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Computational Laboratory for Energy And Nanoscience

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Publications

  1. "Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment", [(open access link)] (2023)


    Manuscript Summary
  2. C. Bellinger, M. Crowley, I. Tamblyn, "Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning", Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization [WANT (open access link)], (2023)


    Manuscript Summary
  3. C. Beeler, S.G. Subramanian, K. Sprague, N. Chatti, C. Bellinger, M. Shahen, N. Paquin, M. Baula, A. Dawit, Z. Yang, X. Li, M. Crowley, I. Tamblyn, "ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry", Digital Discovery, Advance Article [DD (open access link)], (2024)


    Manuscript Summary
  4. C. Casert, I. Tamblyn, S. Whitelam, "Learning stochastic dynamics and predicting emergent behavior using transformers", Nature Communications, 15, 1875 [NC, (open access link)], (2024)


    Manuscript Summary
  5. V Letourneau, C. Bellinger, I. Tamblyn, Maia Fraser, "Time and temporal abstraction in continual learning: tradeoffs, analogies and regret in an active measuring setting", 2nd Conference on Lifelong Learning Agents (CoLLAs) [CoLLAs (open access link)], (2023)

    6. Manuscript Summary
  6. Z. Gariepy, Z. Chen, I. Tamblyn, C. Veer Singh, C.G. Tetsassi Feugmo, "Automatic graph representation algorithm for heterogeneous catalysis", APL Machine Learning, 1, 3, 036103 [APL (open access link)] (2023)


    7. Manuscript Summary
  7. H. Choubisa*, P. Todorovic*, J.M. Pina, D.H. Parmar, O. Voznyy, I. Tamblyn, E. Sargent, "Interpretable discovery of new semiconductors with machine learning", npj Computational Materials, 9, 11 [npj (open access link)] (2023)


    8. Manuscript Summary
  8. S. Whitelam & I. Tamblyn,, "Cellular automata can classify data by inducing trajectory phase coexistence", Physical Review E, 108, 014126 [PRE (open access link)] (2023)


    Manuscript Summary
  9. S. Whitelam, V. Selin, I. Benlolo, C. Casert, I. Tamblyn, "Training neural networks using Metropolis Monte Carlo and an adaptive variant", Machine Learning: Science and Technology, 3, 4, 045026 [MLST (open access link)] (2022)


    Manuscript Summary
  10. Z.-W. Chen, Z. Gariepy, L. Chen; X. Yao, A. Anand, S.-J. Liu, C. Feugmo, I. Tamblyn, C. Veer Singh, "Machine learning-driven high entropy alloy catalyst discovery to circumvent the scaling relation for CO2reduction reaction", ACS Catalysis, 12, 24, 14864–14871 [ACS (open access link)] (2022)


    Manuscript Summary
  11. K. Ryczko, J.T. Krogel, I. Tamblyn, "Machine Learning Diffusion Monte Carlo Energy Densities", Journal of Chemical Theory and Computation, 18, 12, 7695–7701 [ACS (open access link)] (2022)


    Manuscript Summary
  12. S.J. Wetzel, R.G. Melko, I. Tamblyn, "Twin Neural Network Regression is a Semi-Supervised Regression Algorithm", Machine Learning: Science and Technology, 3, 4, 045007 [MLST (open access link)] (2022)


    Manuscript Summary
  13. S. Wetzel, K. Ryczko, R. Melko, I. Tamblyn, "Twin Neural Network Regression", Applied AI, 3, 4 [AAI (open access link)] (2022)


    14. Manuscript Summary
  14. M. Lytova, M. Spanner, I. Tamblyn, "Deep learning and high harmonic generation", Canadian Journal of Physics, 101, 3 [CJP (open access link)] (2022)


    Manuscript Summary
  15. H. Anwar, A. Johnston, S. Mahesh, K. Singh, Z. Wang, D. A. Kuntz, I. Tamblyn, O. Voznyy, G.G. Privé, and E.H. Sargent, "High-Throughput Evaluation of Emission and Structure in Reduced-Dimensional Perovskites", ACS Central Science, 8, 5, 571–580 [ACS (open access link)] (2022)


