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<div id="J78">
<table>
<td>
<li value=78>"High-entropy alloy electrocatalysts screened using machine learning informed by quantum-inspired similarity analysis: computational prediction and experimental synthesis", [(<a href="https://arxiv.org/abs/2108.07614" target="new window">open access link</a>)] (2024)
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<br>
<span class="__dimensions_badge_embed__" data-doi="10.48550/arXiv.2307.02620" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
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<b><h1><a href="publications/078.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/078.png">
</td>
</table>
</div>
<div id="J77">
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<td>
<li value=77>"Efficient determination of Born-effective charges, LO-TO splitting, and Raman tensors of solids with a real-space atom-centered deep learning approach", Olivier Malenfant-Thuot 1, Kevin Ryczko 2 3 4, Isaac Tamblyn 2 3, Michel Côté, [(<a href="http://dx.doi.org/10.1088/1361-648X/ad64a2" target="new window">open access link</a>)] (2024)
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<br>
<span class="__dimensions_badge_embed__" data-doi="10.48550/arXiv.2307.02620" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/077.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/077.png">
</td>
</table>
</div>
<div id="J76">
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<td>
<!-- TODO: this one needs some attention -->
<li value=76>"Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment", [(<a href="https://arxiv.org/abs/2112.14657" target="new window">open access link</a>)] (2023)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.48550/arXiv.2307.02620" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<!--Note: also links to optimus so linked to 073 corresponding to optimus, can change to 075 if needed.-->
<b><h1><a href="publications/076.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/076.png">
</td>
</table>
</div>
<div id="J75">
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<li value =75>C. Bellinger, M. Crowley, <u>I. Tamblyn</u>, "Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning", <i>Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization</i> [<a href="https://doi.org/10.48550/arXiv.2307.02620" target="new window">WANT</a> (<a href="https://doi.org/10.48550/arXiv.2307.02620" target="new window">open access link</a>)], (2023)
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<span class="__dimensions_badge_embed__" data-doi="10.48550/arXiv.2307.02620" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<!--Note: also links to optimus so linked to 073 corresponding to optimus, can change to 075 if needed.-->
<b><h1><a href="publications/073.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/075.png">
</td>
</table>
</div>
<div id="J74">
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<li value=74>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, <u>I. Tamblyn</u>, "ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry", <i>Digital Discovery</i>, Advance Article [<a href="http://dx.doi.org/10.1039/D3DD00183K" target="new window">DD</a> (<a href="http://dx.doi.org/10.1039/D3DD00183K" target="new window">open access link</a>)], (2024)
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<br>
<span class="__dimensions_badge_embed__" data-doi="10.1039/D3DD00183K" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/074.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/074.png">
</td>
</table>
</div>
<div id="J73">
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<td>
<li value=73>C. Casert, <u>I. Tamblyn</u>, S. Whitelam, "Learning stochastic dynamics and predicting emergent behavior using transformers", <i>Nature Communications</i>, 15, 1875 [<a href="https://doi.org/10.1038/s41467-024-45629-w" target="new window">NC</a>, (<a href="https://doi.org/10.1038/s41467-024-45629-w" target="new window">open access link</a>)], (2024)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1038/s41467-024-45629-w" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/073.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/optimus.png">
</td>
</table>
</div>
<div id="J72">
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<td>
