1.6k citations · 2k across the 8 of their papers we have counts for
19 papers
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…
Rotation Invariant Graph Neural Networks using Spin Convolutions
Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das +4
Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomic systems. Simulation techniques…
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
Weihua Hu, Muhammed Shuaibi, Abhishek Das +5
With massive amounts of atomic simulation data available, there is a huge opportunity to develop fast and accurate machine learning models to approximate expensive physics-based ca…
Creative Sketch Generation
Songwei Ge, Vedanuj Goswami, C. Lawrence Zitnick +1
Sketching or doodling is a popular creative activity that people engage in. However, most existing work in automatic sketch understanding or generation has focused on sketches that…
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…