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AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
Janice Lan, Aini Palizhati, Muhammed Shuaibi +6
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…
Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery
Adeesh Kolluru, Muhammed Shuaibi, Aini Palizhati +6
The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While effective in inter…
Spherical Channels for Modeling Atomic Interactions
C. Lawrence Zitnick, Abhishek Das, Adeesh Kolluru +5
Modeling the energy and forces of atomic systems is a fundamental problem in computational chemistry with the potential to help address many of the world's most pressing problems,…
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
Richard Tran, Janice Lan, Muhammed Shuaibi +14
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. One class of materials…
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets
Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram +4
Recent years have seen the advent of molecular simulation datasets that are orders of magnitude larger and more diverse. These new datasets differ substantially in four aspects of…
Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations
Anuroop Sriram, Abhishek Das, Brandon M. Wood +2
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 toward…