Gareth Conduit

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Tom WhiteheadIntellegensVerified email at intellegens.ai
Howard StoneUniversity of CambridgeVerified email at cam.ac.uk
Benjamin D SimonsUniversity of CambridgeVerified email at cam.ac.uk
Benedict W. J. IrwinCHARM TherapeuticsVerified email at charmtx.com
Jin ZhaoNanjing University of Posts and TelecommunicationsVerified email at njupt.edu.cn
Qingyu YanProfessor of Materials Science and Engineering, Nanyang Technological UniversityVerified email at ntu.edu.sg
andrew g. greenUniversity College LondonVerified email at ucl.ac.uk
Pascal BugnionCavendish Laboratory, Cambridge UniversityVerified email at cam.ac.uk
Pablo López RíosMax-Planck Institute for Solid State ReseachVerified email at fkf.mpg.de
Zhi Wei SehInstitute of Materials Research and Engineering, A*STARVerified email at imre.a-star.edu.sg
Man-Fai Ng, AndyInstitute of High Performance ComputingVerified email at ihpc.a-star.edu.sg
Nicholas G JonesUniversity of CambridgeVerified email at cam.ac.uk
Neil DrummondLancaster UniversityVerified email at lancaster.ac.uk
David Fairen-Jimenez | ORCID: 0000-...Dept. of Chemical Engineering and Biotechnology, University of CambridgeVerified email at cam.ac.uk
Bartomeu MonserratDepartment of Materials Science and Metallurgy, Cambridge UniversityVerified email at cam.ac.uk
Peyman Z. MoghadamAssociate Professor, University College LondonVerified email at ucl.ac.uk
Jelle WiemeCenter for Molecular Modeling, Ghent UniversityVerified email at ugent.be
Sven M.J. RoggeAssociate Research Professor, Center for Molecular Modeling, Ghent UniversityVerified email at UGent.be
Diego A. Gomez-GualdronAssociate Professor, Colorado School of MinesVerified email at mines.edu
Aurélia LiPhD, University of CambridgeVerified email at cam.ac.uk

Gareth Conduit

University of Cambridge

Verified email at cam.ac.uk - Homepage

Machine learning Condensed matter physics


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Predicting the state of charge and health of batteries using data-driven machine learning MF Ng, J Zhao, Q Yan, GJ Conduit, ZW Seh Nature Machine Intelligence 2 (3), 161-170, 2020	539	2020
Structure-mechanical stability relations of metal-organic frameworks via machine learning PZ Moghadam, SMJ Rogge, A Li, CM Chow, J Wieme, N Moharrami, ... Matter 1 (1), 219-234, 2019	251	2019
Inhomogeneous phase formation on the border of itinerant ferromagnetism GJ Conduit, AG Green, BD Simons Physical review letters 103 (20), 207201, 2009	231	2009
Design of a nickel-base superalloy using a neural network BD Conduit, NG Jones, HJ Stone, GJ Conduit Materials & Design 131, 358-365, 2017	144	2017
OPTIMADE, an API for exchanging materials data CW Andersen, R Armiento, E Blokhin, GJ Conduit, S Dwaraknath, ... Scientific data 8 (1), 217, 2021	92	2021
Grain growth behaviour during near-γ′ solvus thermal exposures in a polycrystalline nickel-base superalloy DM Collins, BD Conduit, HJ Stone, MC Hardy, GJ Conduit, RJ Mitchell Acta materialia 61 (9), 3378-3391, 2013	86	2013
Superfluidity at the BEC-BCS crossover in two-dimensional Fermi gases with population and mass imbalance GJ Conduit, PH Conlon, BD Simons Physical Review A—Atomic, Molecular, and Optical Physics 77 (5), 053617, 2008	82	2008
Materials data validation and imputation with an artificial neural network PC Verpoort, P MacDonald, GJ Conduit Computational Materials Science 147, 176-185, 2018	79	2018
Imputation of assay bioactivity data using deep learning TM Whitehead, BWJ Irwin, P Hunt, MD Segall, GJ Conduit Journal of chemical information and modeling 59 (3), 1197-1204, 2019	70	2019
Strategies for improving the efficiency of quantum Monte Carlo calculations RM Lee, GJ Conduit, N Nemec, P López Ríos, ND Drummond Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 83 (6 …, 2011	69	2011
Repulsive atomic gas in a harmonic trap on the border of itinerant ferromagnetism GJ Conduit, BD Simons Physical review letters 103 (20), 200403, 2009	64	2009
Itinerant ferromagnetism in an atomic Fermi gas: Influence of population imbalance GJ Conduit, BD Simons Physical Review A—Atomic, Molecular, and Optical Physics 79 (5), 053606, 2009	64	2009
Practical applications of deep learning to impute heterogeneous drug discovery data BWJ Irwin, JR Levell, TM Whitehead, MD Segall, GJ Conduit Journal of Chemical Information and Modeling 60 (6), 2848-2857, 2020	62	2020
Ferromagnetic spin correlations in a few-fermion system PO Bugnion, GJ Conduit Physical Review A—Atomic, Molecular, and Optical Physics 87 (6), 060502, 2013	52	2013
Quantum order-by-disorder in strongly correlated metals AG Green, G Conduit, F Kruger Annual Review of Condensed Matter Physics 9, 59-77, 2018	49	2018
Itinerant ferromagnetism in a two-dimensional atomic gas GJ Conduit Physical Review A—Atomic, Molecular, and Optical Physics 82 (4), 043604, 2010	47	2010
Line of Dirac monopoles embedded in a Bose-Einstein condensate GJ Conduit Physical Review A—Atomic, Molecular, and Optical Physics 86 (2), 021605, 2012	46	2012
Formulation and manufacturing optimization of lithium-ion graphite-based electrodes via machine learning SX Drakopoulos, A Gholamipour-Shirazi, P MacDonald, RC Parini, ... Cell Reports Physical Science 2 (12), 2021	38	2021
Probabilistic design of a molybdenum-base alloy using a neural network BD Conduit, NG Jones, HJ Stone, GJ Conduit Scripta Materialia 146, 82-86, 2018	38	2018
Prediction of in vivo pharmacokinetic parameters and time–exposure curves in rats using machine learning from the chemical structure O Obrezanova, A Martinsson, T Whitehead, S Mahmoud, A Bender, ... Molecular pharmaceutics 19 (5), 1488-1504, 2022	36	2022

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