    Manuscript Summary
  16. M. S. Ghaemi, K. Grantham, I. Tamblyn, Y. Li, H.K. Ooi†, "Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge", Canadian AI [CAI (open access link)] (2022)


    Manuscript Summary
  17. C. Bellinger, A. Drozdyuk, M. Crowley, I. Tamblyn, "Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning", Canadian AI [CAI (open access link)] (2022)


    Manuscript Summary
  18. Kulik, Heather and Hammerschmidt, Thomas and Schmidt, Jonathan and Botti, Silvana and Marques, Miguel A. L. and Boley, Mario and Scheffler, Matthias and Todorović, Milica and Rinke, Patrick and Oses, Corey and Smolyanyuk, Andriy and Curtarolo, Stefano and Tkatchenko, Alexandre and Bartok, Albert and Manzhos, Sergei and Ihara, Manabu and Carrington, Tucker and Behler, Jörg and Isayev, Olexandr and Veit, Max and Grisafi, Andrea and Nigam, Jigyasa and Ceriotti, Michele and Schütt, Kristoff T and Westermayr, Julia and Gastegger, Michael and Maurer, Reinhard and Kalita, Bhupalee and Burke, Kieron and Nagai, Ryo and Akashi, Ryosuke and Sugino, Osamu and Hermann, Jan and Noé, Frank and Pilati, Sebastiano and Draxl, Claudia and Kuban, Martin and Rigamonti, Santiago and Scheidgen, Markus and Esters, Marco and Hicks, David and Toher, Cormac and Balachandran, Prasanna and Tamblyn, Isaac and Whitelam, Stephen and Bellinger, Colin and Ghiringhelli, Luca M. "Roadmap on Machine Learning in Electronic Structure", Electronic Structure, 4, 2 [ES (open access link)] (2022)


    Manuscript Summary
  19. K. Ryczko, S.J. Wetzel, R.G. Melko, I. Tamblyn, "Orbital-Free Density Functional Theory with Small Datasets and Deep Learning", Journal of Chemical Theory and Computation, 18, 2, 1122–1128 [ACS (open access link)] (2022)


    Manuscript Summary
  20. 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", Computer Methods in Applied Mechanics and Engineering, 389, 114392 [CMAME(open access link)] (2022)


    Manuscript Summary
  21. C. Beeler, U. Yahorau, R. Coles, K. Mills, S. Whitelam, and I. Tamblyn, "Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning", Physical Review E, 104, 064128 [PRE (open access link)] (2021)


    22. Manuscript Summary
  22. M. Aldeghi, F. Hase, R.J. Hickman, I. Tamblyn, A. Aspuru-Guzik, "Golem: An algorithm for robust experiment and process optimization", Chemical Science, 12, 14792-14807 [CS (open access link)] (2021)


    Manuscript Summary
  23. P. Abdolghader, G. Resch, A. Ridsdale, T. Grammatikopoulos, F. Légaré, A. Stolow, A.F. Pegoraro, I. Tamblyn, "Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep Learning", Optics Express, 29, 21, 34205-34219 [OE (open access link)] (2021)


    Manuscript Summary
  24. S. Whitelam, V. Selin, S.-W. Park, I. Tamblyn, "Correspondence between neuroevolution and gradient descent", Nature Communications, 12, 6317 [NC (open access link)] (2021)



    25. Manuscript Summary
  25. C. Casert, K. Mills, T Vieijra, J Ryckebusch, and I. Tamblyn, "Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning", Physical Review Research, 3, 033267 [PRR (open access link)] (2021)


    26. Manuscript Summary
  26. C. Casert, T. Vieijra, S. Whitelam, I. Tamblyn, "Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz", Physical Review Letters, 127, 120602 [PRL, NeurIPS (open access link)] (2021)


    Manuscript Summary
  27. S. Whitelam, I. Tamblyn, "Neuroevolutionary learning of particles and protocols for self-assembly", Physical Review Letters>, 127, 018003 [PRL (open access link)] (2021)


    Manuscript Summary
  28. C.G. Tetsassi Feugmo, K. Ryczko, A. Anand, C. Veer Singh, and I. Tamblyn, "Neural evolution structure generation: High Entropy Alloys", Journal of Chemical Physics, 155, 044102 [JCP (open access link)] (2021) Cover Article


