Computational Laboratory for Energy And Nanoscience

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Publications

  • Sebastian J. Wetzel, Roger G. Melko, Isaac Tamblyn, "Twin Neural Network Regression is a Semi-Supervised Regression Algorithm", [(open access link)] submitted (2021)

    Manuscript Summary

  • K. Ryczko, O. Malenfant-Thuot, M. Côté, I. Tamblyn, "Electronic Response Quantities of Solids and Deep Learning", [(open access link)] submitted (2021)

    Manuscript Summary

  • P. Saidi, H. Pirgazi, M. Sanjari, S. Tamimi, M. Mohammadi, L.K. Beland, M.R. Daymond, I. Tamblyn, "Deep Learning and Crystal Plasticity: A Preconditioning Approach for Accurate Orientation Evolution Prediction", [(open access link)] submitted (2021)

    Manuscript Summary

  • K. Ryczko, S.J. Wetzel, R.G. Melko, I. Tamblyn, "Orbital-Free Density Functional Theory with Small Datasets and Deep Learning", [(open access link)] submitted (2021)

    Manuscript Summary

  • P. Abdolghader, A. Ridsdale, T. Grammatikopoulos, F. Légaré, A. Stolow, A.F. Pegoraro, I. Tamblyn, "One-shot Hyperspectral Image Enhancement, Segmentation and De-Noising in Stimulated Raman Microscopy", [(open access link)] submitted (2021)

    Manuscript Summary

  • M. Aldeghi, F. Hase, R.J. Hickman, I. Tamblyn, A. Aspuru-Guzik, "Golem: An algorithm for robust experiment and process optimization", [(open access link)] submitted (2021)

    Manuscript Summary

  • K. Mills, I. Tamblyn, "Weakly-supervised multi-class object localization using only object counts as labels", [(open access link)] submitted (2021)

    Manuscript Summary

  • M. Lytova, M. Spanner, I. Tamblyn, "Deep learning and high harmonic generation", [(open access link)] submitted (2021)

    Manuscript Summary

  • C. Beeler, U. Yahorau, R. Coles, K. Mills, S. Whitelam, and I. Tamblyn, "Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning", submitted [(open access link)] (2021)


  • Manuscript Summary

  • H. Choubisa*, P. Todorovic*, J.M. Pina, D.H. Parmar, O. Voznyy, I. Tamblyn, E. Sargent, "Interpretable discovery of new semiconductors with machine learning", submitted [(open access link)] (2021)



  • Manuscript Summary

  • S. Wetzel, K. Ryczko, R. Melko, I. Tamblyn, "Twin Neural Network Regression", submitted [(open access link)] (2021)



  • Manuscript Summary


  1. S. Whitelam, V. Selin, S.-W. Park, I. Tamblyn, "Correspondence between neuroevolution and gradient descent", accepted Nature Communications, [(open access link)] (2021)


  2. Manuscript Summary

  3. C. Casert, K. Mills, T Vieijra, J Ryckebusch, and I. Tamblyn, "Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning", accepted Phys. Rev. Research[(open access link)] (2021)


  4. Manuscript Summary

  5. C. Casert, T. Vieijra, S. Whitelam, I. Tamblyn, "Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz", Phys. Rev. Lett. 127, 120602, [(PRL/open access link)] (2021)

    Manuscript Summary

  6. S. Whitelam, I. Tamblyn, "Neuroevolutionary learning of particles and protocols for self-assembly", Phys. Rev. Lett. 127, 018003, [(open access link)] (2021)

    Manuscript Summary

  7. C.G. Tetsassi Feugmo, K. Ryczko, A. Anand, C. Veer Singh, and I. Tamblyn, "Neural evolution structure generation: High Entropy Alloys", [(open access link)] accepted (2021)

    Manuscript Summary

  8. C. Bellinger, R. Coles, M. Crowley, I. Tamblyn, "Active Measure Reinforcement Learning for Observation Cost Minimization", submitted [(open access link)] (2021)

    Manuscript Summary

  9. P. Friederich, M. Krenn, I. Tamblyn, A. Aspuru-Guzik, "Scientific intuition inspired by machine learning generated hypotheses", accepted Machine Learning: Science and Technology, [(open access link)] (2021)


