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Isaac Tamblyn
Isaac Tamblyn
Block & University of Ottawa & University of Waterloo
Verified email at uottawa.ca - Homepage
Title
Cited by
Cited by
Year
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
2712008
Hydrogen-helium mixtures in the interiors of giant planets
J Vorberger, I Tamblyn, B Militzer, SA Bonev
Physical Review B 75 (2), 024206, 2007
2332007
Deep learning and the Schrödinger equation
K Mills, M Spanner, I Tamblyn
Physical Review A 96 (4), 042113, 2017
2012017
Structure and phase boundaries of compressed liquid hydrogen
I Tamblyn, SA Bonev
Physical Review Letters 104 (6), 65702, 2010
1282010
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
1232010
Deep learning and density-functional theory
K Ryczko, DA Strubbe, I Tamblyn
Physical Review A 100 (2), 022512, 2019
1082019
Molecular adsorption on metal surfaces with van der Waals density functionals
G Li, I Tamblyn, VR Cooper, HJ Gao, JB Neaton
Physical Review B 85 (12), 121409, 2012
1062012
Tetrahedral clustering in molten lithium under pressure
I Tamblyn, JY Raty, SA Bonev
Physical Review Letters 101 (7), 075703, 2008
1042008
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
962022
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
942011
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
802011
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
662012
Prebiotic chemistry within a simple impacting icy mixture
N Goldman, I Tamblyn
The Journal of Physical Chemistry A 117 (24), 5124-5131, 2013
582013
Convolutional neural networks for atomistic systems
K Ryczko, K Mills, I Luchak, C Homenick, I Tamblyn
Computational Materials Science 149, 134-142, 2018
572018
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
562014
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
512019
Learning to grow: Control of material self-assembly using evolutionary reinforcement learning
S Whitelam, I Tamblyn
Physical Review E 101 (5), 052604, 2020
502020
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
482014
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
452021
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
412020
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Articles 1–20