Search Results for author: Venkatasubramanian Viswanathan

Found 21 papers, 2 papers with code

Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning

no code implementations3 Oct 2023 Shang Zhu, Bharath Ramsundar, Emil Annevelink, Hongyi Lin, Adarsh Dave, Pin-Wen Guan, Kevin Gering, Venkatasubramanian Viswanathan

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces.

Differentiable Turbulence II

no code implementations25 Jul 2023 Varun Shankar, Romit Maulik, Venkatasubramanian Viswanathan

Differentiable fluid simulators are increasingly demonstrating value as useful tools for developing data-driven models in computational fluid dynamics (CFD).

Differentiable Turbulence

no code implementations7 Jul 2023 Varun Shankar, Romit Maulik, Venkatasubramanian Viswanathan

Deep learning is increasingly becoming a promising pathway to improving the accuracy of sub-grid scale (SGS) turbulence closure models for large eddy simulations (LES).

Computational Efficiency

Importance of equivariant and invariant symmetries for fluid flow modeling

no code implementations3 May 2023 Varun Shankar, Shivam Barwey, Zico Kolter, Romit Maulik, Venkatasubramanian Viswanathan

Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics.

Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learning

no code implementations13 Feb 2023 Shivam Barwey, Varun Shankar, Venkatasubramanian Viswanathan, Romit Maulik

The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes.

Differentiable physics-enabled closure modeling for Burgers' turbulence

no code implementations23 Sep 2022 Varun Shankar, Vedant Puri, Ramesh Balakrishnan, Romit Maulik, Venkatasubramanian Viswanathan

Data-driven turbulence modeling is experiencing a surge in interest following algorithmic and hardware developments in the data sciences.


Super-linear Scaling Behavior for Electric Vehicle Chargers and Road Map to Addressing the Infrastructure Gap

no code implementations6 Apr 2022 Alexius Wadell, Matthew Guttenberg, Christopher P. Kempes, Venkatasubramanian Viswanathan

Enabling widespread electric vehicle (EV) adoption requires substantial build-out of charging infrastructure in the coming decade.

Score-Based Generative Models for Molecule Generation

no code implementations7 Mar 2022 Dwaraknath Gnaneshwar, Bharath Ramsundar, Dhairya Gandhi, Rachel Kurchin, Venkatasubramanian Viswanathan

Recent advances in generative models have made exploring design spaces easier for de novo molecule generation.


Cell-Level State of Charge Estimation for Battery Packs Under Minimal Sensing

no code implementations17 Sep 2021 Dong Zhang, Luis D. Couto, Ross Drummond, Shashank Sripad, Venkatasubramanian Viswanathan

This manuscript presents an algorithm for individual Lithium-ion (Li-ion) battery cell state of charge (SOC) estimation in a large-scale battery pack under minimal sensing, where only pack-level voltage and current are measured.

Differentiable Physics: A Position Piece

no code implementations14 Sep 2021 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 numerical methods for physical simulation.


The promise of energy-efficient battery-powered urban aircraft

no code implementations16 Jun 2021 Shashank Sripad, Venkatasubramanian Viswanathan

Improvements in rechargeable batteries are enabling several electric urban air mobility (UAM) aircraft designs with up to 300 miles of range with payload equivalents of up to 7 passengers.

Closed-Loop Design of Proton Donors for Lithium-Mediated Ammonia Synthesis with Interpretable Models and Molecular Machine Learning

no code implementations18 Aug 2020 Dilip Krishnamurthy, Nikifar Lazouski, Michal L. Gala, Karthish Manthiram, Venkatasubramanian Viswanathan

In this work, we experimentally determined the efficacy of several classes of proton donors for lithium-mediated electrochemical nitrogen reduction in a tetrahydrofuran-based electrolyte, an attractive alternative method for producing ammonia.

Classification General Classification

Universal Battery Performance and Degradation Model for Electric Aircraft

no code implementations6 Jul 2020 Alexander Bills, Shashank Sripad, William L. Fredericks, Matthew Guttenberg, Devin Charles, Evan Frank, Venkatasubramanian Viswanathan

We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning in the form of Universal Ordinary Differential Equations (U-ODE's) combined with an electrochemical cell model and degradation models which include solid electrolyte interphase (SEI) growth, lithium plating, and charge loss.

Physics-informed machine learning

Machine Learning Enabled Discovery of Application Dependent Design Principles for Two-dimensional Materials

1 code implementation19 Mar 2020 Victor Venturi, Holden Parks, Zeeshan Ahmad, Venkatasubramanian Viswanathan

The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations.

BIG-bench Machine Learning

Dendrite suppression of metal electrodeposition with liquid crystalline electrolytes

2 code implementations9 Jul 2019 Zeeshan Ahmad, Zijian Hong, Venkatasubramanian Viswanathan

We study the dynamics of electrodeposition of a metal in contact with a liquid crystalline electrolyte.

Applied Physics Chemical Physics

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