We are actively recruiting for post-doctoral positions in the fields of computational chemistry, catalysis, machine learning, and high performance computing. Application include energy storage, hydrogen production, fuel cells, water quality, and more. [Read More]
Muhammed passes his quals!
Muhammed Shuaibi, the fourth PhD student in the group, passed his first-year qualifying exam, discussing his work on augmenting machine learning potentials with physics-based potentials as part of our DOE CCS project on exascale computing. Next step Thesis Proposal!
New project: design of propylene epoxidation catalysts with ML
We will be working with Andy Gellman and John Kitchin (CMU) as well as Jingguang Chen (Columbia / Brookhaven National Lab) on a new project for designing compositionally-diverse propylene epoxidation catalysts sponsored by the NSF “Designing Materials to Revolutionize our Engineering Future (DMREF)” program. On to the science! [Read More]
Two new projects: DOE Data Science in Catalysis
We are part of two new projects funded by the DOE Basic Energy Sciences office on the use of data science and machine learning in catalysis. [Read More]
New project on O2 transport at the ionomer/catalyst interface!
Excited to be part of an academic/industrial partnership led by Shawn Litster (CMU MechE) to design new oxygen-permeable ionomers for platinum interfaces! [Read More]
Papers on graph convolution methods in catalysis published!
In July two papers demonstrating graph convolution methods in catalysis, one for materials for the oxygen evolution reaction, and a methods paper for small molecule adsorbates were published in ACS Catalysis and JPC Letters respectively! [Read More]
Group kayak outing
To enjoy the sunny summer weather in Pittsburgh, we did a group trip to go Kayaking in North Park in Pittsburgh. [Read More]
Richie receives ChESS and SURF fellowships!
Congratulations to one of our newest undergraduate researchers, Rui Qi (Richie) Chen, for winnig both a ChESS fellowship and a SURF fellowship! He will be working on Chenru Duan’s and Heather Kulik’s machine learning method for predicting whether or not our DFT calculations will converge prior to even running them.... [Read More]
Kevin awarded CCMS internship at LLNL
Kevin just accepted a CCMS internship to work with Dr. Joel Varley and the Quantum Simulations Group at Lawrence Livermore National Lab (LLNL) this summer. They will be refining hybrid DFT simulation methods to model the effects of solvents, electrolytles, pH, and electric fields during electrochemical conversion reactions. This collaboration... [Read More]
Kevin featured in This Week in Machine Learning and AI podcast
Kevin was interviewed on This Week in Machine Learning & AI for our research in active optimization of electrocatalysts. Check out the podcast on their site or on Spotify! [Read More]
2019 DOE Allocation
Our supercomputing allocation on Cori at the DOE National Energy Research Scientific Computing Center was renewed and increased to 5.5 million service units. Cori is currently the 12th fastest supercomputer in the world, and the equivalent cost on commodity cloud resources would be something like $200,000. We are excited to... [Read More]
Kevin wins Bushnell fellowship!
Kevin Tran was awarded the Neil and Jo Bushnell Fellowship from CIT for his research in nanotechnology and electronic materials. [Read More]
First funded project announced!
We are working with Prof. Andrew Peterson and team of four others to develop the open-source machine learning simulation AMP code for exascale machines. The project is sponsored by the Computational Chemical Sciences program in the Department of Energy Basic Energy Sciences division. [Read More]
First group paper published!
Congratulations to PhD student Kevin Tran for his first paper published (and group’s first paper on our own!) in Nature Catalysis! Read more about it here: https://www.nature.com/articles/s41929-018-0142-1 [Read More]
Video highlight on research efforts!
CO-coverage and strain effects on NP faceting published in JPC-C!
“Theoretical Investigations of Transition-Metal Surface Energies Under Lattice Strain and CO Environment” published with Michael Tang and Karen Chan from Stanford in JPC-C. Full abstract below! [Read More]
ORR on Cu/Ag thin films published in ACS Applied Energy Materials!
Joint experiment/theory work from time at Stanford, full article here: full article link [Read More]
Spring 18 group hiking trip
We went hiking in Ohiopyle (~1 hour outside of Pittsburgh) with Kevin, Pari, Jun, and Kaylee. Wen and Zong Qian were working hard on their systems final project so couldn’t make it.
Kaylee receives a ChESS summer fellowship!
Congratulations to Kaylee for winning a ChESS fellowship to support her research in the group this summer. Her project is titled “Variational Autoencoders to Learn Efficient Representations of Catalyst Surfaces”.
Pari receives a PPG fellowship!
Pari received a departmental PPG fellowship for 2017-2018, courtesy of the PPG Foundation. Congratulations!
Talk at MRS "AI in Materials" Symposium
Great turnout (packed rooms) for a new AI in materials symposium at the MRS spring meeting in Phoenix. Prof. Ulissi talked about practical benefits and approaches to integrating these into the search for new catalyst materials.
Talk and session chair at ACS Machine Learning in Catalysis symposium
Prof Ulissi co-chaired a session on machine learning methods in catalysis, organized by Hongliang Xin, Andy Peterson and CMU’s John Kitchin. He also gave a talk on the group’s perspective of how ML can fit into a day-to-day computational catalysis workflow.
Group receives a DOE supercomputer allocation!
We received 4 million service unit allocation for 2018 on the Cori supercomputer at the Department of Energy National Energy Research Supercomputing Center (NERSC). This is a boost for our high throughput calculation and NERSC also supports our workflow, machine learning, and database tasks. This is roughly equivalent to about... [Read More]
Setting up the lab at PSC
We visited the Pittsburgh Supercomputing Center (PSC) co-location facility to do some hands-on maintenance with our machines hosted there. Luckily, this was the most labor intensive part of starting the group and building the ‘lab’! We share about 100 high performance CPU and GPU-accelerated nodes with other groups in the... [Read More]
F17 Group dinner
Welcome to new MS students Wen Zhong and Zong Qian Yu!
Wen and Zong Qian will be completing research projects from January through the end of next year. Zong Qian will be working on predictive methods for materials discovery, and Wen will be working on predictive and high-throughput methods for patterned surfaces!
ML for energy materials discovery commentary published in Nature!
A commentary that was written from the proceedings of a workshop we attended in May 2017 was published in Nature, with Zachary Ulissi as a co-signatory. See the article for a high-level description of challenges in applying predictive methods to the discovery of new energy materials: Use machine learning to... [Read More]
Welcome to new PhD students Jun and Pari!
Pari Palizhati and Junwoong Yoon joined the group in November 2017. Welcome!
Kevin passes his quals!
The group’s first PhD student, Kevin Tran, passed his quals with a presentation on his first project “Automated materials simulation: DFT-calculated adsorption energies”. [Read More]
Publication in ACS Catalysis!
Publication in Nature Communications!
Ulissi Group welcomes the first student!
Kevin Tran joined in December 2016, and will be working on systems engineering approaches applied to materials discovery. Welcome to the group!