<li value=72>V Letourneau, C. Bellinger, <u>I. Tamblyn</u>, Maia Fraser, "Time and temporal abstraction in continual learning: tradeoffs, analogies and regret in an active measuring setting", <i>2nd Conference on Lifelong Learning Agents (CoLLAs)</i> [<a href="https://proceedings.mlr.press/v232/letourneau23a/letourneau23a.pdf" target = "new window">CoLLAs</a> (<a href ="https://proceedings.mlr.press/v232/letourneau23a/letourneau23a.pdf" target="new window">open access link</a>)], (2023)</li>
<br>
<b><h1><a href="publications/072.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/072.png">
</td>
</table>
</div>
<div id="J71">
<table>
<td>
<li value=71>Z. Gariepy, Z. Chen, <u>I. Tamblyn</u>, C. Veer Singh, C.G. Tetsassi Feugmo, "Automatic graph representation algorithm for heterogeneous catalysis", <i>APL Machine Learning</i>, 1, 3, 036103 [<a href="https://doi.org/10.1063/5.0140487" target ="new window">APL</a> (<a href="https://doi.org/10.1063/5.0140487" target="new window">open access link</a>)] (2023)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1063/5.0140487" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/071.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/071.png">
</td>
</table>
</div>
<div id="J70">
<table>
<td>
<li value=70>H. Choubisa*, P. Todorovic*, J.M. Pina, D.H. Parmar, O. Voznyy, <u>I. Tamblyn</u>, E. Sargent, "Interpretable discovery of new semiconductors with machine learning", <i>npj Computational Materials</i>, 9, 11 [<a href="https://doi.org/10.1038/s41524-023-01066-9" target="new window">npj</a> (<a href="https://doi.org/10.1038/s41524-023-01066-9" target="new window">open access link</a>)] (2023)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1038/s41524-023-01066-9" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/070.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/DARWIN.png">
</td>
</table>
</div>
<div id="J69">
<table>
<td>
<li value=69> S. Whitelam & <u>I. Tamblyn</u>,, "Cellular automata can classify data by inducing trajectory phase coexistence", <i>Physical Review E</i>, 108, 014126 [<a href="https://doi.org/10.1103/PhysRevE.108.014126"target="new window">PRE</a> (<a href="https://doi.org/10.48550/arXiv.2203.05551" target="new window">open access link</a>)] (2023)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1103/PhysRevE.108.014126" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/069.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/ca_classify.png">
</td>
</table>
</div>
<div id="J68">
<table>
<td>
<li value=68>S. Whitelam, V. Selin, I. Benlolo, C. Casert, <u>I. Tamblyn</u>, "Training neural networks using Metropolis Monte Carlo and an adaptive variant", <i>Machine Learning: Science and Technology</i>, 3, 4, 045026 [<a href="https://doi.org/10.1088/2632-2153/aca6cd" target="new window">MLST</a> (<a href="https://doi.org/10.1088/2632-2153/aca6cd" target="new window">open access link</a>)] (2022)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1088/2632-2153/aca6cd" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/068.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/068.png">
</td>
</table>
</div>
<div id="J67">
<table>
<td>
<!--Needs open access link, could not find open access to article.-->
<li value=67>Z.-W. Chen, Z. Gariepy, L. Chen; X. Yao, A. Anand, S.-J. Liu, C. Feugmo, <u>I. Tamblyn</u>, C. Veer Singh, "Machine learning-driven high entropy alloy catalyst discovery to circumvent the scaling relation for CO2reduction reaction", <i>ACS Catalysis</i>, 12, 24, 14864–14871 [<a href="https://doi.org/10.1021/acscatal.2c03675" target="new window">ACS</a> (<a>open access link</a>)] (2022)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1021/acscatal.2c03675" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/067.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/067.png">
</td>
</table>
</div>
<div id="J66">
<table>
<td>
<li value=66>K. Ryczko, J.T. Krogel, <u>I. Tamblyn</u>, "Machine Learning Diffusion Monte Carlo Energy Densities", <i>Journal of Chemical Theory and Computation</i>, 18, 12, 7695–7701 [<a href="https://doi.org/10.1021/acs.jctc.2c00483" target="new window">ACS</a> (<a href="https://doi.org/10.48550/arXiv.2205.04547" target="new window">open access link</a>)] (2022)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1021/acs.jctc.2c00483" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/066.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/066.png">
</td>
</table>
</div>
<div id="J65">
<table>
<td>