    Manuscript Summary

  10. K. Sprague, J. Carrasquilla, S. Whitelam, and I. Tamblyn, "Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principles", Machine Learning: Science and Technology, 2, 2 [MLST (open access link)] (2021)


  11. Manuscript Summary

  12. K. Ryczko, P. Darancet, I. Tamblyn, "Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution", J. Phys. Chem. C, 124, 48, 26117-26123 [J. Phys. Chem. C. (open access link)] (2020)


    Manuscript Summary

  13. K. Mills, C. Casert, I. Tamblyn, "Adversarial generation of mesoscale surface from small scale chemical motifs", J. Phys. Chem. C, 124, 42, 23158-23163, [J. Phys. Chem. C (open access NeurIPS 2019 workshop)] (2020)


  14. Manuscript Summary

  15. K. Mills, P. Ronagh, and I. Tamblyn, "Controlled Online Optimization Learning (COOL): Finding the ground state of spin Hamiltonians with reinforcement learning", Nature Machine Intelligence, 2, 509-517 [(open access link)] (2020), Cover Article


  16. == News coverage ==

  17. N. A. Rice, W. J. Bodnaryk, I. Tamblyn, Z. J. Jakubek, J. Lefebvre, G. Lopinski, A. Adronov, and C. M. Homenick, "Noncovalent Functionalization of Boron Nitride Nanotubes Using Poly(2,7-carbazole)s", J. Poly. Sci, 58, 13, [JPS] (2020)

    Manuscript Summary

  18. S. Whitelam, D. Jacobson, and I. Tamblyn, "Evolutionary reinforcement learning of dynamical large deviations, J. Chem. Phys. 153, 044113 [JCP (open access link)] (2020)

    Manuscript Summary

  19. Hitarth Choubisa, M. Askerka, K. Ryczko, O. Voznyy, K. Mills, I. Tamblyn, and E.H. Sargent, "Crystal Site Feature Embedding Enables Exploration of Large Chemical Spaces", Matter 3, 1 [Matter], (2020)


  20. Manuscript Summary

  21. S. Whitelam, I. Tamblyn, "Learning to grow: control of materials self-assembly using evolutionary reinforcement learning", Phys. Rev. E, 101, 052604 [PRE(open access link)] (2020)


  22. Manuscript Summary

  23. C. Bellinger, R. Coles, M. Crowley I. Tamblyn, "Reinforcement Learning in a Physics-Inspired Semi-Markov Environment", accepted, CanadianAI [(open access link)] (2020)

  24. Manuscript Summary


  25. K. Ryczko, D. Strubbe, and I. Tamblyn, "Deep learning and density functional theory", Phys. Rev. A 100, 022512 [PRA (open access link)] (2019)



  26. Manuscript Summary


  27. K. Mills, I. Luchak, K. Ryczko, A. Domurad, C. Beeler, and I. Tamblyn, "Extensive deep neural networks for transferring small scale learning to large scale systems", Chemical Science, 10, 15, 4119-4354, [Chemical Science (open access link)] (2019), Cover Article

  28. Code examples here

    Manuscript Summary

  29. M. E. C. Pascuzzi, E. Selinger A. Sacco, M. Castellino, P. Rivolo, S. Henrandez, G. Lopinski, I. Tamblyn, R. Nasi, S. Esposito, M. Manzoli, B. Bonelli, and M. Armandia, "Beneficial effect of iron addition on the catalytic activity of electrodeposited MnOx films in the water oxidation reaction", Electrochimica Acta 284, 294-302, [EA] (2018)


  30. Manuscript Summary

  31. K. Ryczko, K. Mills, I. Luchak, C. Homenick, and I. Tamblyn, "Convolutional neural networks for atomistic systems", accepted, Computational Materials Science, [Comp. Mat. Sci. (open access link)] (2018)


  32. Manuscript Summary

  33. K. Mills and I. Tamblyn, "Deep neural networks for learning operators through observation: the case of the 2d spin models", Phys. Rev. E 97, 032119, [PRE (open access link)] (2018)


  34. Manuscript Summary

  35. K. Mills, M. Spanner, and I. Tamblyn, "Deep learning and the Schrodinger equation", Phys. Rev. A 96, 042113, [PRA (open access link)], Editor's Suggestion, (2017)


