Zachary W. Ulissi
Our research work is focused on study of chemical, mechanical, electronic, and thermal properties of nano sized materials. At nano scales, entropic fluctuations become more prominent and materials behave differently. Our work is on capturing these effects in real devices and applications requires a range of modeling approaches, from hard theory (DFT and kinetics), to soft theory (continuum, statistical mechanics and molecular dynamics), and up through systems engineering approaches. There are various application to study such effect, including biomedical sensors (nanotube-based optical sensors) and energy applications (CO2 to fuels, fuel cells, thermal catalysis). Following are our major projects:
- Controlling selectivity of nanoscale interfaces with co-adsorbates and soft functionalizations
- Machine-learning based approaches to accelerate materials screening
- Bayesian methods for complex reaction mechanism reduction and elucidation
- "Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes." Jingqing Zhang, Markita P Landry, Paul W Barone, Jong-Ho Kim, Shangchao Lin, Zachary W Ulissi, Dahua Lin, Bin Mu, Ardemis A Boghossian, Andrew J Hilmer, Alina Rwei, Allison C Hinckley, Sebastian Kruss, Mia A Shandell, Nitish Nair, Steven Blake, Fatih Şen, Selda Şen, Robert G Croy, Deyu Li, Kyungsuk Yum, Jin-Ho Ahn, Hong Jin, Daniel A Heller, John M Essigmann, Daniel Blankschtein, Michael S Strano. Nature nanotechnology.
- "To address surface reaction network complexity using scaling relations machine learning and DFT calculations." Zachary W Ulissi, Andrew J Medford, Thomas Bligaard, Jens K Nørskov. Nature communications.
- "Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes." Wonjoon Choi, Zachary W Ulissi, Steven FE Shimizu, Darin O Bellisario, Mark D Ellison, Michael S Strano. Nature communications.
- "Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction." Zachary W Ulissi, Michael T Tang, Jianping Xiao, Xinyan Liu, Daniel A Torelli, Mohammadreza Karamad, Kyle Cummins, Christopher Hahn, Nathan S Lewis, Thomas F Jaramillo, Karen Chan, Jens K Nørskov. ACS Catalysis.
- "Active learning across intermetallics to guide discovery of electrocatalysts for CO 2 reduction and H 2 evolution." Kevin Tran, Zachary W Ulissi. Nature Catalysis.
- "Multi-Task Machine Learning to Predict ORR Catalyst Descriptors and Performance across Surface Composition." Aini Palizhati, Seoin Back, Kevin Tran, Zachary Ulissi. 2019 AIChE Annual Meeting.
- "Graph Convolutional Machine Learning Methods for the Predictions of Adsorption and Thermochemistry and Surface Stability." Seoin Back, Aini Palizhati, Wen Zhong, Nianhan Tian, Kevin Tran, Zachary Ulissi. 2019 AIChE Annual Meeting.
- "Thermodynamic Techniques to Capture Non-Ideal Surfactant Assembly at Hard Nanoscale Interfaces." Junwoong Yoon, Zachary Ulissi. 2019 AIChE Annual Meeting.
- "Towards Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks." Aini Palizhati, Wen Zhong, Kevin Tran, Seoin Back, Zachary W Ulissi. Journal of chemical information and modeling.
- "Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts." Seoin Back, Junwoong Yoon, Nianhan Tian, Wen Zhong, Kevin Tran, Zachary W Ulissi. The journal of physical chemistry letters.