Kulik, Heather J.

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

Protection of tissue physicochemical properties using polyfunctional crosslinkers

Park, Y.-G., Sohn, C. H., Chen, R., McCue, M., Yun, D. H., Drummond, G. T., Ku, T., Evans, N. B., Oak, H. C., Trieu, W., et al. (2019) · Nature Biotechnology

Single-cell and spatial transcriptomicsAdvanced Fluorescence Microscopy Techniques

Cited by 421

Critical Knowledge Gaps in Mass Transport through Single-Digit Nanopores: A Review and Perspective

Faucher, S., Aluru, N., Bazant, M. Z., Blankschtein, D., Brozena, A. H., Cumings, J., Pedro de Souza, J., Elimelech, M., Epsztein, R., Fourkas, J. T., et al. (2019) · The Journal of Physical Chemistry C

Nanopore and Nanochannel Transport StudiesMembrane Separation Technologies

Cited by 365

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

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

Janet, J. P., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 289

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

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

Janet, J. P., Chan, L., & Kulik, H. J. (2018) · The Journal of Physical Chemistry Letters

Machine Learning in Materials ScienceMagnetism in coordination complexes

Cited by 200

Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

Janet, J. P., Ramesh, S., Duan, C., & Kulik, H. J. (2020) · ACS Central Science

Machine Learning in Materials ScienceAqueous and Flow Batteries

Cited by 200

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

Predicting electronic structure properties of transition metal complexes with neural networks

Janet, J. P., & Kulik, H. J. (2017) · Chemical Science

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 199

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

Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening

Liu, F., Duan, C., & Kulik, H. J. (2020) · The Journal of Physical Chemistry Letters

Advanced Chemical Physics StudiesMachine Learning in Materials Science

Cited by 58

Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design

Janet, J. P., Gani, T. Z. H., Steeves, A. H., Ioannidis, E. I., & Kulik, H. J. (2017) · Industrial & Engineering Chemistry Research

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 56

Enumeration of de novo inorganic complexes for chemical discovery and machine learning

Gugler, S., Janet, J. P., & Kulik, H. J. (2020) · Molecular Systems Design & Engineering

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 43

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

Duan, C., Chen, S., Taylor, M. G., Liu, F., & Kulik, H. J. (2021) · Chemical Science

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 40

Emergence of a proton exchange-based isomerization and lactonization mechanism in the plant coumarin synthase COSY

Kim, C. Y., Mitchell, A. J., Kastner, D. W., Albright, C. E., Gutierrez, M. A., Glinkerman, C. M., Kulik, H. J., & Weng, J.-K. (2023) · Nature Communications

Plant biochemistry and biosynthesisPlant Gene Expression Analysis

Cited by 25

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