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RESEARCH PAPER · Chemical Science · 2021

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

Chenru Duan, Shuxin Chen, Michael G. Taylor, Fang Liu, Heather J. Kulik

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Machine learning to tame divergent density functional approximations: a new path to consensus materials design principlesOriginalAudacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery98Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost24Unlocking the computational design of metal–organic cages66Computational Scaling Relationships Predict Experimental Activity and Rate-Limiting Behavior in Homogeneous Water Oxidation9Chalcogen 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 Dichalcogenides21Multireference Methods are Realistic and Useful Tools for Modeling Catalysis36Barriers to predictive high-throughput screening for spin-crossover20Machine Learning for the Discovery, Design, and Engineering of Materials66Understanding the chemical bonding of ground and excited states of HfO and HfB with correlated wavefunction theory and density functional approximations20Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks0The first principle study of chalcogen bonds, pnicogen bond and their mutual effects in a set of complexes between the triazine with SHF and PH2F ligands5Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores37A transferable recommender approach for selecting the best density functional approximations in chemical discovery31Gas-phase and solid-state electronic structure analysis and DFT benchmarking of HfCO10Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models4QM/MM study of N501 involved intermolecular interaction between SARS-CoV-2 receptor binding domain and antibody of human origin6Machine Learning Frontier Orbital Energies of Nanodiamonds8Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes21Assessing the performance of approximate density functional theory on 95 experimentally characterized Fe(II) spin crossover complexes30Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets3Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets33Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes16A study on ship hull form transformation using convolutional autoencoder12Identifying and embedding transferability in data-driven representations of chemical space22Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning27Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning1Multitask methods for predicting molecular properties from heterogeneous data10Many-body expansion based machine learning models for octahedral transition metal complexes2Force-Activated Spin-Crossover in Fe 2+ and Co 2+ Transition Metal Mechanophores7Adapting hybrid density functionals with machine learning22Using experimental data in computationally guided rational design of inorganic materials with machine learning7Incorporating Anionic Ligands in Chemical Space Exploration with New Ligand Additivity Relationships1Feature-Driven Prediction of HOMO–LUMO Gaps in Transition-Metal Complexes Using the SLEET Model: A SMILES-Based Transformer Framework2Revealing the Impact of Aggregations in the Graph‐Based Molecular Machine Learning: Electrostatic Interaction Versus Pooling Methods1Are we there yet? Adventures on a road trip through machine learning as a computational chemist2System-Specific Reparameterization of Density Functionals with Machine Learning: Application to Spin-Splitting Energies of Transition Metal Complexes3Accelerating single-atom ORR catalyst discovery through theory and machine learning: a critical review13A Review on the Application of Machine Learning in Nitrogen Reduction Reaction0BOS-TMC Data Set: DFT Properties of 159k Experimentally Characterized Transition Metal Complexes Spanning Multiple Charge and Spin States1

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

    Current Opinion in Chemical Engineering · 2021-12-17

    Aditya Nandy, Chenru Duan, Heather J. Kulik

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

    Chemical Science · 2022-01-01

    Chenru Duan, Daniel B. K. Chu, Aditya Nandy, Heather J. Kulik

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  3. Unlocking the computational design of metal–organic cages

    Chemical Communications · 2022-01-01

    Andrew Tarzia, Kim E. Jelfs

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

    Inorganic Chemistry · 2022-01-17

    Daniel R. Harper, Heather J. Kulik

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  5. 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

    International Journal of Molecular Sciences · 2022-01-23

    Pradeep R. Varadwaj, Arpita Varadwaj, Helder M. Marques, Koichi Yamashita

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  6. Multireference Methods are Realistic and Useful Tools for Modeling Catalysis

    Israel Journal of Chemistry · 2022-02-01

    Jenny G. Vitillo, Christopher J. Cramer, Laura Gagliardi

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  7. Barriers to predictive high-throughput screening for spin-crossover

    Computational Materials Science · 2022-02-11

    Daniel Mejı́a-Rodrı́guez, Angel Albavera-Mata, Eric Fonseca, Dian-Teng Chen, H-P. Cheng, Richard G. Hennig, Samuel B. Trickey

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

    Annual Review of Chemical and Biomolecular Engineering · 2022-03-23

    Chenru Duan, Aditya Nandy, Heather J. Kulik

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

    The Journal of Chemical Physics · 2022-04-25

    Isuru Rangana Ariyarathna, Chenru Duan, Heather J. Kulik

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

    ACS symposium series · 2022-06-14

    Jon Paul Janet

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  11. 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

