| A massive core in Jupiter predicted from first-principles simulations B Militzer, WB Hubbard, J Vorberger, I Tamblyn, SA Bonev The Astrophysical Journal 688 (1), L45, 2008 | 252 | 2008 |
| Hydrogen-helium mixtures in the interiors of giant planets J Vorberger, I Tamblyn, B Militzer, SA Bonev Physical Review B 75 (2), 024206, 2007 | 238 | 2007 |
| Deep learning and the Schrödinger equation K Mills, M Spanner, I Tamblyn Physical Review A 96 (4), 042113, 2017 | 209 | 2017 |
| Structure and phase boundaries of compressed liquid hydrogen I Tamblyn, SA Bonev Physical Review Letters 104 (6), 65702, 2010 | 129 | 2010 |
| Relating Energy Level Alignment and Amine-Linked Single Molecule Junction Conductance M Dell’Angela, G Kladnik, A Cossaro, A Verdini, M Kamenetska, ... Nano Letters, 2010 | 126 | 2010 |
| Roadmap on machine learning in electronic structure HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure 4 (2), 023004, 2022 | 117 | 2022 |
| Deep learning and density-functional theory K Ryczko, DA Strubbe, I Tamblyn Physical Review A 100 (2), 022512, 2019 | 115 | 2019 |
| Molecular adsorption on metal surfaces with van der Waals density functionals G Li, I Tamblyn, VR Cooper, HJ Gao, JB Neaton Physical Review B—Condensed Matter and Materials Physics 85 (12), 121409, 2012 | 108 | 2012 |
| Tetrahedral clustering in molten lithium under pressure I Tamblyn, JY Raty, SA Bonev Physical review letters 101 (7), 075703, 2008 | 106 | 2008 |
| Electronic energy level alignment at metal-molecule interfaces with a approach I Tamblyn, P Darancet, SY Quek, SA Bonev, JB Neaton Physical Review B 84 (20), 201402, 2011 | 95 | 2011 |
| Electronic level alignment at a metal-molecule interface from a short-range hybrid functional A Biller, I Tamblyn, JB Neaton, L Kronik The Journal of Chemical Physics 135, 164706, 2011 | 80 | 2011 |
| Quantitative molecular orbital energies within a G0W0 approximation S Sharifzadeh, I Tamblyn, P Doak, PT Darancet, JB Neaton The European Physical Journal B 85, 1-5, 2012 | 70 | 2012 |
| Convolutional neural networks for atomistic systems K Ryczko, K Mills, I Luchak, C Homenick, I Tamblyn Computational Materials Science 149, 134-142, 2018 | 60 | 2018 |
| Prebiotic chemistry within a simple impacting icy mixture N Goldman, I Tamblyn The Journal of Physical Chemistry A 117 (24), 5124-5131, 2013 | 59 | 2013 |
| Simultaneous determination of structures, vibrations, and frontier orbital energies from a self-consistent range-separated hybrid functional I Tamblyn, S Refaely-Abramson, JB Neaton, L Kronik The Journal of Physical Chemistry Letters 5 (15), 2734-2741, 2014 | 57 | 2014 |
| Learning to grow: Control of material self-assembly using evolutionary reinforcement learning S Whitelam, I Tamblyn Physical Review E 101 (5), 052604, 2020 | 55 | 2020 |
| Extensive deep neural networks for transferring small scale learning to large scale systems K Mills, K Ryczko, I Luchak, A Domurad, C Beeler, I Tamblyn Chemical Science 10 (15), 4129-4140, 2019 | 52 | 2019 |
| Scientific intuition inspired by machine learning-generated hypotheses P Friederich, M Krenn, I Tamblyn, A Aspuru-Guzik Machine Learning: Science and Technology 2 (2), 025027, 2021 | 50 | 2021 |
| Common physical framework explains phase behavior and dynamics of atomic, molecular, and polymeric network formers S Whitelam, I Tamblyn, TK Haxton, MB Wieland, NR Champness, ... Physical Review X 4 (1), 011044, 2014 | 48 | 2014 |
| Crystal site feature embedding enables exploration of large chemical spaces H Choubisa, M Askerka, K Ryczko, O Voznyy, K Mills, I Tamblyn, ... Matter 3 (2), 433-448, 2020 | 42 | 2020 |
Kevin RyczkoSandboxAQ在 sandboxaq.com 的电子邮件经过验证