<li value=65>S.J. Wetzel, R.G. Melko, <u>I. Tamblyn</u>, "Twin Neural Network Regression is a Semi-Supervised Regression Algorithm", <i>Machine Learning: Science and Technology</i>, 3, 4, 045007 [<a href="https://doi.org/10.1088/2632-2153/ac9885" target="new window">MLST</a> (<a href="https://doi.org/10.1088/2632-2153/ac9885" target="new window">open access link</a>)] (2022)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1088/2632-2153/ac9885" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/065.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/TNNR2.png">
</td>
</table>
</div>
<div id="J64">
<table>
<td>
<li value=64>S. Wetzel, K. Ryczko, R. Melko, <u>I. Tamblyn</u>, "Twin Neural Network Regression", <i>Applied AI</i>, 3, 4 [<a href="https://doi.org/10.1002/ail2.78" target="new window">AAI</a> (<a href="https://doi.org/10.1002/ail2.78" target="new window">open access link</a>)] (2022)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1002/ail2.78" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/064.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/TNNR.png">
</td>
</table>
</div>
<div id="J63">
<table>
<td>
<li value=63>M. Lytova, M. Spanner, <u>I. Tamblyn</u>, "Deep learning and high harmonic generation", <i>Canadian Journal of Physics</i>, 101, 3 [<a href="https://doi.org/10.1139/cjp-2022-0115" target="new window">CJP</a> (<a href="https://doi.org/10.48550/arXiv.2012.10328" target="new window">open access link</a>)] (2022)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1139/cjp-2022-0115" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/063.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/HHG.png">
</td>
</table>
</div>
<div id="J62">
<table>
<td>
<li value=62>H. Anwar, A. Johnston, S. Mahesh, K. Singh, Z. Wang, D. A. Kuntz, <u>I. Tamblyn</u>, O. Voznyy, G.G. Privé, and E.H. Sargent, "High-Throughput Evaluation of Emission and Structure in Reduced-Dimensional Perovskites", <i>ACS Central Science</i>, 8, 5, 571–580 [<a href="https://doi.org/10.1021/acscentsci.2c00041" target="new window">ACS</a> (<a href="https://doi.org/10.1021/acscentsci.2c00041" target="new window">open access link</a>)] (2022)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1021/acscentsci.2c00041" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/062.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/062.png">
</td>
</table>
</div>
<div id="J61">
<table>
<td>
<!--Note: no conference DOI link?-->
<li value=61>M. S. Ghaemi, K. Grantham, <u>I. Tamblyn</u>, Y. Li, H.K. Ooi†, "Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge", <i>Canadian AI</i> [<a href="https://doi.org/10.48550/arXiv.2204.02474" target="new window">CAI</a> (<a href="https://doi.org/10.48550/arXiv.2204.02474" target="new window">open access link</a>)] (2022)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.48550/arXiv.2204.02474" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/061.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/061.png">
</td>
</table>
</div>
<div id="J60">
<table>
<td>
<!--Same issue as above with not having conference DOI-->
<li value=60>C. Bellinger, A. Drozdyuk, M. Crowley, <u>I. Tamblyn</u>, "Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning", <i>Canadian AI</i> [<a href="https://doi.org/10.48550/arXiv.2112.07535" target="new window">CAI</a> (<a href="https://doi.org/10.48550/arXiv.2112.07535" target="new window">open access link</a>)] (2022)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.48550/arXiv.2112.07535" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/060.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/060.png">
</td>
</table>
</div>
<div id="J59">
<table>
<td>
<li value=59>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 <u>Tamblyn, Isaac</u> and Whitelam, Stephen and Bellinger, Colin and Ghiringhelli, Luca M. "Roadmap on Machine Learning in Electronic Structure", <i>Electronic Structure</i>, 4, 2 [<a href="https://doi.org/10.1088/2516-1075/ac572f" target="new window">ES</a> (<a href="https://doi.org/10.1088/2516-1075/ac572f" target="new window">open access link</a>)] (2022)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1088/2516-1075/ac572f" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/059.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/059.png">
</td>
</table>
</div>
<div id="J58">
<table>
<td>
