Stebėti
John L.A. Gardner
John L.A. Gardner
Machine Learning for Science, University of Oxford
Patvirtintas el. paštas chem.ox.ac.uk - Pagrindinis puslapis
Pavadinimas
Cituota
Cituota
Metai
How to validate machine-learned interatomic potentials
JD Morrow, JLA Gardner, VL Deringer
The Journal of chemical physics 158 (12), 2023
822023
Synthetic pre-training for neural-network interatomic potentials
JLA Gardner, KT Baker, VL Deringer
Machine Learning: Science and Technology 5 (1), 015003, 2024
142024
Synthetic data enable experiments in atomistic machine learning
JLA Gardner, ZF Beaulieu, VL Deringer
Digital Discovery 2 (3), 651-662, 2023
132023
Data as the next challenge in atomistic machine learning
C Ben Mahmoud, JLA Gardner, VL Deringer
Nature Computational Science, 1-4, 2024
82024
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
42023
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
22020
An automated framework for exploring and learning potential-energy surfaces
Y Liu, JD Morrow, C Ertural, NL Fragapane, JLA Gardner, AA Naik, ...
arXiv preprint arXiv:2412.16736, 2024
12024
Sistema negali atlikti operacijos. Bandykite vėliau dar kartą.
Straipsniai 1–7