no code implementations • 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.
2 code implementations • 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.
2 code implementations • 17 Jun 2022 • Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon M. Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Felix Therrien, Jehad Abed, Oleksandr Voznyy, Edward H. Sargent, Zachary Ulissi, C. Lawrence Zitnick
The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials.
1 code implementation • ICLR 2022 • Anuroop Sriram, Abhishek Das, Brandon M. Wood, Siddharth Goyal, C. Lawrence Zitnick
Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat climate change.
Ranked #2 on Initial Structure to Relaxed Energy (IS2RE) on OC20