Total (up to Dec. 2022): 32 published, 1 in press, 1 submitted, 18 first-authored. Google scholar page (link)
34. C. Duan, A. Nandy, G. Terrones, D. W. Kastner, H. J. Kulik, “Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores”, JACS Au, in press (2022) (link)
33. C. Duan, A. Nandy, R. Meyer, N. Arunachalam, and H. J. Kulik, “A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery", submitted (2022) (link)
32. C. Duan, A. Nandy, H. J. Kulik, “A Density Functional Recommendation Approach for Accurate Predictions of Vertical Spin Splitting of Transition Metal Complexes”, accepted as oral presentation in ICML AI4Science workshop (2022) (link)
31. C. Duan, A. J. Ladera, J. C.-L. Liu, M.. G. Taylor, I. R. Ariyarathna, and H. J. Kulik, “Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands”, J. Chem. Theory Comput., 18, 8, 4836–4845 (2022), (link)
30. A. Bajaj, C. Duan, A.Nandy, M. G. Taylor, and H. J. Kulik, “Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition-metal chemistry”, J. Chem. Phys. 156, 184112 (2022) (link)
29. I. R. Ariyarathna, C. Duan, and H. J. Kulik, “Understanding the chemical bonding of ground and excited states of HfO and HfB with correlated wavefunction theory and density functional approximations”, J. Chem. Phys. 156, 184113 (2022) (link)
28. A. Nandy, C. Duan, C. Coffinet, and H. J. Kulik, "New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts”, submitted (2022) (link)
27. C. Duan, A. Nandy, H. Adamji, Y. Roman-Leshkov, and H. J. Kulik, “Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis”, submitted (2022) (link)
26. C. Duan, D. B. K. Chu, A. Nandy, and H. J. Kulik, “Detection of Multi-Reference Character Imbalances Enables a Transfer Learning Approach for Virtual High Throughput Screening with Coupled Cluster Accuracy at DFT Cost”, submitted (2022) (link)
25. A. Nandy†, G. Terrones†, N. Arunachalam, C. Duan, D. W. Kastner, and H. J. Kulik, “MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal–Organic Frameworks”, Sci. Data, 9, 74, (2022) (link)
24. C. Duan†, A. Nandy†, and H. J. Kulik, “Machine Learning for the Discovery, Design, and Engineering of Materials”, Annu. Rev. Chem. Biomol. Eng. in press (2022)
23. M. Liu, A. Nazemi, M. G. Taylor, A. Nandy, C. Duan, A. H. Steeves, and H. J. Kulik, “Large-scale Screening Reveals Geometric Structure Matters More than Electronic Structure in Bioinspired Catalyst Design of Formate Dehydrogenase Mimics”, ACS Catal., 12, 1, 383–396, (2021) (link)
22. A. Nandy†, C. Duan†, and H. J. Kulik, “Audacity of Huge: Overcoming Challenges of Data Scarcity and Data Quality for Machine Learning in Computational Materials Discovery”, Curr. Opin. Chem. Eng., 36, 100778 (2021) (link)
21. A. Nandy, C. Duan, and H. J. Kulik, “Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks”, J. Am. Chem. Soc., 143, 42, 17535–17547 (2021) (link)
20. C. Duan, S. Chen, M. G. Taylor, F. Liu, and H. J. Kulik, “Machine Learning to Tame Divergent Density Functional Approximations: A New Path to Consensus Materials Design Principles”, Chem. Sci, 12, 39, 13021-13036 (2021) (link)
19. A. Nandy†, C. Duan†, M. G. Taylor, F. Liu, A. H. Steeves, and H. J. Kulik, “Computational Discovery of Transition-Metal Complexes: From High-throughput Screening to Machine Learning”, Chem. Rev. 121, 16, 9927–10000 (2021) (link)
