Zachary W. Ulissi

2015 Chemical Engineering Massachusetts Institute of Technology, Cambridge, MA, United States 
 2015-2017 CHEMICAL ENGINEERING Stanford University, Palo Alto, CA 
 2017- Chemical Engineering Carnegie Mellon University, Pittsburgh, PA 
"Zachary Ulissi"


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Richard D. Braatz grad student 2015 MIT (MathTree)
 (Modeling and Simulation of Stochastic Phenomena in Carbon Nanotube-Based Single Molecule Sensors)
Michael  S. Strano grad student 2015 MIT (Chemistry Tree)
Jens K. Nørskov post-doc 2015-2017 Stanford (Physics Tree)
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Back S, Na J, Ulissi ZW. (2021) Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H2O2 Synthesis through Active Motif Screening Acs Catalysis. 11: 2483-2491
Back S, Na J, Tran K, et al. (2020) discovery of active, stable, CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation. Physical Chemistry Chemical Physics : Pccp
Back S, Tran K, Ulissi ZW. (2020) Discovery of Acid-Stable Oxygen Evolution Catalysts: High-Throughput Computational Screening of Equimolar Bimetallic Oxides. Acs Applied Materials & Interfaces
Zhong M, Tran K, Min Y, et al. (2020) Accelerated discovery of CO electrocatalysts using active machine learning. Nature. 581: 178-183
Gu GH, Noh J, Kim S, et al. (2020) Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening. The Journal of Physical Chemistry Letters
Lopato EM, Eikey EA, Simon ZC, et al. (2020) Parallelized Screening of Characterized and DFT-Modeled Bimetallic Colloidal Cocatalysts for Photocatalytic Hydrogen Evolution Acs Catalysis. 10: 4244-4252
Yoon J, Ulissi ZW. (2019) Capturing Structural Transitions in Surfactant Adsorption Isotherms at Solid/Solution Interfaces. Langmuir : the Acs Journal of Surfaces and Colloids
Palizhati A, Zhong W, Tran K, et al. (2019) Towards Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks. Journal of Chemical Information and Modeling
Back S, Yoon J, Tian N, et al. (2019) Convolutional Neural Network of Atomic Surface structures to Predict Binding Energies For High-throughput Screening of Catalysts. The Journal of Physical Chemistry Letters
Back S, Tran K, Ulissi ZW. (2019) Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning Acs Catalysis. 9: 7651-7659
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