John L.A. Gardner

2022202320241 28 27

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Volker L. DeringerUniversity of OxfordVerified email at chem.ox.ac.uk
Zoe Faure BeaulieuPhD Candidate in Computational Discovery, University of OxfordVerified email at stcatz.ox.ac.uk
Joe D. MorrowPhD Student, University of OxfordVerified email at queens.ox.ac.uk
Andrew GoodwinProfessor of Materials Chemistry, University of OxfordVerified email at chem.ox.ac.uk
Thomas NicholasDPhil in Inorganic Chemistry, University of OxfordVerified email at chem.ox.ac.uk
William BarfordUniversity of OxfordVerified email at chem.ox.ac.uk
Jonathan MannouchPostdoctoral researcher at MPI for the Structure and Dynamics of MatterVerified email at mpsd.mpg.de

John L.A. Gardner

Machine Learning for Science, University of Oxford

Verified email at chem.ox.ac.uk - Homepage


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
How to validate machine-learned interatomic potentials JD Morrow, JLA Gardner, VL Deringer The Journal of Chemical Physics 158 (12), 2023	38	2023
Synthetic data enable experiments in atomistic machine learning JLA Gardner, ZF Beaulieu, VL Deringer Digital Discovery 2 (3), 651-662, 2023	8	2023
Synthetic pre-training for neural-network interatomic potentials JLA Gardner, KT Baker, VL Deringer Machine Learning: Science and Technology 5 (1), 015003, 2024	5	2024
Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks ZF Beaulieu, TC Nicholas, JLA Gardner, AL Goodwin, VL Deringer Chemical Communications 59 (76), 11405-11408, 2023	3	2023
Using spectroscopy to probe relaxation, decoherence, and localization of photoexcited states in π-conjugated polymers W Barford, JLA Gardner, JR Mannouch Faraday Discussions 221, 281-298, 2020	2	2020

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