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Chenru Duan - Publications

Affiliations: 
2017-2022 Massachusetts Institute of Technology, Cambridge, MA, United States 
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35 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2023 Duan C, Du Y, Jia H, Kulik HJ. Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model. Nature Computational Science. 3: 1045-1055. PMID 38177724 DOI: 10.1038/s43588-023-00563-7  0.565
2023 Vennelakanti V, Kilic IB, Terrones GG, Duan C, Kulik HJ. Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes. The Journal of Physical Chemistry. A. 128: 204-216. PMID 38148525 DOI: 10.1021/acs.jpca.3c07104  0.656
2023 Rasmussen MH, Duan C, Kulik HJ, Jensen JH. Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets. Journal of Cheminformatics. 15: 121. PMID 38111020 DOI: 10.1186/s13321-023-00790-0  0.572
2023 Kevlishvili I, Duan C, Kulik HJ. Classification of Hemilabile Ligands Using Machine Learning. The Journal of Physical Chemistry Letters. 14: 11100-11109. PMID 38051982 DOI: 10.1021/acs.jpclett.3c02828  0.8
2023 Ariyarathna IR, Cho Y, Duan C, Kulik HJ. Gas-phase and solid-state electronic structure analysis and DFT benchmarking of HfCO. Physical Chemistry Chemical Physics : Pccp. 25: 26632-26639. PMID 37767841 DOI: 10.1039/d3cp03550f  0.746
2023 Vennelakanti V, Taylor MG, Nandy A, Duan C, Kulik HJ. Assessing the performance of approximate density functional theory on 95 experimentally characterized Fe(II) spin crossover complexes. The Journal of Chemical Physics. 159. PMID 37431914 DOI: 10.1063/5.0157187  0.776
2023 Cytter Y, Nandy A, Duan C, Kulik HJ. Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models. Physical Chemistry Chemical Physics : Pccp. 25: 8103-8116. PMID 36876903 DOI: 10.1039/d3cp00258f  0.822
2023 Terrones GG, Duan C, Nandy A, Kulik HJ. Low-cost machine learning prediction of excited state properties of iridium-centered phosphors. Chemical Science. 14: 1419-1433. PMID 36794185 DOI: 10.1039/d2sc06150c  0.805
2022 Duan C, Nandy A, Meyer R, Arunachalam N, Kulik HJ. A transferable recommender approach for selecting the best density functional approximations in chemical discovery. Nature Computational Science. 3: 38-47. PMID 38177951 DOI: 10.1038/s43588-022-00384-0  0.804
2022 Duan C, Nandy A, Terrones GG, Kastner DW, Kulik HJ. Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores. Jacs Au. 3: 391-401. PMID 36873700 DOI: 10.1021/jacsau.2c00547  0.821
2022 Cho Y, Nandy A, Duan C, Kulik HJ. DFT-Based Multireference Diagnostics in the Solid State: Application to Metal-Organic Frameworks. Journal of Chemical Theory and Computation. 19: 190-197. PMID 36548116 DOI: 10.1021/acs.jctc.2c01033  0.776
2022 Arunachalam N, Gugler S, Taylor MG, Duan C, Nandy A, Janet JP, Meyer R, Oldenstaedt J, Chu DBK, Kulik HJ. Ligand additivity relationships enable efficient exploration of transition metal chemical space. The Journal of Chemical Physics. 157: 184112. PMID 36379790 DOI: 10.1063/5.0125700  0.786
2022 Duan C, Ladera AJ, Liu JC, Taylor MG, Ariyarathna IR, Kulik HJ. Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands. Journal of Chemical Theory and Computation. PMID 35834742 DOI: 10.1021/acs.jctc.2c00468  0.79
2022 Duan C, Nandy A, Adamji H, Roman-Leshkov Y, Kulik HJ. Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis. Journal of Chemical Theory and Computation. PMID 35737587 DOI: 10.1021/acs.jctc.2c00331  0.798
2022 Duan C, Chu DBK, Nandy A, Kulik HJ. 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. 13: 4962-4971. PMID 35655882 DOI: 10.1039/d2sc00393g  0.793
2022 Nandy A, Duan C, Goffinet C, Kulik HJ. New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts. Jacs Au. 2: 1200-1213. PMID 35647589 DOI: 10.1021/jacsau.2c00176  0.77
