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

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Iron(II) and Iron(III) Spin Crossover: Toward an Optimal Density Functional

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Spin Propensities of Octahedral Complexes From Density Functional Theory

Mortensen, S. R., & Kepp, K. P. (2015) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.5b01626 · Cited by 97 · see citing works

Evaluation of the Performance of the B3LYP, PBE0, and M06 DFT Functionals, and DBLOC-Corrected Versions, in the Calculation of Redox Potentials and Spin Splittings for Transition Metal Containing Systems

Coskun, D., Jerome, S. V., & Friesner, R. A. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00782 · Cited by 94 · see citing works

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 · see citing works

DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory

Chen, Y., Zhang, L., Wang, H., & Weinan, E. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00872 · Cited by 88 · see citing works

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 · see citing works

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 · see citing works

Simulated evolution of fluorophores for light emitting diodes

Shu, Y., & Levine, B. G. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4914294 · Cited by 79 · see citing works

Identifying Active Sites of the Water–Gas Shift Reaction over Titania Supported Platinum Catalysts under Uncertainty

Walker, E. A., Mitchell, D., Terejanu, G., & Heyden, A. (2018) · ACS Catalysis · doi:10.1021/acscatal.7b03531 · Cited by 66 · see citing works

Breaking the Correlation between Energy Costs and Kinetic Barriers in Hydrogen Evolution via a Cobalt Pyridine-Diimine-Dioxime Catalyst

Huo, P., Uyeda, C., Goodpaster, J. D., Peters, J. C., & Miller, T. F. (2016) · ACS Catalysis · doi:10.1021/acscatal.6b01387 · Cited by 64 · see citing works

Spin Splitting Energy of Transition Metals: A New, More Affordable Wave Function Benchmark Method and Its Use to Test Density Functional Theory

Zhang, D., & Truhlar, D. G. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00518 · Cited by 61 · see citing works

Ligand-Field-Dependent Behavior of Meta-GGA Exchange in Transition-Metal Complex Spin-State Ordering

Ioannidis, E. I., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.6b11930 · Cited by 60 · see citing works

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 · see citing works

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Janet, J. P., Duan, C., Nandy, A., Liu, F., & Kulik, H. J. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00686 · Cited by 56 · see citing works

Bridging the Homogeneous-Heterogeneous Divide: Modeling Spin for Reactivity in Single Atom Catalysis

Liu, F., Yang, T., Yang, J., Xu, E. Y., Bajaj, A., & Kulik, H. J. (2019) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00219 · Cited by 55 · see citing works

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

Duan, C., Liu, F., Nandy, A., & Kulik, H. J. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c00631 · Cited by 55 · see citing works

Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics

Duan, C., Liu, F., Nandy, A., & Kulik, H. J. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00358 · Cited by 47 · see citing works

Computational Investigation and Design of Cobalt Aqua Complexes for Homogeneous Water Oxidation

Schilling, M., Patzke, G. R., Hutter, J., & Luber, S. (2016) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.6b00712 · Cited by 46 · see citing works

Spin‐State Energetics of Fe Complexes from an Optimally Tuned Range‐Separated Hybrid Functional

Prokopiou, G., & Kronik, L. (2017) · Chemistry - A European Journal · doi:10.1002/chem.201704014 · Cited by 44 · see citing works

Correction: MN15: A Kohn–Sham global-hybrid exchange–correlation density functional with broad accuracy for multi-reference and single-reference systems and noncovalent interactions

Yu, H. S., He, X. D., Li, S. L., & Truhlar, D. G. (2016) · Chemical Science · doi:10.1039/c6sc90044e · Cited by 44 · see citing works

Enumeration of <i>de novo</i> inorganic complexes for chemical discovery and machine learning

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

How Large is the Elephant in the Density Functional Theory Room?