    Manuscript Summary
  29. C. Bellinger, R. Coles, M. Crowley, I. Tamblyn, "Active Measure Reinforcement Learning for Observation Cost Minimization", Canadian Conference on AI, 37, 2021L10 [CCAI (open access link)] (2021)


    Manuscript Summary
  30. P. Friederich, M. Krenn, I. Tamblyn, A. Aspuru-Guzik, "Scientific intuition inspired by machine learning generated hypotheses", Machine Learning: Science and Technology, 2, 2, 025027 [MLST (open access link)] (2021)


    Manuscript Summary
  31. 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, 02LT02 [MLST (open access link)] (2021)


    32. Manuscript Summary
  32. K. Ryczko, P. Darancet, I. Tamblyn, "Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution", Journal of Physical Chemistry C, 124, 48, 26117-26123 [JPCC (open access link)] (2020)


    Manuscript Summary
  33. K. Mills, C. Casert, I. Tamblyn, "Adversarial generation of mesoscale surface from small scale chemical motifs", Journal of Physical Chemistry C>, 124, 42, 23158-23163, [JPCC (open access NeurIPS 2019 workshop)] (2020)


    34. Manuscript Summary
  34. 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 [NMI (open access link)] (2020), Cover Article


    35. Manuscript Summary

    == News coverage ==
  35. 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", Journal of Polymer Science, 58, 13, 1889-1902 [JPS (open access link)] (2020)


    Manuscript Summary
  36. S. Whitelam, D. Jacobson, and I. Tamblyn, "Evolutionary reinforcement learning of dynamical large deviations, Journal of Chemical Physics, 153, 4, 044113 [JCP (open access link)] (2020)


    Manuscript Summary
  37. 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, 2, 433-448 [Matter (open access link)], (2020)


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


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

    40. Manuscript Summary
  40. K. Ryczko, D. Strubbe, and I. Tamblyn, "Deep learning and density functional theory", Physical Review A 100, 022512 [PRA (open access link)] (2019)


    41. Manuscript Summary
  41. 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 [CS (open access link)] (2019), Cover Article

    42. Code examples here

    Manuscript Summary
  42. 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 (open access link)] (2018)


    43. Manuscript Summary
  43. K. Ryczko, K. Mills, I. Luchak, C. Homenick, and I. Tamblyn, "Convolutional neural networks for atomistic systems", Computational Materials Science, 149, 134-142 [CMS (open access link)] (2018)


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


    45. Manuscript Summary
  45. K. Mills, M. Spanner, and I. Tamblyn, "Deep learning and the Schrodinger equation", Physical Review A, 96, 042113 [PRA (open access link)] (2017), Editor's Suggestion


    46. Manuscript Summary
  46. I. Tamblyn, "The electronic structure of nanoscale interfaces", Molecular Simulation, 43, 10-11 [MS (open access link)] (2017)


    47. Manuscript Summary
  47. Y. Chen, I. Tamblyn, and S.Y. Quek, "Energy Level Alignment at Hybridized Organic-Metal Interfaces: The Role of Many-Electron Effects", Journal of Physical Chemistry C, 121, 24, 13125–13134 [JPC (open access link)] (2017)


    48. Manuscript Summary
  48. 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)


    49. Manuscript Summary
  49. K. Ryczko & I. Tamblyn "Structural characterizations of water-metal interfaces", Physical Review B, 96, 064104 [PRB (open access link)] (2017)


    50. Manuscript Summary
  50. K. Ryczko, A. Domurad, N. Buhagiar, and I. Tamblyn, "hashkat: Large-scale simulations of online social networks", Social Network Analysis and Mining, 7, 4 [SNA (open access link)] (2017)


    51. Manuscript Summary
  51. 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", US Army Medical Department Journal, 2-17, 33-38 [NLM (open access link)] (2016)

    52. Manuscript Summary
  52. S Whitelam, I. Tamblyn, J.P. Garrahan, and P.H. Beton, "Emergent rhombus tilings from molecular interactions with M-fold rotational symmetry", Physical Review Letters, 114, 115702 [PRL (open access link)] (2015) Cover article


    53. Manuscript Summary
  53. 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", Journal of Physical Chemistry C, 2015, 119, 17029-17038 [JPC (open access link)](2015) Cover article


