Jon Paul Janet
Jon Paul Janet
Verified email at - Homepage
Cited by
Cited by
Understanding the diversity of the metal-organic framework ecosystem
SM Moosavi, A Nandy, KM Jablonka, D Ongari, JP Janet, PG Boyd, Y Lee, ...
Nature communications 11 (1), 1-10, 2020
Resolving transition metal chemical space: Feature selection for machine learning and structure–property relationships
JP Janet, HJ Kulik
The Journal of Physical Chemistry A 121 (46), 8939-8954, 2017
Predicting electronic structure properties of transition metal complexes with neural networks
JP Janet, HJ Kulik
Chemical science 8 (7), 5137-5152, 2017
Accelerating chemical discovery with machine learning: simulated evolution of spin crossover complexes with an artificial neural network
JP Janet, L Chan, HJ Kulik
The journal of physical chemistry letters 9 (5), 1064-1071, 2018
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
JP Janet, C Duan, T Yang, A Nandy, HJ Kulik
Chemical science 10 (34), 7913-7922, 2019
Accurate multiobjective design in a space of millions of transition metal complexes with neural-network-driven efficient global optimization
JP Janet, S Ramesh, C Duan, HJ Kulik
ACS central science 6 (4), 513-524, 2020
Strategies and software for machine learning accelerated discovery in transition metal chemistry
A Nandy, C Duan, JP Janet, S Gugler, HJ Kulik
Industrial & Engineering Chemistry Research 57 (42), 13973-13986, 2018
Designing in the face of uncertainty: exploiting electronic structure and machine learning models for discovery in inorganic chemistry
JP Janet, F Liu, A Nandy, C Duan, T Yang, S Lin, HJ Kulik
Inorganic chemistry 58 (16), 10592-10606, 2019
Machine learning accelerates the discovery of design rules and exceptions in stable metal–oxo intermediate formation
A Nandy, J Zhu, JP Janet, C Duan, RB Getman, HJ Kulik
Acs Catalysis 9 (9), 8243-8255, 2019
Learning from failure: predicting electronic structure calculation outcomes with machine learning models
C Duan, JP Janet, F Liu, A Nandy, HJ Kulik
Journal of Chemical Theory and Computation 15 (4), 2331-2345, 2019
Heterogeneous nucleation in CFD simulation of flashing flows in converging–diverging nozzles
JP Janet, Y Liao, D Lucas
International Journal of Multiphase Flow 74, 106-117, 2015
Seeing is believing: Experimental spin states from machine learning model structure predictions
MG Taylor, T Yang, S Lin, A Nandy, JP Janet, C Duan, HJ Kulik
The Journal of Physical Chemistry A 124 (16), 3286-3299, 2020
Machine Learning in Chemistry
JP Janet, HJ Kulik
American Chemical Society, 2020
Leveraging cheminformatics strategies for inorganic discovery: application to redox potential design
JP Janet, TZH Gani, AH Steeves, EI Ioannidis, HJ Kulik
Industrial & Engineering Chemistry Research 56 (17), 4898-4910, 2017
Communication: Recovering the flat-plane condition in electronic structure theory at semi-local DFT cost
A Bajaj, JP Janet, HJ Kulik
The Journal of Chemical Physics 147 (19), 2017
DockStream: a docking wrapper to enhance de novo molecular design
J Guo, JP Janet, MR Bauer, E Nittinger, KA Giblin, K Papadopoulos, ...
Journal of cheminformatics 13, 1-21, 2021
Navigating transition-metal chemical space: artificial intelligence for first-principles design
JP Janet, C Duan, A Nandy, F Liu, HJ Kulik
Accounts of Chemical Research 54 (3), 532-545, 2021
Density functional theory for modelling large molecular adsorbate–surface interactions: a mini-review and worked example
JP Janet, Q Zhao, EI Ioannidis, HJ Kulik
Molecular Simulation 43 (5-6), 327-345, 2017
Enumeration of de novo inorganic complexes for chemical discovery and machine learning
S Gugler, JP Janet, HJ Kulik
Molecular Systems Design & Engineering 5 (1), 139-152, 2020
Graph neural networks with adaptive readouts
D Buterez, JP Janet, SJ Kiddle, D Oglic, P Liņ
Advances in Neural Information Processing Systems 35, 19746-19758, 2022
The system can't perform the operation now. Try again later.
Articles 1–20