Zachary W. Ulissi

Research Scientist at Meta Fundamental AI Research (FAIR), and Adjunct Professor of Chemical Engineering at Carnegie Mellon University.


I joined Meta’s Fundamental AI Research lab in 2023 to work on AI for chemistry and climate applications and am located in the SF bay area. I am extremely excited about how AI/ML methods can help many types of quantum chemistry simulations and lead to better materials and catalysts.

In 2024 I am an adjunct professor of chemical engineering at CMU. Prior to 2023 I was an assistant and then associate professor, and in 2023 I was on leave from my position at CMU. I joined Carnegie Mellon University in 2017, after doing my PhD at MIT and post-doc at Stanford. My PhD work at MIT focused on the applications of systems engineering methods to understanding selective nanoscale carbon nanotube devices and sensors under the supervision of Michael Strano and Richard Braatz. I did my postdoctoral work at Stanford with Jens Nørskov where I worked on machine learning techniques to simplify complex catalyst reaction networks, applied to the electrochemical reduction of N2 and CO2 to fuels. At CMU I continued these efforts to model, understand, and design nanoscale interfaces using machine learning and predictive methods to guide detailed molecular simulations.

In my free time, I enjoy the outdoors and used to be a competitive cyclist, though mostly I do bike trips for fun now. I also enjoy cooking and traveling, and have a toddler (Enzo) and a large friendly dog (Laika).


Jul 01, 2023 The OCP Demo website was launched! Check it out at
Dec 01, 2022 AdsorbML, a strategy to use pre-trained GNNs to massively accelerate the adsorbate placement and adsorption energy calculation process, was released on arxiv!
Jul 01, 2022 I was promoted to Associate Professor of Chemical Engineering at CMU!
Jul 01, 2022 The OC22 dataset and models for oxide electrocatalysts were released.
Oct 01, 2020 The OC20 dataset and open ML models were released to the public!

selected publications

  1. The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
    Richard Tran , Janice Lan , Muhammed Shuaibi , and 14 more authors
    ACS Catalysis, 2023
  2. AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials
    Janice Lan , Aini Palizhati , Muhammed Shuaibi , and 6 more authors
    npj Computational Materials, 2023
  3. FINETUNA: Fine-tuning Accelerated Molecular Simulations
    Joseph Musielewicz , Xiaoxiao Wang , Tian Tian , and 1 more author
    Machine Learning: Science and Technology, Sep 2022
  4. Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion
    Brook Wander , Kirby Broderick , and Zachary W. Ulissi
    Catal. Sci. Technol., Jul 2022
  5. Open Catalyst 2020 (OC20) Dataset and Community Challenges
    Lowik Chanussot , Abhishek Das , Siddharth Goyal , and 14 more authors
    ACS Catalysis, Apr 2021
  6. Accelerated discovery of CO2 electrocatalysts using active machine learning
    Miao Zhong , Kevin Tran , Yimeng Min , and 19 more authors
    Nature, May 2020
  7. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution
    Kevin Tran , and Zachary W. Ulissi
    Nature Catalysis, Sep 2018