Jensen, F. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b04760 · Cited by 40 · see citing works

Spin-state diversity in a series of Co( ii ) PNP pincer bromide complexes

Shaffer, D. W., Bhowmick, I., Rheingold, A. L., Tsay, C., Livesay, B. N., Shores, M. P., & Yang, J. Y. (2016) · Dalton Transactions · doi:10.1039/c6dt03461f · Cited by 36 · see citing works

Communication: Evaluating non-empirical double hybrid functionals for spin-state energetics in transition-metal complexes

Wilbraham, L., Adamo, C., & Ciofini, I. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019641 · Cited by 36 · see citing works

Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

Duan, C., Liu, F., Nandy, A., & Kulik, H. J. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02018 · Cited by 35 · see citing works

Method Calibration or Data Fitting?

Jensen, F. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00477 · Cited by 30 · see citing works

Density Functionals for Hydrogen Storage: Defining the H2Bind275 Test Set with Ab Initio Benchmarks and Assessment of 55 Functionals

Veccham, S. P., & Head‐Gordon, M. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00292 · Cited by 27 · see citing works

Rational Density Functional Selection Using Game Theory

McAnanama-Brereton, S. R., & Waller, M. P. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00542 · Cited by 23 · see citing works

Density Functional Theory and the Basis Set Truncation Problem with Correlation Consistent Basis Sets: Elephant in the Room or Mouse in the Closet?

Feller, D. F., & Dixon, D. A. (2018) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.8b00392 · Cited by 23 · see citing works

Accurate Hybrid Density Functionals with UW12 Correlation

Williams, Z., Wiles, T. C., & Manby, F. R. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00442 · Cited by 10 · see citing works

Simple Automatized Tool for Exchange–Correlation Functional Fitting

Mitrofanov, A. A., Korolev, V., Andreadi, N., Petrov, V. G., & Kalmykov, S. N. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b09093 · Cited by 6 · see citing works

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Nandy, A., Duan, C., & Kulik, H. J. (2021) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2021.100778 · Cited by 103 · see citing works

Machine Learning for the Discovery, Design, and Engineering of Materials.

Duan, C., Nandy, A., & Kulik, H. J. (2022) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092320-120230 · Cited by 69 · see citing works

Unlocking the computational design of metal–organic cages

Tarzia, A., & Jelfs, K. E. (2022) · Chemical Communications · doi:10.1039/d2cc00532h · Cited by 66 · see citing works

Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores

Duan, C., Nandy, A., Terrones, G., Kastner, D. W., & Kulik, H. J. (2022) · JACS Au · doi:10.1021/jacsau.2c00547 · Cited by 39 · see citing works

Multireference Methods are Realistic and Useful Tools for Modeling Catalysis

Vitillo, J. G., Cramer, C. J., & Gagliardi, L. (2022) · Israel Journal of Chemistry · doi:10.1002/ijch.202100136 · Cited by 36 · see citing works

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets

Rasmussen, M. H., Duan, C., Kulik, H. J., & Jensen, J. H. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00790-0 · Cited by 34 · see citing works

A transferable recommender approach for selecting the best density functional approximations in chemical discovery

Duan, C., Nandy, A., Meyer, R., Arunachalam, N., & Kulik, H. J. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00384-0 · Cited by 31 · see citing works

Assessing the performance of approximate density functional theory on 95 experimentally characterized Fe(II) spin crossover complexes

Vennelakanti, V., Taylor, M. G., Nandy, A., Duan, C., & Kulik, H. J. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0157187 · Cited by 30 · see citing works

Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning

Macé, S., Xu, Y., & Nguyen, B. N. (2024) · ChemCatChem · doi:10.1002/cctc.202301475 · Cited by 27 · see citing works

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

Duan, C., Chu, D. B. K., Nandy, A., & Kulik, H. J. (2022) · Chemical Science · doi:10.1039/d2sc00393g · Cited by 26 · see citing works

