Janet, Jon Paul

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

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 ScienceAdvanced battery technologies research

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

Link-INVENT: Generative Linker Design with Reinforcement Learning

Guo, J., Knuth, F., Margreitter, C., Janet, J. P., Papadopoulos, K., Engkvist, O., & Patronov, A. (2022) · Digital Discovery

Computational Drug Discovery MethodsProtein Degradation and Inhibitors

Cited by 101

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

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

Democratising real-world drug discovery through agentic AI

He, J., Lai, H., Saigiridharan, L., Ghiandoni, G. M., Jenei, K., Gokalp, U., Nuković, A., Engkvist, O., Janet, J. P., & Genheden, S. (2026) · Drug Discovery Today

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 17