Reprints for all articles available upon e-mail request

Refereed Journal Articles

  1. Oh, C., Nandy, A., Yue, S., & Kulik, H. J. (2024). MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules. Submitted.
  2. Pitt, T., Jia, H., Azbell, T. J., Zick, M. E., Nandy, A., Kulik, H. J., & Milner, P. J. (2024). Benchmarking N_2O Adsorption and Activation in Coordinatively Unsaturated Metal-Organic Frameworks. J. Mater. Chem. C, 12, 3164–3174.
  3. Jia, H., Duan, C., Kevlishvili, I., Nandy, A., Liu, M., & Kulik, H. J. (2024). Computational Discovery of Co-doped Single-Atom Catalysts for Methane-to-Methanol Conversion. ACS Catal., 14, 2992–3005.
  4. Adamji, H., Kevlishvili, I., Nandy, A., Roman-Leshkov, Y., & Kulik, H. J. (2024). Large-scale Comparison of Fe and Ru Polyolefin C–H Activation Catalysts. J. Catal., 431, 115361.
  5. Edholm, F., Nandy, A., Reinhardt, C., Kastner, D. W., & Kulik, H. J. (2024). Protein3D: Enabling Analysis and Extraction of Metal-Containing Sites from the Protein Data Bank with molSimplify. J. Comput. Chem., 45, 352–361.
  6. Yue, S., Nandy, A., & Kulik, H. J. (2023). Discovering Molecular Coordination Environments for Selective Ion Binding Using Machine Learning. J. Phys. Chem. B, 127, 10592–10600.
  7. Vennelakanti, V., Taylor, M. G., Nandy, A., Duan, C., & Kulik, H. J. (2023). Assessing the Performance of Approximate Density Functional Theory on 95 Experimentally Characterized Fe(II) Spin Crossover Complexes. J. Chem. Phys., 159, 024120.
  8. Adamji, H., Nandy, A., Kevlishvili, I., Roman-Leshkov, Y., & Kulik, H. J. (2023). Computationally Guided Discovery of Stable Metal-Organic Frameworks that are Promising Methane to Methanol Catalysts. J. Am. Chem. Soc., 145, 14365–14378.
  9. Nandy, A., Taylor, M. G., & Kulik, H. J. (2023). Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes. J. Phys. Chem. Lett, 14, 5798–5804.
  10. Cytter, Y., Nandy, A., Duan, C., & Kulik, H. J. (2023). Insights into the Deviation from Piecewise Linearity in Transition Metal Complexes from Supervised Machine Learning Models. Phys. Chem. Chem. Phys., 25, 8103–8116.
  11. Nandy, A., Yue, S., Oh, C., Duan, C., Terrones, G., Chung, Y. G., & Kulik, H. J. (2023). A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models. Matter, 6, 1–19.
  12. Yue, S., Oh, C., Nandy, A., Terrones, G., & Kulik, H. J. (2023). Effects of MOF Linker Rotation and Functionalization on Methane Uptake and Diffusion. Mol. Sys. Des. Eng., 8, 527–537.
  13. Terrones, G., Duan, C., Nandy, A., & Kulik, H. J. (2023). Low-Cost Machine Learning Prediction of Excited State Properties of Iridium-Centered Phosphors. Chem. Sci., 14, 1419–1433.
  14. Kastner, D. W., Nandy, A., Mehmood, R., & Kulik, H. J. (2023). Mechanistic Insights Into Substrate Positioning Across Non-heme Fe(II)/alpha-ketoglutarate-dependent Halogenases and Hydroxylases. ACS Catal., 13, 2489–2501.
  15. Duan, C., Nandy, A., Terrones, G., Kastner, D. W., & Kulik, H. J. (2023). Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores. JACS Au, 3, 391–401.
  16. Cho, Y., Nandy, A., Duan, C., & Kulik, H. J. (2023). DFT-Based Multireference Diagnostics in the Solid State: Application to Metal–Organic Frameworks. J. Chem. Theory Comput., 19, 190–197.