18. C. Duan, F. Liu, A. Nandy, and H. J. Kulik, “Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery”, J. Phys. Chem. Lett. 12, 19, 4628–4637 (2021) (link)
17. J. P. Janet, C. Duan, A. Nandy, F. Liu, and H. J. Kulik, “Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design”, Acc. Chem. Res., 54, 3, 532–545 (2021) (link)
16. F. Liu, C. Duan, H. J. Kulik, “Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening”, J. Phys. Chem. Lett., 11, 19, 8067–8076 (2020) (link)
15. C. Duan, F. Liu, A. Nandy, H. J. Kulik, “Semi-Supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost”, J. Phys. Chem. Lett. 11, 16, 6640–6648 (2020) (link)
14. C. Duan, F. Liu, A. Nandy, H. J. Kulik, “Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-off in Multireference Diagnostics”, J. Chem. Theory Comput. 16, 7, 4373–4387 (2020) (link)
13. M. G. Taylor†, T. Yang†, S. Lin†, A. Nandy, J. P. Janet, C. Duan, and H. J. Kulik, “Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions”, J. Phys. Chem. A 124, 16, 3286–3299 (2020) (link)
12. C. Duan, C.-Y. Hsieh, J. Liu, J. Wu and J. Cao, “Unusual Transport Properties with Non-Commutative System-Bath Coupling Operators”, J. Phys. Chem. Lett., 11, 10, 4080–4085 (2020) (link)
11. J. P. Janet, S. Ramesh, C. Duan, H. J. Kulik, “Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization”, ACS Cent. Sci., 6, 413–524 (2020) (link)
10. A. Nandy†, D. B. K. Chu†, D. R. Harper, C. Duan, N. Arunachalam, Y. Cytter, and H. J. Kulik, “Large-Scale Comparison of 3d and 4d Transition Metal Complexes Illuminates the Reduced Effect of Exchange on Second-Row Spin-State Energetics”, Phys. Chem. Chem. Phys. 22, 19326-19341 (2020) (link)
9. A. Nandy, J. Zhou, J. P. Janet, C. Duan, R. B. Getman, and H. J. Kulik, “Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal−Oxo Intermediate Formation”, ACS Catal. 9, 9, 8243–8255 (2019) (link)
8. J. P. Janet, F. Liu, A. Nandy, C. Duan, T. Yang, S. Lin, and H. J. Kulik, “Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry”, Inorg. Chem. 58, 16, 10592–10606 (2019) (link)
7. J. P. Janet, C. Duan, T. Yang, A. Nandy, and H. J. Kulik, “A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery”, Chem. Sci. 10, 7913-7922 (2019) (link)
6. C. Duan, J. P. Janet, F. Liu, A. Nandy, and H. J. Kulik, “Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models”, J. Chem. Theory Comput. 15, 4, 2331–2345 (2019) (link)
5. C.-Y. Hsieh, J. Liu, C. Duan, and J. Cao, “A Nonequilibrium Variational Polaron Theory to Study Quantum Heat Transport”, J. Phys. Chem. C 123, 28, 17196–17204 (2019) (link)
4. Q. Wang, Z. Gong, C. Duan, Z. Tang, and J. Wu, “Dynamical Scaling in the Ohmic Spin-Boson Model Studied by Extended Hierarchical Equations of Motion”, J. Chem. Phys. 150, 084114 (2019) (link)
3. A. Nandy†, C. Duan†, J. P. Janet, S. Gugler, and H. J. Kulik, “Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry”, Ind. Eng. Chem. Res. 57, 42, 13973–13986 (2018) (link)
2. C. Duan, Q. Wang, Z. Tang, and J. Wu, “The Study of an Extended Hierarchy Equation of Motion in the Spin-Boson Model: The Cutoff Function of the Sub-Ohmic Spectral Density”, J. Chem. Phys. 147, 164112 (2017) (link)
1. C. Duan, Z. Tang, J. Cao, and J. Wu, “Zero-Temperature Localization in a Sub-Ohmic Spin-Boson Model Investigated by an Extended Hierarchy Equation of Motion”, Phys. Rev. B 95, 214308 (2017) (link)
† These authors contribute equally.