| 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 | 502 | 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 | 237 | 2019 |
| Inhomogeneous phase formation on the border of itinerant ferromagnetism GJ Conduit, AG Green, BD Simons Physical review letters 103 (20), 207201, 2009 | 226 | 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 | 135 | 2017 |
| OPTIMADE, an API for exchanging materials data CW Andersen, R Armiento, E Blokhin, GJ Conduit, S Dwaraknath, ... Scientific data 8 (1), 217, 2021 | 86 | 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 | 85 | 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 | 80 | 2008 |
| Materials data validation and imputation with an artificial neural network PC Verpoort, P MacDonald, GJ Conduit Computational Materials Science 147, 176-185, 2018 | 77 | 2018 |
| 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 |
| 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 | 68 | 2019 |
| 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 | 63 | 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 | 61 | 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 | 48 | 2018 |
| Itinerant ferromagnetism in a two-dimensional atomic gas GJ Conduit Physical Review A—Atomic, Molecular, and Optical Physics 82 (4), 043604, 2010 | 46 | 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 | 44 | 2012 |
| 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 | 37 | 2018 |
| Itinerant ferromagnetism in an interacting Fermi gas with mass imbalance CW von Keyserlingk, GJ Conduit Physical Review A—Atomic, Molecular, and Optical Physics 83 (5), 053625, 2011 | 36 | 2011 |
| Probabilistic neural network identification of an alloy for direct laser deposition BD Conduit, T Illston, S Baker, DV Duggappa, S Harding, HJ Stone, ... Materials & Design 168, 107644, 2019 | 33 | 2019 |
Tom WhiteheadIntellegens在 intellegens.ai 的电子邮件经过验证
