, X., & Tian, Z.-Q. (2009) · The Journal of Physical Chemistry C · doi:10.1021/jp9050929 · Cited by 311

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

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

The radical mechanism of biological methane synthesis by methyl-coenzyme M reductase

Wongnate, T., Sliwa, D., Ginovska, B., Smith, D., Wolf, M. W., Lehnert, N., Raugei, S., & Ragsdale, S. W. (2016) · Science · doi:10.1126/science.aaf0616 · Cited by 170

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

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

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

Group 4 Metal Complexes Bearing Thioetherphenolate Ligands. Coordination Chemistry and Ring-Opening Polymerization Catalysis

Della Monica, F., Luciano, E., Roviello, G., Grassi, A., Milione, S., & Capacchione, C. (2014) · Macromolecules · doi:10.1021/ma5003358 · Cited by 74

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

Phosphorus-doped carbon nanotubes supported low Pt loading catalyst for the oxygen reduction reaction in acidic fuel cells

Liu, Z., Shi, Q., Zhang, R., Wang, Q., Kang, G., & Peng, F. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.06.036 · Cited by 70

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

Deactivation behavior of Pd/C and Pt/C catalysts in the gas-phase hydrodechlorination of chloromethanes: Structure–reactivity relationship

Martin-Martinez, M., Álvarez-Montero, A., Gómez-Sainero, L. M., R.T.Baker, Palomar, J., Omar, S., Eser, S., & Rodriguez, J. J. (2015) · Applied Catalysis B: Environmental · doi:10.1016/j.apcatb.2014.07.017 · Cited by 56

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

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

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

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

Density functional theory study of model complexes for the revised nitrate reductase active site in Desulfovibrio desulfuricans NapA

Hofmann, M. (2009) · JBIC Journal of Biological Inorganic Chemistry · doi:10.1007/s00775-009-0545-1 · Cited by 35

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

A computational insight into a metal mediated pathway for the ring-opening polymerization (ROP) of lactides by an ionic {(NHC)2Ag}+X− (X = halide) type N-heterocyclic carbene (NHC) complex

Stephen, R., Sunoj, R. B., & Ghosh, P. (2011) · Dalton Transactions · doi:10.1039/c1dt10345h · Cited by 23

Necessity of fine tuning in Mo(iv) bis(dithiolene) complexes to warrant nitrate reduction

Majumdar, A., Pal, K., & Sarkar, S. (2009) · Dalton Transactions · doi:10.1039/b815436h · Cited by 19

NSSN-Type Group 4 Metal Complexes in the Ring-Opening Polymerization of l-Lactide

Impemba, S., Roviello, G., Milione, S., & Capacchione, C. (2021) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.1c01056 · Cited by 17

Dinuclear zirconium complex bearing a 1,5-bridged-calix [8]arene ligand as an effective catalyst for the synthesis of macrolactones

Lapenta, R., De Simone, N. A., Buonerba, A., Talotta, C., Gaeta, C., Neri, P., Grassi, A., & Milione, S. (2018) · Catalysis Science & Technology · doi:10.1039/c7cy02537h · Cited by 16

Electronic influence of ligand substituents in the ring-opening polymerization of l-Lactide promoted by OSSO-type zirconium complexes

Meduri, A., Mazzeo, M., Lamberti, M., Capacchione, C., & Milione, S. (2019) · Molecular Catalysis · doi:10.1016/j.mcat.2019.04.007 · Cited by 14

Titanium complexes bearing 2,6-bis(o-hydroxyalkyl)pyridine ligands in the ring-opening polymerization of L-lactide and ε-caprolactone

Lai, F.-J., Huang, T.-W., Chang, Y.-L., Chang, H.-Y., Lu, W.-Y., Ding, S., Chen, H.-Y., Chiu, C.-C., & Wu, K.-H. (2020) · Polymer · doi:10.1016/j.polymer.2020.122860 · Cited by 12

Synthesis and Characterization of N,N,O-Tridentate Aminophenolate Zinc Complexes and Their Catalysis in the Ring-Opening Polymerization of Lactides

Lu, W.-Y., Wu, K.-H., Chen, H.-Y., & Lin, C.-C. (2019) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00189 · Cited by 5

Theoretical insight into the opposite redox activity of iron complexes toward the ring opening polymerization of lactide and epoxide

Xu, X., Lu, H., Luo, G., Kang, X., & Luo, Y. (2021) · Inorganic Chemistry Frontiers · doi:10.1039/d0qi01306d · Cited by 5

Conformationally Adaptive SERS Receptors: Reconfiguring Binding Environments via Dynamic Gauche – Trans Transitions for Broad-Spectrum Ion Sensing

Chen, Y., Nguyen, L. B. T., Zhong, Q.-Z., Tan, E. X., Wang, L., Phang, I. Y., Lv, Y., & Ling, X. Y. (2026) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.6c01066