  36. Manuscript Summary

  37. I. Tamblyn, "The electronic structure of nanoscale interfaces", Molecular Simulation, 43, 10-11, [Mol. Sim.] (2017)
  38. Y. Chen, I. Tamblyn, and S.Y. Quek, "Energy Level Alignment at Hybridized Organic-Metal Interfaces: The Role of Many-Electron Effects", accepted, J. Phys. Chem. C., [JPC] (2017)

  39. N. Portman & I. Tamblyn "Sampling algorithms for validation of supervised learning models for Ising-like systems", Journal of Computational Physics, 350, 871-890, [JCP (open access link)] (2017)


  40. Manuscript Summary

  41. K. Ryczko & I. Tamblyn "Structural characterizations of water-metal interfaces", Phys. Rev. B 96, 064104, [PRB (open access link)] (2017)


  42. Manuscript Summary

  43. K. Ryczko, A. Domurad, N. Buhagiar, and I. Tamblyn, "hashkat: Large-scale simulations of online social networks", Soc. Netw. Anal. Min. 7:4, [SNA (open access link)] (2017)


  44. Manuscript Summary

  45. G. Gupta, M. Radhakrishna, I. Tamblyn, D. QH Tran, M. Besemann, A. Thonnagith, M.F. Elgueta, M.E. Robitaille, R.J. Finlayson, "A randomized comparison between neurostimulation- and ultrasound-guided lateral femoral cutaneous nerve block", accepted Army Medical Dept. J., (2016)
  46. S Whitelam, I. Tamblyn, J.P. Garrahan, and P.H. Beton, "Emergent rhombus tilings from molecular interactions with M-fold rotational symmetry", Phys. Rev. Lett., 114, 115702 [PRL (open access link)] (2015) Cover article

  47. S. Choing, A. J. Francis, G. Clendenning*, M. Schuurman, Roger D. Sommer, I. Tamblyn, W.W. Weare, and T. Cuk, "Long-Lived LMCT in a d0 Vanadium(V) Complex by Internal Conversion to a State of 3dxy Character", J. Phys. Chem. C, 2015, 119 (30), 17029-17038, (2015) Cover article



  48. Manuscript Summary

  49. I. Tamblyn, S. Refaely-Abramson, J.B. Neaton, and L. Kronik, "Simultaneous determination of structures, vibrations, and frontier orbital energies from a self-consistent range-separated hybrid functional", J. Phys. Chem. Lett., 5, 2734, [JPCL] (2014)

  50. S.G. Srinivasan, N. Goldman, I. Tamblyn, S. Hamel, and M. Gaus, "A Density Functional Tight Binding Model with an Extended Basis Set and Three-Body Repulsion for Hydrogen under Extreme Thermodynamic Conditions", J. Phys. Chem. A, 118, 5520-5528 [JPCA] (2014)

  51. S. Whitelam, I. Tamblyn, T.K. Haxton, M.B. Wieland, N.R. Champness, J.P. Garrahan, and P.H. Beton, "Common physical framework explains phase behavior and dynamics of atomic, molecular and polymeric network-formers", Phys. Rev. X 4, 011044, [PRX (open access link)] (2014)

  52. N. Goldman, I. Tamblyn, "Prebiotic chemistry within a simple impacting icy mixture", Journal of Physical Chemistry A, 117 (24), 5124-5131, [JPCA], (2013), Cover article


  53. == News coverage ==

  54. T.K. Haxton, H. Zho, I. Tamblyn, D. Eom, Z. Hu, J.B. Neaton, T.F. Heinz, and S. Whitelam, "Competing thermodynamic and dynamic factors select molecular assemblies on a gold surface", Phys. Rev. Lett., 111, 265701 [PRL (open access link)] (2013)

  55. M. Yu, P. Doak, I. Tamblyn, and J.B. Neaton, "Theoretical design of redox levels of thiophene on functionalized light-absorbing semiconductor surfaces", J. Phys. Chem. Lett., 4, 1701-1706, [JPCL], (2013)

  56. S. Sharifzadeh, I. Tamblyn, P. Doak, P. Darancet, and J.B. Neaton, "Quantitative Molecular Orbital Energies within a G0W0 Approximation", European Physical Journal B, [EPJB (open access link)], (2012)

