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  • C. Bellinger, M. Crowley, I. Tamblyn, "Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning", [(open access link)] submitted (2023)

    Manuscript Summary

  • C. Casert, I. Tamblyn, S. Whitelam, "Learning stochastic dynamics and predicting emergent behavior using transformers", [(open access link)] submitted (2023)

    Manuscript Summary

  • C. Beeler, X. Li, M. Fraser, M. Crowley, I. Tamblyn, "Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment", [(open access link)] submitted (2023)

    Manuscript Summary

  • K. Ryczko, O. Malenfant-Thuot, M. Côté, I. Tamblyn, "Electronic Response Quantities of Solids and Deep Learning", [(open access link)] submitted (2023)

    Manuscript Summary

  1. 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) (2023)

  2. Manuscript Summary

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

  4. Manuscript Summary

  5. 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 [(open access link)] (2023)

  6. Manuscript Summary

  7. S. Whitelam & I. Tamblyn,, "Cellular automata can classify data by inducing trajectory phase coexistence", [(open access link)] accepted Phys. Rev. E (2023)

    Manuscript Summary

  8. S. Whitelam, V. Selin, I. Benlolo, C. Casert, I. Tamblyn, "Training neural networks using Metropolis Monte Carlo and an adaptive variant", [(open access link)] accepted MLST (2022)

    Manuscript Summary

  9. 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, (2022)

    Manuscript Summary

  10. K. Ryczko, J.T. Krogel, I. Tamblyn, "Machine Learning Diffusion Monte Carlo Energy Densities", [(open access link)] accepted Journal of Chemical Theory and Computation (2022)

    Manuscript Summary

  11. S.J. Wetzel, R.G. Melko, I. Tamblyn, "Twin Neural Network Regression is a Semi-Supervised Regression Algorithm", [(open access link)] accepted, MLST (2022)

    Manuscript Summary

  12. S. Wetzel, K. Ryczko, R. Melko, I. Tamblyn, "Twin Neural Network Regression", submitted [(open access link)] accepted, Applied AI (2022)

  13. Manuscript Summary

  14. M. Lytova, M. Spanner, I. Tamblyn, "Deep learning and high harmonic generation", [(open access link)] accepted Can. J. Phys. (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", [(open access link)] ACS Cent. Sci., XXXX, XXX, XXX-XXX (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", [(open access link)] accepted CanadianAI (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", [(open access link)] accepted CanadianAI (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", accepted Electronic Structure, [(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", accepted Journal of Chemical Theory and Computation, [(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", Phys. Rev. E 104, 064128 [PRE (open access link)] (2021)

  22. Manuscript Summary

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

    Manuscript Summary

  24. 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", [(open access link)] acceptedOptics Express (2021)

    Manuscript Summary

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

  26. Manuscript Summary

  27. 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)

  28. Manuscript Summary

  29. 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/NeurIPS Workshop/open access link)] (2021)

    Manuscript Summary

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

    Manuscript Summary

  31. 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) Cover article

    Manuscript Summary

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

    Manuscript Summary

  33. 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

  34. 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)

  35. Manuscript Summary

  36. 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

  37. 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)

  38. Manuscript Summary

  39. 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

  40. == News coverage ==

  41. 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

  42. 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

  43. 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)

  44. Manuscript Summary

  45. 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)

  46. Manuscript Summary

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

  48. Manuscript Summary

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

  50. Manuscript Summary

  51. 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

  52. Code examples here

    Manuscript Summary

  53. 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)

  54. Manuscript Summary

  55. 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)

  56. Manuscript Summary

  57. 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)

  58. Manuscript Summary

  59. 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)

  60. Manuscript Summary

  61. I. Tamblyn, "The electronic structure of nanoscale interfaces", Molecular Simulation, 43, 10-11, [Mol. Sim.] (2017)
  62. 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)

  63. 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)

  64. Manuscript Summary

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

  66. Manuscript Summary

  67. 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)

  68. Manuscript Summary

  69. 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)
  70. 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

  71. 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

  72. Manuscript Summary

  73. 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)

  74. 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)

  75. 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)

  76. 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

  77. == News coverage ==

  78. 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)

  79. 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)

  80. 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)

  81. 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)

  82. 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)

  83. 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)

  84. 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)

  85. 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)

  86. 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

  87. 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)

  88. 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)

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

  90. 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)

  91. 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)
  92. J. Vorberger, I. Tamblyn, B. Militzer, S.A. Bonev "Hydrogen-Helium Mixtures in the Interiors of Giant Planets", Phys. Rev. B, 75, 024206 (2007)

  93. 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)
  94. 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)

  95. 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)
  96. I. Tamblyn and B. Paton "Sands of Time", Canadian Undergraduate Physics Journal, 4:13-16 (2005, undergraduate work)

    Unfinised tales

  • K. Mills, I. Tamblyn, "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)
  • Phase space sampling and operator confidence with generative adversarial networks [(open access link)] (2019)
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