<li value=58>K. Ryczko, S.J. Wetzel, R.G. Melko, <u>I. Tamblyn</u>, "Orbital-Free Density Functional Theory with Small Datasets and Deep Learning", <i>Journal of Chemical Theory and Computation</i>, 18, 2, 1122–1128 [<a href="https://doi.org/10.1021/acs.jctc.1c00812" target="new window">ACS</a> (<a href="https://doi.org/10.48550/arXiv.2104.05408" target="new window">open access link</a>)] (2022)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1021/acs.jctc.1c00812" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/058.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/OFDFT.png">
</td>
</table>
</div>
<div id="J57">
<table>
<td>
<li value=57>P. Saidi, H. Pirgazi, M. Sanjari, S. Tamimi, M. Mohammadi, L.K. Beland, M.R. Daymond, <u>I. Tamblyn</u>, "Deep Learning and Crystal Plasticity: A Preconditioning Approach for Accurate Orientation Evolution Prediction", <i>Computer Methods in Applied Mechanics and Engineering</i>, 389, 114392 [<a href="https://doi.org/10.1016/j.cma.2021.114392">CMAME</a>(<a href="https://doi.org/10.48550/arXiv.2106.12730" target="new window">open access link</a>)] (2022)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1016/j.cma.2021.114392" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/057.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/plasticity.png">
</td>
</table>
</div>
<div id="J56">
<table>
<td>
<li value=56>C. Beeler, U. Yahorau, R. Coles, K. Mills, S. Whitelam, and <u>I. Tamblyn</u>, "Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning", <i>Physical Review E</i>, 104, 064128 [<a href="https://doi.org/10.1103/PhysRevE.104.064128" target="new window">PRE</a> (<a href="https://doi.org/10.48550/arXiv.1903.08543" target="new window">open access link</a>)] (2021)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1103/PhysRevE.104.064128" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/056.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/CARNOT.png">
</td>
</table>
</div>
<div id="J55">
<table>
<td>
<li value=55>M. Aldeghi, F. Hase, R.J. Hickman, <u>I. Tamblyn</u>, A. Aspuru-Guzik, "Golem: An algorithm for robust experiment and process optimization", <i>Chemical Science</i>, 12, 14792-14807 [<a href="https://doi.org/10.1039/D1SC01545A" target="new window">CS</a> (<a href="https://doi.org/10.1039/D1SC01545A" target="new window">open access link</a>)] (2021)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1039/D1SC01545A" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/055.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/GOLEM.png">
</td>
</table>
</div>
<div id="J54">
<table>
<td>
<li value=54>P. Abdolghader, G. Resch, A. Ridsdale, T. Grammatikopoulos, F. Légaré, A. Stolow, A.F. Pegoraro, <u>I. Tamblyn</u>, "Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep Learning", <i>Optics Express</i>, 29, 21, 34205-34219 [<a href="https://doi.org/10.1364/OE.439662">OE</a> (<a href="https://doi.org/10.1364/OE.439662" target="new window">open access link</a>)] (2021)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1364/OE.439662" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/054.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/SRS.png">
</td>
</table>
</div>
<div id="J53">
<table>
<td>
<li value=53>S. Whitelam, V. Selin, S.-W. Park, <u>I. Tamblyn</u>, "Correspondence between neuroevolution and gradient descent", <i>Nature Communications</i>, 12, 6317 [<a href="https://doi.org/10.1038/s41467-021-26568-2" target ="new window">NC</a> (<a href="https://doi.org/10.1038/s41467-021-26568-2" target="new window">open access link</a>)] (2021)</li>
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1038/s41467-021-26568-2" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/053.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/EQ.png">
</td>
</table>
</div>
<div id="J52">
<table>
<td>
<li value=52>C. Casert, K. Mills, T Vieijra, J Ryckebusch, and <u>I. Tamblyn</u>, "Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning", <i>Physical Review Research</i>, 3, 033267 [<a href="https://doi.org/10.1103/PhysRevResearch.3.033267" target="new window">PRR</a> (<a href="https://doi.org/10.1103/PhysRevResearch.3.033267" target="new window">open access link</a>)] (2021)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1103/PhysRevResearch.3.033267" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/052.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/RUGAN1.png">
</td>
</table>
</div>
<div id="J51">
<table>
<td>