  57. G. Li, I. Tamblyn, V. Cooper and J.B. Neaton, "Molecular Adsorption on Metal Surfaces with a van der Waals Density Functional", Phys. Rev. B 85, 121409(R), [PRB (open access link)], (2012)

  58. S. Whitelam, I. Tamblyn, P.H. Beton and J.P. Garrahan, "Random and ordered phases of off-lattice rhombus tiles", Physical Review Letters, 108, 035702, [PRL (open access link)], (2012)

  59. I. Tamblyn, P. Darancet, S.Y. Quek, S.A. Bonev, and J.B. Neaton, "Electronic energy level alignment at metal-molecule interfaces with a GW approach", Phys. Rev. B 84, 201402(R), [PRB (open access link)], (2011)

  60. A. Biller, I. Tamblyn, J.B. Neaton, and L. Kronik, "Electronic level alignment at a metal-molecule interface from a short-range hybrid functional", J. Chem. Phys. 135, 164706, [JCP] (2011)

  61. M.A. Morales, L.X. Benedict, D.S. Clark, E. Schwegler, I. Tamblyn, S.A. Bonev, A.A. Correa, S. W. Haan, "Ab initio equation of state of hydrogen for inertial fusion applications", High Energy Density Physics 8, 1, (2011)

  62. I. Tamblyn and S.A. Bonev "Structure and phase boundaries of compressed liquid hydrogen", Physical Review Letters, 104, 065702, [PRL (open access link)] (2010). PRL Editor's Suggestion; featured in Physics

  63. I. Tamblyn and S.A. Bonev "A note on the metallization of compressed liquid hydrogen", Journal of Chemical Physics, 132, 134503, [JCP (open access link)] (2010)

  64. M. Dell'Angela, G. Kladnik, A. Cossaro, A. Verdini, M. Kamenetska, I. Tamblyn, S.Y. Quek, J.B. Neaton, D. Cvetko, A. Morgante, L. Venkataraman "Relating Energy Level Alignment and Amine-Linked Molecular Junction Conductance", Nano Lett., 10 (7), pp 2470-2474, (2010)

  65. I. Tamblyn, J.-Y. Raty, S.A. Bonev "Tetrahedral clustering in molten lithium under pressure", Physical Review Letters, 101, 075703 (2008). Cover article

  66. B. Militzer, W.B. Hubbard, J. Vorberger, I. Tamblyn, and S.A. Bonev "Massive core in Jupiter predicted from first-principles simulations", Astrophysical Journal Letters, 688: L45-L48 (2008)


  67. I. Tamblyn and S.A. Bonev "Exploring the high pressure phase diagrams of light elements using large scale ab-initio molecular dynamics simulations", HPCS, pp. 154-160, 22nd International Symposium on High Performance Computing Systems and Applications (2008)
  68. J. Vorberger, I. Tamblyn, B. Militzer, S.A. Bonev "Hydrogen-Helium Mixtures in the Interiors of Giant Planets", Phys. Rev. B, 75, 024206 (2007)

  69. I. Tamblyn, J. Vorberger, B. Militzer, S.A. Bonev, "Inside the Jovian atmosphere: Hydrogen and Helium at extreme conditions", Physics in Canada, 63, 3, 133, Cover article (2007)
  70. J. Vorberger, I. Tamblyn, S.A. Bonev, B. Militzer "Properties of Dense Fluid Hydrogen and Helium in Giant Gas Planets" Contrib. Plasma Phys. 47, 4-5, 375 (2007)


  71. J. Garcia Sucerquia, W. Xu, S.K. Jericho, M.H. Jericho, I. Tamblyn, H.J. Kreuzer "Digital in line holography: 4-D imaging and tracking of microstructures and organisms in microfluidics and biology" ICO20: Biomedical Optics, Proc. SPIE 6026, 267-275, (2006, undergraduate work)
  72. I. Tamblyn and B. Paton "Sands of Time", Canadian Undergraduate Physics Journal, 4:13-16 (2005, undergraduate work)

    Unfinised tales

  • Electronic Structure of Liquid Water and a Platinum Surface, (open access link)
  • Phase space sampling and operator confidence with generative adversarial networks [(open access link)] (2019)
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