1 code implementation • 16 Oct 2024 • Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu, Brandon M. Wood, Misko Dzamba, Meng Gao, Ammar Rizvi, C. Lawrence Zitnick, Zachary W. Ulissi
The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware.
no code implementations • 14 Mar 2024 • Christian M. Clausen, Jan Rossmeisl, Zachary W. Ulissi
Computational high-throughput studies, especially in research on high-entropy materials and catalysts, are hampered by high-dimensional composition spaces and myriad structural microstates.
1 code implementation • 25 Oct 2023 • Nima Shoghi, Adeesh Kolluru, John R. Kitchin, Zachary W. Ulissi, C. Lawrence Zitnick, Brandon M. Wood
Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains.
1 code implementation • 29 Nov 2022 • Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon M. Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C. Lawrence Zitnick, Zachary W. Ulissi
Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications.
no code implementations • 2 Feb 2021 • Kevin Tran, Willie Neiswanger, Kirby Broderick, Erix Xing, Jeff Schneider, Zachary W. Ulissi
We address this issue by relaxing the catalyst discovery goal into a classification problem: "What is the set of catalysts that is worth testing experimentally?"
Chemical Physics
1 code implementation • 20 Dec 2019 • Kevin Tran, Willie Neiswanger, Junwoong Yoon, Eric Xing, Zachary W. Ulissi
These uncertainty estimates are instrumental for determining which materials to screen next, but there is not yet a standard procedure for judging the quality of such uncertainty estimates objectively.
Materials Science Computational Physics