| 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 | 546 | 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 | 433 | 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 | 395 | 2017 |
| 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 | 303 | 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 | 266 | 2013 |
| 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 | 152 | 2021 |
| 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 | 151 | 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 | 120 | 2019 |
| 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 | 96 | 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 | 91 | 2020 |
| 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 | 78 | 2016 |
| 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 | 77 | 2014 |
| 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 | 77* | 2012 |
| Effect of multiscale model uncertainty on identification of optimal catalyst properties Z Ulissi, V Prasad, DG Vlachos Journal of catalysis 281 (2), 339-344, 2011 | 52 | 2011 |
| 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 | 51 | 2020 |
| Quantitative theory of adsorptive separation for the electronic sorting of single-walled carbon nanotubes RM Jain, K Tvrdy, R Han, Z Ulissi, MS Strano ACS nano 8 (4), 3367-3379, 2014 | 47 | 2014 |
| Low dimensional carbon materials for applications in mass and energy transport QH Wang, DO Bellisario, LW Drahushuk, RM Jain, S Kruss, MP Landry, ... Chemistry of Materials 26 (1), 172-183, 2014 | 47 | 2014 |
| 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 | 46 | 2012 |
| A mathematical formulation and solution of the CoPhMoRe inverse problem for helically wrapping polymer corona phases on cylindrical substrates G Bisker, J Ahn, S Kruss, ZW Ulissi, DP Salem, MS Strano The Journal of Physical Chemistry C 119 (24), 13876-13886, 2015 | 42 | 2015 |
| 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 | 40 | 2019 |
Darin BellisarioMassachusetts Institute of Technology在 alumni.tufts.edu 的电子邮件经过验证
