Isaac Tamblyn

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	All	Since 2019
Citations	2947	1781
h-index	29	23
i10-index	54	50

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Kevin RyczkoSandboxAQVerified email at sandboxaq.com
Kyle Mills1QBitVerified email at uoit.net
Stephen WhitelamStaff Scientist, Lawrence Berkeley National LabVerified email at lbl.gov
Jeffrey B. NeatonUniversity of California, Berkeley & Lawrence Berkeley National LaboratoryVerified email at berkeley.edu
Jan VorbergerHelmholtz Zentrum Dresden-Rossendorf e.V.Verified email at hzdr.de
Chris BeelerUniversity of OttawaVerified email at uottawa.ca
Corneel CasertLawrence Berkeley National LaboratoryVerified email at lbl.gov
Su Ying QuekAssociate Professor, Department of Physics, National University of SingaporeVerified email at nus.edu.sg
Michael SpannerNational Research Council of CanadaVerified email at nrc.ca
Iryna LuchakUniversity of CalgaryVerified email at ucalgary.ca
Pierre DarancetScientist, Center for Nanoscale Materials, Argonne National LaboratoryVerified email at anl.gov
Rory ColesUniversity of Ontario Institute of TechnologyVerified email at uoit.net
Tom VieijraPhD Student, Research Foundation FlandersVerified email at ugent.be
Peter BetonUniversity of NottinghamVerified email at nottingham.ac.uk
Juan P. GarrahanProfessor of Physics, University of NottinghamVerified email at nottingham.ac.uk
Leeor KronikWeizmann Institute of ScienceVerified email at weizmann.ac.il
Peter DoakScientific Software Engineer, Oak Ridge National LaboratoryVerified email at ornl.gov
jean yves ratyVerified email at scarlet.be
Nir GoldmanLLNL, UC DavisVerified email at llnl.gov
Thomas K. HaxtonLawrence Berkeley National LaboratoryVerified email at lbl.gov

Isaac Tamblyn

Block & University of Ottawa & University of Waterloo

Verified email at uottawa.ca - Homepage

Materials Science Machine learning Deep Learning Physics Chemistry


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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	253	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	244	2007
Deep learning and the Schrödinger equation K Mills, M Spanner, I Tamblyn Physical Review A 96 (4), 042113, 2017	212	2017
Structure and phase boundaries of compressed liquid hydrogen I Tamblyn, SA Bonev Physical Review Letters 104 (6), 65702, 2010	129	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	127	2022
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	127	2010
Deep learning and density-functional theory K Ryczko, DA Strubbe, I Tamblyn Physical Review A 100 (2), 022512, 2019	120	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	96	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	81	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	62	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
Learning to grow: Control of material self-assembly using evolutionary reinforcement learning S Whitelam, I Tamblyn Physical Review E 101 (5), 052604, 2020	57	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	57	2019
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
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	53	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	50	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	43	2020

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