2022 Bajaj A, Duan C, Nandy A, Taylor MG, Kulik HJ. Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition-metal chemistry. The Journal of Chemical Physics. 156: 184112. PMID 35568542 DOI: 10.1063/5.0089460  0.788
2022 Ariyarathna IR, Duan C, Kulik HJ. 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. 156: 184113. PMID 35568536 DOI: 10.1063/5.0090128  0.761
2022 Duan C, Nandy A, Kulik HJ. Machine Learning for the Discovery, Design, and Engineering of Materials. Annual Review of Chemical and Biomolecular Engineering. PMID 35320698 DOI: 10.1146/annurev-chembioeng-092320-120230  0.766
2022 Nandy A, Terrones G, Arunachalam N, Duan C, Kastner DW, Kulik HJ. MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworks. Scientific Data. 9: 74. PMID 35277533 DOI: 10.1038/s41597-022-01181-0  0.778
2022 Harper DR, Nandy A, Arunachalam N, Duan C, Janet JP, Kulik HJ. Representations and strategies for transferable machine learning improve model performance in chemical discovery. The Journal of Chemical Physics. 156: 074101. PMID 35183086 DOI: 10.1063/5.0082964  0.811
2021 Duan C, Chen S, Taylor MG, Liu F, Kulik HJ. Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles. Chemical Science. 12: 13021-13036. PMID 34745533 DOI: 10.1039/d1sc03701c  0.786
2021 Nandy A, Duan C, Kulik HJ. Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks. Journal of the American Chemical Society. 143: 17535-17547. PMID 34643374 DOI: 10.1021/jacs.1c07217  0.796
2021 Nandy A, Duan C, Taylor MG, Liu F, Steeves AH, Kulik HJ. Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning. Chemical Reviews. PMID 34260198 DOI: 10.1021/acs.chemrev.1c00347  0.832
2021 Duan C, Liu F, Nandy A, Kulik HJ. Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery. The Journal of Physical Chemistry Letters. 4628-4637. PMID 33973793 DOI: 10.1021/acs.jpclett.1c00631  0.831
2021 Janet JP, Duan C, Nandy A, Liu F, Kulik HJ. Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design. Accounts of Chemical Research. PMID 33480674 DOI: 10.1021/acs.accounts.0c00686  0.837
2020 Liu F, Duan C, Kulik HJ. Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening. The Journal of Physical Chemistry Letters. PMID 32864977 DOI: 10.1021/acs.jpclett.0c02288  0.789
2020 Nandy A, Chu DBK, Harper DR, Duan C, Arunachalam N, Cytter Y, Kulik HJ. Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics. Physical Chemistry Chemical Physics : Pccp. PMID 32820781 DOI: 10.1039/d0cp02977g  0.769
2020 Duan C, Liu F, Nandy A, Kulik HJ. Semi-Supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost. The Journal of Physical Chemistry Letters. PMID 32692570 DOI: 10.1021/acs.jpclett.0c02018  0.815
2020 Duan C, Liu F, Nandy A, Kulik HJ. Data-Driven Approaches Can Overcome the Cost-Accuracy Trade-off in Multireference Diagnostics. Journal of Chemical Theory and Computation. PMID 32536161 DOI: 10.1021/acs.jctc.0c00358  0.813
2020 Janet JP, Ramesh S, Duan C, Kulik HJ. Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization. Acs Central Science. 6: 513-524. PMID 32342001 DOI: 10.1021/acscentsci.0c00026  0.656
2020 Taylor MG, Yang T, Lin S, Nandy A, Janet JP, Duan C, Kulik HJ. Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions. The Journal of Physical Chemistry. A. PMID 32223165 DOI: 10.1021/Acs.Jpca.0C01458  0.803
2019 Janet JP, Duan C, Yang T, Nandy A, Kulik HJ. A quantitative uncertainty metric controls error in neural network-driven chemical discovery. Chemical Science. 10: 7913-7922. PMID 31588334 DOI: 10.1039/c9sc02298h  0.78
2019 Duan C, Janet JP, Liu F, Nandy A, Kulik HJ. Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models. Journal of Chemical Theory and Computation. PMID 30860839 DOI: 10.1021/acs.jctc.9b00057  0.826
2019 Janet JP, Liu F, Nandy A, Duan C, Yang T, Lin S, Kulik HJ. Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry. Inorganic Chemistry. PMID 30834738 DOI: 10.1021/acs.inorgchem.9b00109  0.82
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