Nandy, Aditya

Understanding the diversity of the metal-organic framework ecosystem

Moosavi, S. M., Nandy, A., Jablonka, K. M., Ongari, D., Janet, J. P., Boyd, P. G., Lee, Y., Smit, B., & Kulik, H. J. (2020) · Nature Communications

Metal-Organic Frameworks: Synthesis and ApplicationsMachine Learning in Materials Science

Cited by 622

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 358

A quantitative uncertainty metric controls error in neural network-driven chemical discovery

Janet, J. P., Duan, C., Yang, T., Nandy, A., & Kulik, H. J. (2019) · Chemical Science

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 243

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks

Nandy, A., Duan, C., & Kulik, H. J. (2021) · Journal of the American Chemical Society

Metal-Organic Frameworks: Synthesis and ApplicationsMachine Learning in Materials Science

Cited by 200

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Nandy, A., Duan, C., Janet, J. P., Gugler, S., & Kulik, H. J. (2018) · Industrial & Engineering Chemistry Research

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 161

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

Janet, J. P., Liu, F., Nandy, A., Duan, C., Yang, T., Lin, S., & Kulik, H. J. (2019) · Inorganic Chemistry

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 114

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

Machine Learning in Materials ScienceCatalytic Processes in Materials Science

Cited by 91

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

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 87

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

Magnetism in coordination complexesMachine Learning in Materials Science

Cited by 81

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

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 69

Evaluating Large Language Models in Scientific Discovery

Song, Z., Lu, J., Du, Y., Yu, B., Pruyn, T. M., Huang, Y., Guo, K., Luo, X., Qu, Y., Qu, Y., et al. (2025) · arXiv

Topic ModelingArtificial Intelligence Applications

Cited by 3