840 citations · 4.2k across the 66 of their papers we have counts for
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Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learning
Shivam Barwey, Varun Shankar, Venkatasubramanian Viswanathan +1
The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes. This is accomplished here…
Score-Based Generative Models for Molecule Generation
Dwaraknath Gnaneshwar, Bharath Ramsundar, Dhairya Gandhi +2
Recent advances in generative models have made exploring design spaces easier for de novo molecule generation. However, popular generative models like GANs and normalizing flows fa…
Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning
Adarsh Dave, Jared Mitchell, Sven Burke +3
In this work, we introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte. A custom-built automated expe…
Differentiable Physics: A Position Piece
Bharath Ramsundar, Dilip Krishnamurthy, Venkatasubramanian Viswanathan
Differentiable physics provides a new approach for modeling and understanding the physical systems by pairing the new technology of differentiable programming with classical numeri…
Rotation Equivariant Operators for Machine Learning on Scalar and Vector Fields
Paul Shen, Michael Herbst, Venkat Viswanathan
We develop theory and software for rotation equivariant operators on scalar and vector fields, with diverse applications in simulation, optimization and machine learning. Rotation…