    Computational and Theoretical Chemistry · 2022-08-29

    Mahdiyeh Ranjbar, Alireza Nowroozi, Ebrahim Nakhaei

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

    JACS Au · 2022-12-01

    Chenru Duan, Aditya Nandy, Gianmarco Terrones, David W. Kastner, Heather J. Kulik

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

    Nature Computational Science · 2022-12-22

    Chenru Duan, Aditya Nandy, Ralf Meyer, Naveen Arunachalam, Heather J. Kulik

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

    Physical Chemistry Chemical Physics · 2023-01-01

    Isuru Rangana Ariyarathna, Yeongsu Cho, Chenru Duan, Heather J. Kulik

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

    Physical Chemistry Chemical Physics · 2023-01-01

    Yael Cytter, Aditya Nandy, Chenru Duan, Heather J. Kulik

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

    Computational Biology and Chemistry · 2023-01-04

    Yuemin Liu, Hana F. Sulaiman, Bruce R. Johnson, Rulong Ma, Yunxiang Gao, Harshica Fernando, Ananda S. Amarasekara, Andrea Ashley-Oyewole, Hua‐Jun Fan, Heaven N. Ingram, James M. Briggs

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  17. Machine Learning Frontier Orbital Energies of Nanodiamonds

    Journal of Chemical Theory and Computation · 2023-04-13

    Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande, Frank Noé

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

    The Journal of Physical Chemistry Letters · 2023-06-20

    Aditya Nandy, Michael G. Taylor, Heather J. Kulik

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

    The Journal of Chemical Physics · 2023-07-11

    Vyshnavi Vennelakanti, Michael G. Taylor, Aditya Nandy, Chenru Duan, Heather J. Kulik

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

    ChemRxiv · 2023-09-08

    Maria Harris Rasmussen, Chenru Duan, Heather J. Kulik, Jan Halborg Jensen

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

    Journal of Cheminformatics · 2023-12-18

    Maria Harris Rasmussen, Chenru Duan, Heather J. Kulik, Jan Halborg Jensen

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

    The Journal of Physical Chemistry A · 2023-12-27

    Vyshnavi Vennelakanti, Irem B. Kilic, Gianmarco Terrones, Chenru Duan, Heather J. Kulik

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  23. A study on ship hull form transformation using convolutional autoencoder

    Journal of Computational Design and Engineering · 2023-12-28

    Jeongbeom Seo, Dayeon Kim, Inwon Lee

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

    Chemical Science · 2024-01-01

    Tim Gould, Bun Chan, Stephen G. Dale, Stefan Vuckovic

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

    ChemCatChem · 2024-01-05

    S. Macé, Yingjian Xu, Bao N. Nguyen

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  26. Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning

    Unknown journal · 2024-05-17

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  27. Multitask methods for predicting molecular properties from heterogeneous data

    The Journal of Chemical Physics · 2024-07-03

    K. E. Fisher, Michael F. Herbst, Youssef Marzouk

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

    Machine Learning Science and Technology · 2024-12-01

    Ralf Meyer, Daniel B. K. Chu, Heather J. Kulik

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

    Inorganic Chemistry · 2024-12-23

    Xiao Huang, Ilia Kevlishvili, Stephen L. Craig, Heather J. Kulik

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  30. Adapting hybrid density functionals with machine learning

    Science Advances · 2025-01-31

    Danish Khan, Alastair J. A. Price, Bing Huang, Maximilian L. Ach, O. Anatole von Lilienfeld

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

    Journal of materials research/Pratt's guide to venture capital sources · 2025-03-30

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

    Journal of Chemical Information and Modeling · 2025-06-03

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

    Journal of Chemical Theory and Computation · 2025-06-23

    Sheng-Hsuan Hung, Zong-Rong Ye, Chi‐Feng Cheng, An-Cheng Yang, Berlin Chen, Ming‐Kang Tsai

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

    Advanced Theory and Simulations · 2025-08-12

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

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

    Journal of Chemical Theory and Computation · 2026-02-17

    Aaron G. Garrison, Heather J. Kulik

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

    Coordination Chemistry Reviews · 2026-03-21

    Anuj Kumar, Parth Bishnoi, Akshay Parmar, Mohammad Khalid, Mohd Ubaidullah, Abdullah M. Al‐Enizi

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

    ChemistrySelect · 2026-08-01

    Baskaran Kannan, Saranya Chandrasekaran, Velu Sagadevan, Shankar Raman Dhanushkodi

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

    Journal of Chemical Information and Modeling · 2026-08-17

    Aaron G. Garrison, Jacob Toney, Tatiana Nikolaeva, Roland G. St. Michel, Christopher Johannes Stein, Heather J. Kulik

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