    54. Manuscript Summary
  54. 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", Journal of Physical Chemistry Letters, 5, 2734 [JPCL (open access link)] (2014)


    55. Manuscript Summary
  55. 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", Journal of Physical Chemistry A, 118, 5520-5528 [JPCA (open access link)] (2014)


    56. Manuscript Summary
  56. 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", Physical Review X, 4, 011044 [PRX (open access link)] (2014)


    57. Manuscript Summary
  57. N. Goldman, I. Tamblyn, "Prebiotic chemistry within a simple impacting icy mixture", Journal of Physical Chemistry A, 117, 24, 5124-5131 [JPCA (open access link)] (2013), Cover Article


    58. Manuscript Summary

    == News coverage ==
  58. 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", Physical Review Letters, 111, 265701 [PRL (open access link)] (2013)


    59. Manuscript Summary
  59. M. Yu, P. Doak, I. Tamblyn, and J.B. Neaton, "Theoretical design of redox levels of thiophene on functionalized light-absorbing semiconductor surfaces", Journal of Physical Chemistry Letters, 4, 1701-1706 [JPCL (open access link)], (2013)


    60. Manuscript Summary
  60. 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)


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


    Manuscript Summary
  62. 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)


    Manuscript Summary
  63. 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", Physical Review B, 84, 201402(R) [PRB (open access link)] (2011)


    Manuscript Summary
  64. A. Biller, I. Tamblyn, J.B. Neaton, and L. Kronik, "Electronic level alignment at a metal-molecule interface from a short-range hybrid functional", Journal of Chemical Physics 135, 164706 [JCP (open access link)] (2011)


    Manuscript Summary
  65. 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, 5-12 [HEDP (open access link)](2011)


    Manuscript Summary
  66. 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


    Manuscript Summary
  67. 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)


    Manuscript Summary
  68. 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 Letters, 10, 7, 2470-2474 [NL (open access link)](2010)


    Manuscript Summary
  69. I. Tamblyn, J.-Y. Raty, S.A. Bonev "Tetrahedral clustering in molten lithium under pressure", Physical Review Letters, 101, 075703 [PRL (open access link)] (2008), Cover Article


    Manuscript Summary
  70. B. Militzer, W.B. Hubbard, J. Vorberger, I. Tamblyn, and S.A. Bonev "Massive core in Jupiter predicted from first-principles simulations", The Astrophysical Journal, 688, 1, L45-L48 [TAJ (open access link)] (2008)


    Manuscript Summary
  71. I. Tamblyn and S.A. Bonev "Exploring the high pressure phase diagrams of light elements using large scale ab-initio molecular dynamics simulations", 22nd International Symposium on High Performance Computing Systems and Applications, 154-160 [HPCS (open access link)] (2008)


    Manuscript Summary

  72. J. Vorberger, I. Tamblyn, B. Militzer, S.A. Bonev "Hydrogen-Helium Mixtures in the Interiors of Giant Planets", Physical Review B, 75, 024206 [PRB (open access link)] (2007)


    73. Manuscript Summary
  73. 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 [PIC (open access link)]Cover Article (2007)

    Manuscript Summary
  74. J. Vorberger, I. Tamblyn, S.A. Bonev, B. Militzer "Properties of Dense Fluid Hydrogen and Helium in Giant Gas Planets", Contributions to Plasma Physics 47, 4-5, 375 [CPP (open access link)] (2007)


    Manuscript Summary
  75. 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, [ICO20 (open access link)] (2006, undergraduate work)


    Manuscript Summary

  76. I. Tamblyn and B. Paton "Sands of Time", Canadian Undergraduate Physics Journal, 4, 13-16 [CUPJ (open access link)] (2005, undergraduate work)

    Manuscript Summary

    Unfinished Tales

  • "Electronic Response Quantities of Solids and Deep Learning", [(open access link)] (2023)
  • "Weakly-supervised multi-class object localization using only object counts as labels", [(open access link)] (2022)
  • "Electronic Structure of Liquid Water and a Platinum Surface", [(open access link)] (2014)
  • "Phase space sampling and operator confidence with generative adversarial networks", [(open access link)] (2019)
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