<li value=51>C. Casert, T. Vieijra, S. Whitelam, <u>I. Tamblyn</u>, "Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz", <i>Physical Review Letters</i>, 127, 120602 [<a href="https://doi.org/10.1103/PhysRevLett.127.120602" target="new window">PRL</a>, <a href="https://ml4physicalsciences.github.io/2020/files/NeurIPS_ML4PS_2020_60.pdf" target="new window">NeurIPS</a> (<a href="https://doi.org/10.1103/PhysRevLett.127.120602" target="new window">open access link</a>)] (2021)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1103/PhysRevLett.127.120602" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/051.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/NRNN.png">
</td>
</table>
</div>
<div id="J50">
<table>
<td>
<li value=50>S. Whitelam, <u>I. Tamblyn</u>, "Neuroevolutionary learning of particles and protocols for self-assembly", <i>Physical Review Letters</i>>, 127, 018003 [<a href="https://doi.org/10.1103/PhysRevLett.127.018003" target="new window">PRL</a> (<a href="https://doi.org/10.48550/arXiv.2012.11832" target="new window">open access link</a>)] (2021)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1103/PhysRevLett.127.018003" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/050.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/L2G2.png">
</td>
</table>
</div>
<div id="J49">
<table>
<td>
<li value=49>C.G. Tetsassi Feugmo, K. Ryczko, A. Anand, C. Veer Singh, and <u>I. Tamblyn</u>, "Neural evolution structure generation: High Entropy Alloys", <i>Journal of Chemical Physics</i>, 155, 044102 [<a href="https://doi.org/10.1063/5.0049000" target="new window">JCP</a> (<a href="https://doi.org/10.48550/arXiv.2103.01462" target="new window">open access link</a>)] (2021) <b>Cover Article</b>
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1063/5.0049000" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/049.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/HEA_NES.png">
</td>
</table>
</div>
<div id="J48">
<table>
<td>
<li value=48>C. Bellinger, R. Coles, M. Crowley, <u>I. Tamblyn</u>, "Active Measure Reinforcement Learning for Observation Cost Minimization", <i>Canadian Conference on AI</i>, 37, 2021L10 [<a href="https://doi.org/10.21428/594757db.72846d04" target="new window">CCAI</a> (<a href="https://doi.org/10.21428/594757db.72846d04" target="new window">open access link</a>)] (2021)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.21428/594757db.72846d04" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/048.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/ARML.png">
</td>
</table>
</div>
<div id="J47">
<table>
<td>
<li value=47>P. Friederich, M. Krenn, <u>I. Tamblyn</u>, A. Aspuru-Guzik, "Scientific intuition inspired by machine learning generated hypotheses", <i>Machine Learning: Science and Technology</i>, 2, 2, 025027 [<a href="https://doi.org/10.1088/2632-2153/abda08" target="new window">MLST</a> (<a href="https://doi.org/10.1088/2632-2153/abda08" target="new window">open access link</a>)] (2021)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1088/2632-2153/abda08" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/047.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/047.png">
</td>
</table>
</div>
<div id="J46">
<table>
<td>
<li value=46>K. Sprague, J. Carrasquilla, S. Whitelam, and <u>I. Tamblyn</u>, "Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principles", <i>Machine Learning: Science and Technology</i>, 2, 2, 02LT02 [<a href="http://dx.doi.org/10.1088/2632-2153/abc81b" target="new window">MLST</a> (<a href="http://dx.doi.org/10.1088/2632-2153/abc81b" target="new window">open access link</a>)] (2021)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1088/2632-2153/abc81b" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/046.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/046.png">
</td>
</table>
</div>
<div id="J45">
<table>
<td>
<li value=45>K. Ryczko, P. Darancet, <u>I. Tamblyn</u>, "Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution", <i>Journal of Physical Chemistry C</i>, 124, 48, 26117-26123 [<a href="https://doi.org/10.1021/acs.jpcc.0c06903" target="new window">JPCC</a> (<a href="https://doi.org/10.48550/arXiv.2007.07070" target="new window">open access link</a>)] (2020)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1021/acs.jpcc.0c06903" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/045.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/045.png">
</td>
</table>
</div>
<div id="J44">
<table>
<td>
