Zachary Ulissi-Chemical Engineering - Carnegie Mellon University

Zachary Ulissi

Assistant Professor, Chemical Engineering

Office: Doherty Hall A207A

Bio

Prof. Zachary W. Ulissi joined Carnegie Mellon University in 2016.  He received his B.S. in Physics and B.E. in Chemical Engineering from the University of Delaware in 2009, a Masters of Advanced Studies in Mathematics from the University of Cambridge in 2010, and a Ph.D. in Chemical Engineering from MIT in 2015.  His thesis research at MIT focused on the the applications of systems engineering methods to understanding selective nanoscale carbon nanotube devices and sensors under the supervision of Michael S. Strano and Richard Braatz.  Prof. Ulissi was then a postdoctoral fellow at Stanford with Jens K. Nørskov where he worked on machine learning techniques to simplify complex catalyst reaction networks, applied to the electrochemical reduction of N2 and CO2 to fuels.

Education

B.E. in Chemical Engineering, B.S. in Physics from the University of Delaware, 2009

M.A.St. in Applied Mathematics from Churchill College, University of Cambridge 2010

Ph.D. in Chemical Engineering from the Massachusetts Institute of Technology in 2015

Research

Chemical, mechanical, electronic, and thermal properties of materials all change at the nanoscale.  Entropic fluctuations become more important and materials confined in 1- or 2- dimensions (tubes, wires, sheets) behave differently.  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.  Applications include biomedical sensors (nanotube-based optical sensors)  and energy applications (CO2 to fuels, fuel cells, thermal catalysis).

Controlling selectivity of nanoscale interfaces with co-adsorbates and soft functionalizations

Interfacial selectivity is central to catalysis and biomedical imaging. Nanoparticles with desirable properties (optical, electronic, catalytic) are often available, but face challenges in complex chemical environments with a variety of substrates.  Restricting access to these surfaces using soft functionalizations (polymers, DNA, surfactants) has been successful in developing selective carbon-nanotube sensors, but designing the selectivity a priori remains a challenge.  Prof. Ulissi uses molecular simulations and thermodynamic models to study interfacial packing and selectivity at these interfaces. 

Machine-learning based approaches to accelerate materials screening

Density functional theory (DFT) is the tool of choice for studying reactions on chemical surfaces.  However, it is prohibitively expensive to use full-accuracy DFT to identify catalyst surface with optimal properties due to the sheer number of possible materials, facets, and active sites.  The search process can be greatly accelerated by using flexible surrogate models that learn from previous DFT calculations to predict interesting materials.  This process can be accelerated with deep-learning methods from the computer science community.  Prof. Ulissi contributes to the AMP software package and is exploring larger models and networks for accelerated learning and uncertainty quantification using Google’s tensorflow library.  Applications include both thermal and electrochemical catalysis.

Bayesian methods for complex reaction mechanism reduction and elucidation

Identifying the most important reaction mechanisms in hydrocarbon reaction networks remains a challenge for the computational chemistry community.  Treating all possible intermediates with full-accuracy DFT is impossible, but these tools can be focused on the most interesting reactions.  Bayesian techniques allow uncertainty to be propagated through these networks and through multiple levels of approximation and the most important reaction steps to be identified probabilistically. Current applications are large hydrocarbon reaction networks in thermal catalysis.

Awards and Honors

NSF Graduate Research Fellowship (2009)

DOE Computational Science Graduate Fellowship (2010-2014)

Research Websites

Catalysis and Surface Science

Energy Science and Engineering

Research Group Site

Publications

Recent Publications
Selected Publications
Full Publications

Recent Publications

Zachary W. Ulissi, A. J. Medford, Thomas Bligaard, and Jens K. Norskov. To address surface reaction network complexity using scaling relations machine learning and dft calculations. Nature Communications, 2017.

Zachary W Ulissi, Ananth Govind Rajan, and Michael S Strano. Persistently auxetic materials: Engineering the poisson ratio of 2d self-avoiding membranes under conditions of non-zero anisotropic strain. ACS nano, 10(8):7542–7549, 2016.

Zachary W Ulissi, Aayush R Singh, Charlie Tsai, and Jens K Norskov. Automated discovery and construction of surface phase diagrams using machine learning. The Journal of Physical Chemistry Letters, 2016.

Selected Publications

Zachary W. Ulissi, A. J. Medford, Thomas Bligaard, and Jens K. Norskov. To address surface reaction network complexity using scaling relations machine learning and dft calculations. Nature Communications, 2017.

Zachary W. Ulissi, Jingqing Zhang, Vishnu Sresht, Daniel Blankschtein, and Michael S. Strano. A 2d equation-of-state model for corona phase molecular recognition on single-walled carbon nanotube and graphene surfaces. Langmuir, 31(1):628636, 2015.

Zachary W. Ulissi*, Wonjoon Choi*, Steven F. E. Shimizu, Darin O. Bellisario, Mark D. Ellison, and Michael S. Strano. Diameter-dependent ion transport through the interior of isolated single-walled carbon nanotubes. Nature Communications, 4, September 2013. 

Full Publications

https://scholar.google.com/citations?hl=en&user=E0tlVgQAAAAJ&view_op=list_works&sortby=pubdate