The Ulissi Group in the Department of Chemical Engineering at Carnegie Mellon University applies molecular simulations, high-throughput computation, and machine learning methods development to design materials at the atomistic scale. We use a variety of molecular simulation techniques including both electronic structure calculations (DFT) and soft materials (molecular dynamics) to understand material properties at interfaces. Applications currently include electrochemical catalysis, thermal catalysis, nanoparticle design, surfactant design, polymer/catalyst interfaces, and additive manufacturing.
Machine learning development for surface science and catalysis requires improvements in the efficiency of calculations, automated methods for surface science to build large datasets, and machine learning models that can bridge both organic and inorganic materials. Check out the Research site for more details on the applications, and the Software & Datasets site for links to the actual models and datasets we generate. Almost all of our work is collaborative and open science, so please contact us if you have questions. We try to post all of our papers, analysis, and code in repositories alongside our papers, so if something is missing it’s probably because we forgot to put it up!
Carnegie Mellon University has complementary strengths for our group, including computational chemistry (Kohn worked here!), machine learning (#1 in the country), general engineering, and chemical engineering (well known for computational and mathematical methods, especially for process systems). We collaborate across campus, and we also work with a large number of partners with complementary skills, including Penn State, the University of Toronto, Johns Hopkins University, and national labs like LBL and LLNL.
We are always looking for excellent PhD students, post-docs, MS students, and undergraduates (more info)!
We are grateful for funding a variety of sources, including federal funding (DOE, NSF, ARPAE), national labs (LLNL), computing resources (NERSC, PSC, NVIDIA), and foundations (3Mgives, ACS, others).
The Army Research Office awards a new project on ML for methanol chemistry on oxides. On to the science!5/2020
Our collaborative experiment/theory work with Ted Sargent (Toronto) on CO2 reduction catalysis is published in Nature!3/2020
Zack received a 3M Non-Tenured Faculty Award to support his research over the next three years.3/2020
Two new post-docs have joined: Javi will be working on ML methods applied to oxygen chemistry, and Rajesh will be working on our ARPA-E project on surface segregation. See their bio's in the member section, and looking forward to the great science coming!1/2020
Our group was awarded a collaborative grant from the Sloan Foundation with Shoji Hall and Iryna Zenyuk on zinc air battery development. Pari will be leading!