<li value=44>K. Mills, C. Casert, <u>I. Tamblyn</u>, "Adversarial generation of mesoscale surface from small scale chemical motifs", <i>Journal of Physical Chemistry C</i>>, 124, 42, 23158-23163, [<a href="https://doi.org/10.1021/acs.jpcc.0c06673" target="new window">JPCC</a> (<a href="https://ml4physicalsciences.github.io/2019/files/NeurIPS_ML4PS_2019_120.pdf">open access NeurIPS 2019 workshop</a>)] (2020)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1021/acs.jpcc.0c06673" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/044.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/044.png">
</td>
</table>
</div>
<div id="J43">
<table>
<td>
<li value=43>K. Mills, P. Ronagh, and <u>I. Tamblyn</u>, "Controlled Online Optimization Learning (COOL): Finding the ground state of spin Hamiltonians with reinforcement learning", <i>Nature Machine Intelligence</i>, 2, 509-517 [<a href="https://doi.org/10.1038/s42256-020-0226-x" target="new window">NMI</a> (<a href="https://doi.org/10.48550/arXiv.2003.00011" target="new window">open access link</a>)] (2020), <b>Cover Article</b></li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1038/s42256-020-0226-x" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/043.html">Manuscript Summary</a></h1></b>
<br><b><h1>== News coverage ==</h1></b>
<ul>
<li><a href="https://rdcu.be/b7UN0" target="_blank">Nature News & Views: Solving optimization tasks in condensed matter</a></li>
</u>
</td>
<td>
<img height=100px src="publications/images/043.png">
</td>
</table>
</div>
<div id="J42">
<table>
<td>
<!--No open access link-->
<li value=42>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", <i>Journal of Polymer Science</i>, 58, 13, 1889-1902 [<a href=" http://dx.doi.org/10.1002/pol.20200164" target="new window">JPS</a> (<a>open access link</a>)] (2020)
<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1002/pol.20200164" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/042.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/042.png">
</td>
</table>
</div>
<div id="J41">
<table>
<td>
<li value=41>S. Whitelam, D. Jacobson, and <u>I. Tamblyn</u>, "Evolutionary reinforcement learning of dynamical large deviations, <i>Journal of Chemical Physics</i>, 153, 4, 044113 [<a href="http://dx.doi.org/10.1063/5.0015301" target="new window">JCP</a> (<a href="https://doi.org/10.48550/arXiv.1909.00835" target="new window">open access link)</a>] (2020)<br>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1063/5.0015301" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/041.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/041.png">
</td>
</table>
</div>
<div id="J40">
<table>
<td>
<li value=40>Hitarth Choubisa, M. Askerka, K. Ryczko, O. Voznyy, K. Mills, <u>I. Tamblyn</u>, and E.H. Sargent, "Crystal Site Feature Embedding Enables Exploration of Large Chemical Spaces", <i>Matter</i>, 3, 2, 433-448 [<a href="https://doi.org/10.1016/j.matt.2020.04.016" target="new window">Matter</a> (<a href="https://doi.org/10.1016/j.matt.2020.04.016" target="new window">open access link</a>)], (2020)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1016/j.matt.2020.04.016" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/040.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/040.png">
</td>
</table>
</div>
<div id="J39">
<table>
<td>
<li value=39>S. Whitelam, <u>I. Tamblyn</u>, "Learning to grow: control of materials self-assembly using evolutionary reinforcement learning", <i>Physical Review E</i>, 101, 052604 [<a href="https://dx.doi.org/10.1103/PhysRevE.101.052604" target="new window">PRE</a> (<a href="https://doi.org/10.48550/arXiv.1912.08333" target="new window">open access link</a>)] (2020)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1103/PhysRevE.101.052604" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/039.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/039.png">
</td>
</table>
</div>
<div id="J38">
<table>
<td>
<!--Note no conference DOI link-->
<li value=38>C. Bellinger, R. Coles, M. Crowley <u>I. Tamblyn</u>, "Reinforcement Learning in a Physics-Inspired Semi-Markov Environment", <i>CanadianAI</i> [<a href="https://www.caiac.ca/en/conferences/canadianai-2020/session/rl" target ="new window">CAI</a> (<a href="https://arxiv.org/abs/2004.07333" target="new window">open access link</a>)] (2020)</li>
<br>
<b><h1><a href="publications/038.html">Manuscript Summary</a></h1></b>
<br>
</td>
<td>
<img height=100px src="publications/images/038.png">
</td>
</table>
</div>
<div id="J37">
<table>
<td>