  17. Duan, C., Nandy, A., Meyer, R., Arunachalam, N., & Kulik, H. J. (2023). A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery. Nat. Comput. Sci., 3, 38–47.
  18. Arunachalam, N., Gugler, S., Taylor, M. G., Duan, C., Nandy, A., Janet, J. P., Meyer, R., Oldenstaedt, J., Chu, D. B. K., & Kulik, H. J. (2022). Ligand Additivity Relationships Enable Efficient Exploration of Transition Metal Chemical Space. J. Chem. Phys., 157, 184112.
  19. Nandy, A., Adamji, H., Kastner, D. W., Vennelakanti, V., Nazemi, A., Liu, M., & Kulik, H. J. (2022). Using Computational Chemistry to Reveal Nature’s Blueprints in Single-Site Catalyst C–H Activation. ACS Catal., 12(15), 9281–9306.
  20. Duan, C., Nandy, A., Adamji, H., & Kulik, H. J. (2022). Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis. J. Chem. Theory Comput., 18(7), 4282–4292.
  21. Nandy, A., Duan, C., Goffinet, C., & Kulik, H. J. (2022). New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts. JACS Au, 2(5), 1200–1213.
  22. Cytter, Y., Nandy, A., Bajaj, A., & Kulik, H. J. (2022). Divergent Ligand Additivity Effects in Two Types of Delocalization Errors From Approximate Density Functional Theory. J. Phys. Chem. Lett., 13(20), 4549–4555.
  23. Duan, C., Chu, D. B. K., Nandy, A., & Kulik, H. J. (2022). Detection of Multi-Reference Character Imbalances Enables a Transfer Learning Approach for Virtual High Throughput Screening with Coupled Cluster Accuracy at DFT Cost. Chem. Sci., 13, 4962–4971.
  24. Bajaj, A., Duan, C., Nandy, A., Taylor, M. G., & Kulik, H. J. (2022). Molecular Orbital Projectors in Non-empirical jmDFT Recover Exact Conditions in Transition Metal Chemistry. J. Chem. Phys., 156, 184112.
  25. Nandy, A., Terrones, G., Arunachalam, N., Duan, C., Kastner, D. W., & Kulik, H. J. (2022). MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks. Sci. Data., 9, 74.
  26. Duan, C., Nandy, A., & Kulik, H. J. (2022). Machine Learning for the Discovery and Design of Materials. Ann. Rev. Chem. Eng., 13, 405–429.
  27. Harper, D., Nandy, A., Arunachalam, N., Duan, C., Janet, J. P., & Kulik, H. J. (2022). Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery. J. Chem. Phys., 156, 074101.
  28. Jia, H., Nandy, A., Liu, M., & Kulik, H. J. (2022). Modeling the Roles of Rigidity and Dopants in Single-Atom Methane-to-Methanol Catalysts. J. Mater. Chem. A., 10, 6193–6203.
  29. Liu, M., Nazemi, A., Taylor, M. G., Nandy, A., Duan, C., & Kulik, H. J. (2022). Large-Scale Analysis of the Electronic and Geometric Properties of Bio-Inspired Mo/W Complexes. ACS Catal., 12(2), 383–396.
  30. Nandy, A., Duan, C., & Kulik, H. J. (2022). Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery. Curr. Opin. in Chem. Eng., 36, 100778.
  31. Vennelakanti, V., Nandy, A., & Kulik, H. J. (2022). The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts. Top. Catal., 65, 296–311.
  32. Nandy, A., Duan, C., & Kulik, H. J. (2021). 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.
  33. Taylor, M. G., Nandy, A., Lu, C. C., & Kulik, H. J. (2021). Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning. J. Phys. Chem. Lett., 12(40), 9812–9820.
  34. Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021). Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning. Chem. Rev., 121(16), 9927–10000.
  35. Duan, C., Liu, F., Nandy, A., & Kulik, H. J. (2021). Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery. J. Phys. Chem. Lett., 12(19), 4628–4637.
