| Accelerated discovery of CO2 electrocatalysts using active machine learning M Zhong, K Tran, Y Min, C Wang, Z Wang, CT Dinh, P De Luna, Z Yu, ... Nature 581 (7807), 178-183, 2020 | 1099 | 2020 |
| Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution K Tran, ZW Ulissi Nature Catalysis 1 (9), 696-703, 2018 | 714 | 2018 |
| To address surface reaction network complexity using scaling relations machine learning and DFT calculations ZW Ulissi, AJ Medford, T Bligaard, JK Nørskov Nature communications 8 (1), 14621, 2017 | 552 | 2017 |
| Open catalyst 2020 (OC20) dataset and community challenges L Chanussot, A Das, S Goyal, T Lavril, M Shuaibi, M Riviere, K Tran, ... Acs Catalysis 11 (10), 6059-6072, 2021 | 532 | 2021 |
| Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction ZW Ulissi, MT Tang, J Xiao, X Liu, DA Torelli, M Karamad, K Cummins, ... Acs Catalysis 7 (10), 6600-6608, 2017 | 387 | 2017 |
| Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes J Zhang, MP Landry, PW Barone, JH Kim, S Lin, ZW Ulissi, D Lin, B Mu, ... Nature nanotechnology 8 (12), 959-968, 2013 | 348 | 2013 |
| Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts S Back, J Yoon, N Tian, W Zhong, K Tran, ZW Ulissi The journal of physical chemistry letters 10 (15), 4401-4408, 2019 | 215 | 2019 |
| Methods for comparing uncertainty quantifications for material property predictions K Tran, W Neiswanger, J Yoon, Q Zhang, E Xing, ZW Ulissi Machine Learning: Science and Technology 1 (2), 025006, 2020 | 206 | 2020 |
| Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes W Choi, ZW Ulissi, SFE Shimizu, DO Bellisario, MD Ellison, MS Strano Nature communications 4 (1), 2397, 2013 | 175 | 2013 |
| Toward a design of active oxygen evolution catalysts: insights from automated density functional theory calculations and machine learning S Back, K Tran, ZW Ulissi Acs Catalysis 9 (9), 7651-7659, 2019 | 160 | 2019 |
| The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts R Tran, J Lan, M Shuaibi, BM Wood, S Goyal, A Das, J Heras-Domingo, ... ACS Catalysis 13 (5), 3066-3084, 2023 | 138 | 2023 |
| Spatiotemporal intracellular nitric oxide signaling captured using internalized, near-infrared fluorescent carbon nanotube nanosensors ZW Ulissi, F Sen, X Gong, S Sen, N Iverson, AA Boghossian, LC Godoy, ... Nano letters 14 (8), 4887-4894, 2014 | 106 | 2014 |
| Automated discovery and construction of surface phase diagrams using machine learning ZW Ulissi, AR Singh, C Tsai, JK Nørskov The journal of physical chemistry letters 7 (19), 3931-3935, 2016 | 100 | 2016 |
| Modelling and development of photoelectrochemical reactor for H2 production C Carver, Z Ulissi, CK Ong, S Dennison, GH Kelsall, K Hellgardt International Journal of Hydrogen Energy 37 (3), 2911-2923, 2012 | 97 | 2012 |
| Practical deep-learning representation for fast heterogeneous catalyst screening GH Gu, J Noh, S Kim, S Back, Z Ulissi, Y Jung The journal of physical chemistry letters 11 (9), 3185-3191, 2020 | 93 | 2020 |
| An introduction to electrocatalyst design using machine learning for renewable energy storage CL Zitnick, L Chanussot, A Das, S Goyal, J Heras-Domingo, C Ho, W Hu, ... arXiv preprint arXiv:2010.09435, 2020 | 88 | 2020 |
| Rotation invariant graph neural networks using spin convolutions M Shuaibi, A Kolluru, A Das, A Grover, A Sriram, Z Ulissi, CL Zitnick arXiv preprint arXiv:2106.09575, 2021 | 83 | 2021 |
| GemNet-OC: developing graph neural networks for large and diverse molecular simulation datasets J Gasteiger, M Shuaibi, A Sriram, S Günnemann, Z Ulissi, CL Zitnick, ... arXiv preprint arXiv:2204.02782, 2022 | 65 | 2022 |
| Observation of oscillatory surface reactions of riboflavin, trolox, and singlet oxygen using single carbon nanotube fluorescence spectroscopy F Sen, AA Boghossian, S Sen, ZW Ulissi, J Zhang, MS Strano Acs Nano 6 (12), 10632-10645, 2012 | 65 | 2012 |
| Toward predicting intermetallics surface properties with high-throughput DFT and convolutional neural networks A Palizhati, W Zhong, K Tran, S Back, ZW Ulissi Journal of chemical information and modeling 59 (11), 4742-4749, 2019 | 64 | 2019 |
Steven ShimizuMITVerified email at mit.edu
