Zobrazeno 1 - 10
of 149
pro vyhledávání: '"Ulissi, Zachary W."'
Autor:
Barroso-Luque, Luis, Shuaibi, Muhammed, Fu, Xiang, Wood, Brandon M., Dzamba, Misko, Gao, Meng, Rizvi, Ammar, Zitnick, C. Lawrence, Ulissi, Zachary W.
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. AI has the potential to accelerate materials discovery and d
Externí odkaz:
http://arxiv.org/abs/2410.12771
CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks
Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural network potential trained on the OC20 dataset, a related but different task,
Externí odkaz:
http://arxiv.org/abs/2405.02078
Computational high-throughput studies, especially in research on high-entropy materials and catalysts, are hampered by high-dimensional composition spaces and myriad structural microstates. They present bottlenecks to the conventional use of density
Externí odkaz:
http://arxiv.org/abs/2403.09811
Autor:
Wang, Xiaoxiao, Musielewicz, Joseph, Tran, Richard, Ethirajan, Sudheesh Kumar, Fu, Xiaoyan, Mera, Hilda, Kitchin, John R., Kurchin, Rachel C., Ulissi, Zachary W.
Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput efforts. This has prompted an explosion in exploration of machine learn
Externí odkaz:
http://arxiv.org/abs/2311.01987
Autor:
Shoghi, Nima, Kolluru, Adeesh, Kitchin, John R., Ulissi, Zachary W., Zitnick, C. Lawrence, Wood, Brandon M.
Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across
Externí odkaz:
http://arxiv.org/abs/2310.16802
According to density functional theory, any chemical property can be inferred from the electron density, making it the most informative attribute of an atomic structure. In this work, we demonstrate the use of established physical methods to obtain i
Externí odkaz:
http://arxiv.org/abs/2309.04811
Autor:
Lan, Janice, Palizhati, Aini, Shuaibi, Muhammed, Wood, Brandon M., Wander, Brook, Das, Abhishek, Uyttendaele, Matt, Zitnick, C. Lawrence, Ulissi, Zachary W.
Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the adsorption energy for an adsorbate
Externí odkaz:
http://arxiv.org/abs/2211.16486
Catalyst discovery is paramount to support access to energy and key chemical feedstocks in a post fossil fuel era. Exhaustive computational searches of large material design spaces using ab-initio methods like density functional theory (DFT) are infe
Externí odkaz:
http://arxiv.org/abs/2208.12717
Autor:
Kolluru, Adeesh, Shuaibi, Muhammed, Palizhati, Aini, Shoghi, Nima, Das, Abhishek, Wood, Brandon, Zitnick, C. Lawrence, Kitchin, John R, Ulissi, Zachary W
The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While effective in interpolating between available materials, these approaches struggle to gen
Externí odkaz:
http://arxiv.org/abs/2206.02005
Autor:
Tran, Kevin, Neiswanger, Willie, Broderick, Kirby, Xing, Erix, Schneider, Jeff, Ulissi, Zachary W.
The recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-sca
Externí odkaz:
http://arxiv.org/abs/2102.01528