  36. Janet, J. P., Duan, C., Nandy, A., Liu, F., & Kulik, H. J. (2021). Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design. Acc. Chem. Res., 54(3), 532–545.
  37. Nandy, A., & Kulik, H. J. (2020). Why Conventional Design Rules for C-H Activation Fail for Open Shell Transition Metal Catalysts. ACS Catal., 10(24), 15033–15047.
  38. Moosavi, S. M., Nandy, A., Jablonka, K. M., Ongari, D., Janet, J. P., Boyd, P. G., Lee, Y., Smit, B., & Kulik, H. J. (2020). Understanding Diversity in the Metal-Organic Framework Ecosystem. Nat. Commun., 11, 4068.
  39. Nandy, A., Chu, D. B. K., Harper, D. R., Duan, C., Arunachalam, N., Cytter, Y., & Kulik, H. J. (2020). 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.
  40. Duan, C., Liu, F., Nandy, A., & Kulik, H. J. (2020). Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost. J. Phys. Chem. Lett., 11(16), 6640–6648.
  41. Duan, C., Liu, F., Nandy, A., & Kulik, H. J. (2020). Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics. J. Chem. Theory Comput., 16(7), 4373–4387.
  42. Taylor, M. G., Yang, T., Lin, S., Nandy, A., Janet, J. P., Duan, C., & Kulik, H. J. (2020). Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions. J. Phys. Chem. A, 124(16), 3286–3299.
  43. Nandy, A., Zhu, J., Janet, J. P., Duan, C., Getman, R. B., & Kulik, H. J. (2019). Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation. ACS Catal., 9(9), 8243–8255.
  44. Janet, J. P., Duan, C., Yang, T., Nandy, A., & Kulik, H. J. (2019). A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery. Chem. Sci., 10, 7913–7922.
  45. Duan, C., Janet, J. P., Liu, F., Nandy, A., & Kulik, H. J. (2019). Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models. J. Chem. Theory Comput., 15(4), 2331–2345.
  46. Janet, J. P., Liu, F., Nandy, A., Duan, C., Yang, T., Lin, S., & Kulik, H. J. (2019). Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry. Inorg. Chem., 58(16), 10592–10606.
  47. Nandy, A., Duan, C., Janet, J. P., Gugler, S. O., & Kulik, H. J. (2018). Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry. Ind. Eng. Chem. Res., 57(42), 13973–13986.
  48. Nandy, A., Forse, A. C., Witherspoon, V. J., & Reimer, J. A. (2018). NMR Spectroscopy Reveals Adsorbate Binding Sites in the Metal-Organic Framework UiO-66(Zr). J. Phys. Chem. C, 122(15), 8295–8305.
  49. Khirich, G., Holliday, M. J., Lin, J. C., & Nandy, A. (2018). Measurement and Characterization of Hydrogen-Deuterium Exchange Chemistry Using Relaxation Dispersion NMR Spectroscopy. J. Phys. Chem. B, 122(8), 2368–2378.
  50. Ford, A. C., Chui, W. F., Zeng, A. Y., Nandy, A., Liebenberg, E., Carraro, C., Kazakia, G., Alliston, T., & O’Connell, G. D. (2018). A Modular Approach to Creating Large Engineered Cartilage Surfaces. J. Biomech., 67, 177–183.
  51. Barin, G., Peterson, G. W., Crocellà, V., Xu, J., Colwell, K. A., Nandy, A., Reimer, J. A., Bordiga, S., & Long, J. R. (2017). Highly Effective Ammonia Removal in a Series of Bronsted Acidic Porous Polymers: Investigation of Chemical and Structural Variations. Chem. Sci., 8, 4399–4409.
  52. Bezci, S. E., Nandy, A., & O’Connell, G. D. (2015). Effect of Hydration on Healthy Intervertebral Disk Mechanical Stiffness. J. Biomech. Eng., 137(10), 101007.