Adapting hybrid density functionals with machine learning

Khan, D., Price, A. J. A., Huang, B., Ach, M. L., & von Lilienfeld, O. A. (2025) · Science Advances · doi:10.1126/sciadv.adt7769 · Cited by 23 · see citing works

Identifying and embedding transferability in data-driven representations of chemical space

Gould, T., Chan, B., Dale, S. G., & Vuckovic, S. (2024) · Chemical Science · doi:10.1039/d4sc02358g · Cited by 23 · see citing works

Chalcogen Bonding in the Molecular Dimers of WCh2 (Ch = S, Se, Te): On the Basic Understanding of the Local Interfacial and Interlayer Bonding Environment in 2D Layered Tungsten Dichalcogenides

Varadwaj, P. R., Varadwaj, A., Marques, H. M., & Yamashita, K. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23031263 · Cited by 21 · see citing works

Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes

Nandy, A., Taylor, M. G., & Kulik, H. J. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c01214 · Cited by 21 · see citing works

Barriers to predictive high-throughput screening for spin-crossover

Mejı́a-Rodrı́guez, D., Albavera-Mata, A., Fonseca, E., Chen, D.-T., Cheng, H.-P., Hennig, R. G., & Trickey, S. B. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2021.111161 · Cited by 20 · see citing works

Understanding the chemical bonding of ground and excited states of HfO and HfB with correlated wavefunction theory and density functional approximations

Ariyarathna, I. R., Duan, C., & Kulik, H. J. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0090128 · Cited by 20 · see citing works

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes

Vennelakanti, V., Kilic, I. B., Terrones, G., Duan, C., & Kulik, H. J. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c07104 · Cited by 18 · see citing works

Accelerating single-atom ORR catalyst discovery through theory and machine learning: a critical review

Kumar, A., Bishnoi, P., Parmar, A., Khalid, M., Ubaidullah, M., & Al‐Enizi, A. M. (2026) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2026.217870 · Cited by 17 · see citing works

A study on ship hull form transformation using convolutional autoencoder

Seo, J., Kim, D., & Lee, I. (2023) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwad111 · Cited by 13 · see citing works

Multitask methods for predicting molecular properties from heterogeneous data

Fisher, K. E., Herbst, M. F., & Marzouk, Y. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0201681 · Cited by 10 · see citing works

Gas-phase and solid-state electronic structure analysis and DFT benchmarking of HfCO

Ariyarathna, I. R., Cho, Y., Duan, C., & Kulik, H. J. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d3cp03550f · Cited by 10 · see citing works

Computational Scaling Relationships Predict Experimental Activity and Rate-Limiting Behavior in Homogeneous Water Oxidation

Harper, D. R., & Kulik, H. J. (2022) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.1c03376 · Cited by 9 · see citing works

Machine Learning Frontier Orbital Energies of Nanodiamonds

Kirschbaum, T., von Seggern, B., Dzubiella, J., Bande, A., & Noé, F. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01275 · Cited by 8 · see citing works

Using experimental data in computationally guided rational design of inorganic materials with machine learning

Kulik, H. J. (2025) · Journal of materials research/Pratt's guide to venture capital sources · doi:10.1557/s43578-025-01568-w · Cited by 7 · see citing works

Force-Activated Spin-Crossover in Fe 2+ and Co 2+ Transition Metal Mechanophores

Huang, X., Kevlishvili, I., Craig, S. L., & Kulik, H. J. (2024) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.4c04732 · Cited by 7 · see citing works

QM/MM study of N501 involved intermolecular interaction between SARS-CoV-2 receptor binding domain and antibody of human origin

Liu, Y., Sulaiman, H. F., Johnson, B. R., Ma, R., Gao, Y., Fernando, H., Amarasekara, A. S., Ashley-Oyewole, A., Fan, H., Ingram, H. N., et al. (2023) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2023.107810 · Cited by 6 · see citing works

Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models

Cytter, Y., Nandy, A., Duan, C., & Kulik, H. J. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d3cp00258f · Cited by 5 · see citing works

The first principle study of chalcogen bonds, pnicogen bond and their mutual effects in a set of complexes between the triazine with SHF and PH2F ligands

Ranjbar, M., Nowroozi, A., & Nakhaei, E. (2022) · Computational and Theoretical Chemistry · doi:10.1016/j.comptc.2022.113867 · Cited by 5 · see citing works

Feature-Driven Prediction of HOMO–LUMO Gaps in Transition-Metal Complexes Using the SLEET Model: A SMILES-Based Transformer Framework

Hung, S.-H., Ye, Z.-R., Cheng, C., Yang, A.-C., Chen, B., & Tsai, M. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00085 · Cited by 3 · see citing works

System-Specific Reparameterization of Density Functionals with Machine Learning: Application to Spin-Splitting Energies of Transition Metal Complexes

Garrison, A. G., & Kulik, H. J. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c01833 · Cited by 3 · see citing works

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data setsPreprint

Rasmussen, M. H., Duan, C., Kulik, H. J., & Jensen, J. H. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-w93dm · Cited by 3 · see citing works

Are we there yet? Adventures on a road trip through machine learning as a computational chemist

Kulik, H. J. (2025) · APL Computational Physics · doi:10.1063/5.0297853 · Cited by 3 · see citing works

BOS-TMC Data Set: DFT Properties of 159k Experimentally Characterized Transition Metal Complexes Spanning Multiple Charge and Spin States

Garrison, A. G., Toney, J., Nikolaeva, T., Michel, R. G. S., Stein, C. J., & Kulik, H. J. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01792 · Cited by 2 · see citing works

Many-body expansion based machine learning models for octahedral transition metal complexes

Meyer, R., Chu, D. B. K., & Kulik, H. J. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad9f22 · Cited by 2 · see citing works

Incorporating Anionic Ligands in Chemical Space Exploration with New Ligand Additivity Relationships

Kulik, H. J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00636 · Cited by 1 · see citing works

Revealing the Impact of Aggregations in the Graph‐Based Molecular Machine Learning: Electrostatic Interaction Versus Pooling Methods

Lee, S., & Kim, H. W. (2025) · Advanced Theory and Simulations · doi:10.1002/adts.202500133 · Cited by 1 · see citing works

Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning

Nandy, A., & Kulik, H. J. (2024) · Wiley eBooks · doi:10.1002/9783527843435.ch20 · Cited by 1 · see citing works

Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks

Janet, J. P. (2022) · ACS symposium series · doi:10.1021/bk-2022-1416.ch007

Additional file 1 of Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets

Rasmussen, M. H., Duan, C., Kulik, H. J., & Jensen, J. H. (2024) · Figshare · doi:10.6084/m9.figshare.26655709

A Review on the Application of Machine Learning in Nitrogen Reduction Reaction

Kannan, B., Chandrasekaran, S., Sagadevan, V., & Dhanushkodi, S. R. (2026) · ChemistrySelect · doi:10.1002/slct.74100

A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery

Duan, C., Nandy, A., Meyer, R. C., Arunachalam, N., & Kulik, H. J. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.7350957 · Cited by 1 · see citing works

Machine learning for accuracy in density functional approximations

Voss, J. (2024) · Journal of Computational Chemistry · doi:10.1002/jcc.27366 · Cited by 10 · see citing works

PUTTING DENSITY FUNCTIONAL THEORY TO THE TEST WITH MACHINE LEARNING

Kulik, H. J. (2022) · International Symposium on Molecular Spectroscopy (ISMS) · doi:10.15278/isms.2022.tk04

Predictive Chemistry and Materials Science by Density Functional Theory: Accelerating New Discovery

Jung, Y. (2012)

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

Duan, C., Liu, F., Nandy, A., & Kulik, H. J. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c00631 · Cited by 55 · see citing works