<li value=37>K. Ryczko, D. Strubbe, and <u>I. Tamblyn</u>, "Deep learning and density functional theory", <i>Physical Review A</i> 100, 022512 [<a href="https://doi.org/10.1103/PhysRevA.100.022512" target = "new window">PRA</a> (<a href="https://doi.org/10.48550/arXiv.1811.08928" target="new window">open access link</a>)] (2019)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1103/PhysRevA.100.022512" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/037.html">Manuscript Summary</a></h1></b>
<br>
</td>
<td>
<img height=100px src="publications/images/037.png">
</td>
</table>
</div>
<div id="J36">
<table>
<td>
<li value=36>K. Mills, I. Luchak, K. Ryczko, A. Domurad, C. Beeler, and <u>I. Tamblyn</u>, "Extensive deep neural networks for transferring small scale learning to large scale systems", <i>Chemical Science</i>, 10, 15, 4119-4354 [<a href="https://doi.org/10.1039/C8SC04578J" target="new window">CS</a> (<a href="https://doi.org/10.1039/C8SC04578J" target="new window">open access link</a>)] (2019), <b>Cover Article</b></li>
<br>
Code examples <a href="codes.html">here</a>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1039/c8sc04578j" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/036.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/036.png">
</td>
</table>
</div>
<div id="J35">
<table>
<td>
<!--No open access link-->
<li value=35>M. E. C. Pascuzzi, E. Selinger A. Sacco, M. Castellino, P. Rivolo, S. Henrandez, G. Lopinski, <u>I. Tamblyn</u>, 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", <i>Electrochimica Acta</i> 284, 294-302 [<a href="https://doi.org/10.1016/j.electacta.2018.07.148" target="new window">EA</a> (<a>open access link</a>)] (2018)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1016/j.electacta.2018.07.148" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/035.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/035.png">
</td>
</table>
</div>
<div id="J34">
<table>
<td>
<li value=34>K. Ryczko, K. Mills, I. Luchak, C. Homenick, and <u>I. Tamblyn</u>, "Convolutional neural networks for atomistic systems", <i>Computational Materials Science</i>, 149, 134-142 [<a href="https://doi.org/10.1016/j.commatsci.2018.03.005" target="new window">CMS</a> (<a href="https://doi.org/10.48550/arXiv.1706.09496" target="new window">open access link</a>)] (2018)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1016/j.commatsci.2018.03.005" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/034.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/034.png">
</td>
</table>
</div>
<div id="J33">
<table>
<td>
<li value=33>K. Mills and <u>I. Tamblyn</u>, "Deep neural networks for learning operators through observation: the case of the 2d spin models", <i>Physical Review E</i>, 97, 032119 [<a href="https://doi.org/10.1103/PhysRevE.97.032119">PRE</a> (<a href="https://doi.org/10.48550/arXiv.1706.09779" target="new window">open access link</a>)] (2018)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1103/PhysRevE.97.032119" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/033.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/033.png">
</td>
</table>
</div>
<div id="J32">
<table>
<td>
<li value=32>K. Mills, M. Spanner, and <u>I. Tamblyn</u>, "Deep learning and the Schrodinger equation", <i>Physical Review A</i>, 96, 042113 [<a href="https://doi.org/10.1103/PhysRevA.96.042113" target="new window">PRA</a> (<a href="https://doi.org/10.48550/arXiv.1702.01361" target="new window">open access link</a>)] (2017), <b>Editor's Suggestion</b></li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1103/PhysRevA.96.042113" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/032.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/032.png">
</td>
</table>
</div>
<div id="J31">
<table>
<td>
<li value=31><u>I. Tamblyn</u>, "The electronic structure of nanoscale interfaces", <i>Molecular Simulation</i>, 43, 10-11 [<a href="http://dx.doi.org/10.1080/08927022.2017.1313417" target="new window">MS</a> (<a>open access link</a>)] (2017)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1080/08927022.2017.1313417" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/031.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/031.png">
</td>
</table>
</div>
<div id="J30">
<table>
<td>
<li value=30>Y. Chen, <u>I. Tamblyn</u>, and S.Y. Quek, "Energy Level Alignment at Hybridized Organic-Metal Interfaces: The Role of Many-Electron Effects", <i>Journal of Physical Chemistry C</i>, 121, 24, 13125–13134 [<a href="http://dx.doi.org/10.1021/acs.jpcc.7b00715" target="new window">JPC</a> (<a href="https://doi.org/10.48550/arXiv.1706.10024" target="new window">open access link</a>)] (2017)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1021/acs.jpcc.7b00715" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/030.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/030.png">
