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Jon Paul Janet

Observed names: Janet J. P., Janet, Jon Paul, Janet, Jon-Paul, Jon Paul Janet (3367922), Jon-Paul Janet
ORCID: 0000-0001-7825-4797
Observed ORCID: 0000-0001-7825-4797
OpenAlex ID: A5030485362
Understanding the diversity of the metal-organic framework ecosystem
2020 · Seyed Mohamad Moosavi, Aditya Nandy, et al. · Nature Communications · 622
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Reinvent 4: Modern AI–driven generative molecule design
2024 · Hannes H. Loeffler, Jiazhen He, et al. · Journal of Cheminformatics · 334
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Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
2017 · Jon Paul Janet, Heather J. Kulik · The Journal of Physical Chemistry A · 289
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A quantitative uncertainty metric controls error in neural network-driven chemical discovery
2019 · Jon Paul Janet, Chenru Duan, et al. · Chemical Science · 245
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Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization
2020 · Jon Paul Janet, Sahasrajit Ramesh, et al. · ACS Central Science · 200
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Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network
2018 · Jon Paul Janet, Lydia Chan, et al. · The Journal of Physical Chemistry Letters · 200
Predicting electronic structure properties of transition metal complexes with neural networks
2017 · Jon Paul Janet, Heather J. Kulik · Chemical Science · 199
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Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
2018 · Aditya Nandy, Chenru Duan, et al. · Industrial & Engineering Chemistry Research · 161
Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting
2024 · David Buterez, Jon Paul Janet, et al. · Nature Communications · 138
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Predicting electronic structure properties of transition metal complexes with neural networks
2017 · Janet, Jon Paul, Kulik, Heather Janine · DSpace@MIT (Massachusetts Institute of Technology) · 134
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Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
2019 · Jon Paul Janet, Fang Liu, et al. · Inorganic Chemistry · 114
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Link-INVENT: generative linker design with reinforcement learning
2023 · Jeff Guo, Franziska Knuth, et al. · Digital Discovery · 101
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Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation
2019 · Aditya Nandy, Jiazhou Zhu, et al. · ACS Catalysis · 91
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Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
2019 · Chenru Duan, Jon Paul Janet, et al. · Journal of Chemical Theory and Computation · 87
Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions
2020 · Michael G. Taylor, Tzuhsiung Yang, et al. · The Journal of Physical Chemistry A · 81
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Machine Learning in Chemistry
2020 · Jon Paul Janet, Heather J. Kulik · ACS in focus · 78
DockStream: a docking wrapper to enhance de novo molecular design
2021 · Jeff Guo, Jon Paul Janet, et al. · Journal of Cheminformatics · 77
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Heterogeneous nucleation in CFD simulation of flashing flows in converging–diverging nozzles
2015 · Jon Paul Janet, Yixiang Liao, et al. · International Journal of Multiphase Flow · 64
Improving de novo molecular design with curriculum learning
2022 · Jeff Guo, Vendy Fialková, et al. · Nature Machine Intelligence · 59
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Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design
2017 · Jon Paul Janet, Terry Z. H. Gani, et al. · Industrial & Engineering Chemistry Research · 56
Sample efficient reinforcement learning with active learning for molecular design
2024 · Michael Dodds, Jeff Guo, et al. · Chemical Science · 55
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Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design
2021 · Jon Paul Janet, Chenru Duan, et al. · Accounts of Chemical Research · 55
Communication: Recovering the flat-plane condition in electronic structure theory at semi-local DFT cost
2017 · Akash Bajaj, Jon Paul Janet, et al. · The Journal of Chemical Physics · 52
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Density functional theory for modelling large molecular adsorbate–surface interactions: a mini-review and worked example
2016 · Jon Paul Janet, Qing Zhao, et al. · Molecular Simulation · 47
Understanding the Diversity of the Metal-Organic Framework Ecosystem
2020 · Seyed Mohamad Moosavi, Aditya Nandy, et al. · ChemRxiv · 45
Works count: 105
Citation count: 4,224
H-index: 32
I10-index: 46
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