793 citations · 942 across the 6 of their papers we have counts for
6 papers
Robust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling
Ji Qi, Tsz Wai Ko, Brandon C. Wood +2
Machine learning interatomic potentials (MLIPs) enable the accurate simulation of materials at larger sizes and time scales, and play increasingly important roles in the computatio…
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Yi-Lun Liao, Brandon Wood, Abhishek Das +1
Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying Transformers to the domain of 3D atomistic systems. However, they are limited to small degree…
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,…
An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage
C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das +14
Scalable and cost-effective solutions to renewable energy storage are essential to addressing the world's rising energy needs while reducing climate change. As we increase our reli…
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal +14
Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer produc…