</td>
</table>
</div>
<div id="J29">
<table>
<td>
<li value=29>N. Portman & <u>I. Tamblyn</u> "Sampling algorithms for validation of supervised learning models for Ising-like systems", <i>Journal of Computational Physics</i>, 350, 871-890 [<a href="https://doi.org/10.1016/j.jcp.2017.06.045" target="new window">JCP</a> (<a href="https://doi.org/10.48550/arXiv.1611.05891" target="new window">open access link</a>)] (2017)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1016/j.jcp.2017.06.045" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/029.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="publications/images/ising_PCA.png">
</td>
</table>
</div>
<div id="J28">
<table>
<td>
<li value=28>K. Ryczko & <u>I. Tamblyn</u> "Structural characterizations of water-metal interfaces", <i>Physical Review B</i>, 96, 064104 [<a href="https://doi.org/10.1103/PhysRevB.96.064104" target="new window">PRB</a> (<a href="https://doi.org/10.48550/arXiv.1611.02307" target="new window">open access link</a>)] (2017)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1103/PhysRevB.96.064104" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/028.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="group/images/itamblyn/structural_characterization.png">
</td>
</table>
</div>
<div id="J27">
<table>
<td>
<li value=27>K. Ryczko, A. Domurad, N. Buhagiar, and <u>I. Tamblyn</u>, "hashkat: Large-scale simulations of online social networks", <i>Social Network Analysis and Mining</i>, 7, 4 [<a href="http://dx.doi.org/10.1007/s13278-017-0424-7" target="new window">SNA</a> (<a href="https://doi.org/10.48550/arXiv.1610.07458" target="new window">open access link</a>)] (2017)</li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1007/s13278-017-0424-7" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/026.html">Manuscript Summary</a></h1></b>
</td>
</td>
<td>
<img height=100px src="group/images/itamblyn/agent_types.png">
</td>
</table>
</div>
<div id="J26">
<table>
<td>
<!--note no DOI links-->
<li value=26>G. Gupta, M. Radhakrishna, <u>I. Tamblyn</u>, 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", <i>US Army Medical Department Journal</i>, 2-17, 33-38 [<a href="https://pubmed.ncbi.nlm.nih.gov/28853117/" target="new window">NLM</a> (<a href="https://nrc-publications.canada.ca/eng/view/ft/?id=a881526e-18f1-4a3d-a2fd-057fb2f364e1" target="new window">open access link</a>)] (2016)</li>
<br>
<b><h1><a href="publications/026.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=100px src="group/images/itamblyn/leg_freeze.png">
</td>
</table>
</div>
<div id="J25">
<table>
<td>
<li value=25>S Whitelam, <u>I. Tamblyn</u>, J.P. Garrahan, and P.H. Beton, "Emergent rhombus tilings from molecular interactions with M-fold rotational symmetry", <i>Physical Review Letters</i>, 114, 115702 [<a href="https://doi.org/10.1103/PhysRevLett.114.115702" target="new window">PRL</a> (<a href="https://doi.org/10.48550/arXiv.1411.3644" target="new window">open access link</a>)] (2015) <a href="http://journals.aps.org/prl/covers/114/11"><b>Cover article</b></a></li>
<br><span class="__dimensions_badge_embed__" data-doi="10.1103/PhysRevLett.114.115702" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/025.html">Manuscript Summary</a></h1></b>
</td>
<td>
<img height=150px src="group/images/itamblyn/emergent_rhombus.png">
</td>
</table>
</div>
<div id="J24">
<table>
<td>
<!--no open access DOI-->
<li value=24>S. Choing, A. J. Francis, G. Clendenning*, M. Schuurman, Roger D. Sommer, <u>I. Tamblyn</u>, W.W. Weare, and T. Cuk, "Long-Lived LMCT in a d0 Vanadium(V) Complex by Internal Conversion to a State of 3dxy Character", <i>Journal of Physical Chemistry C</i>, 2015, 119, 17029-17038 [<a href="https://doi.org/10.1021/acs.jpcc.5b00513" target="new window">JPC</a> (<a href="https://nrc-publications.canada.ca/eng/view/ft/?id=5da554e5-7ac2-43d0-bfc9-07b668c650eb" target="new window">open access link</a>)](2015) <b>Cover article</b></li>
<br>
<span class="__dimensions_badge_embed__" data-doi="10.1021/acs.jpcc.5b00513" data-style="small_rectangle"></span><script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<br>
<b><h1><a href="publications/024.html">Manuscript Summary</a></h1></b>
</td>