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

no code implementations • 3 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.

Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning

no code implementations • 23 Nov 2021 • Adarsh Dave, Jared Mitchell, Sven Burke, Hongyi Lin, Jay Whitacre, Venkatasubramanian Viswanathan

In this work, we introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte.

Bayesian Optimization BIG-bench Machine Learning

AutoMat: Accelerated Computational Electrochemical systems Discovery

no code implementations • 3 Nov 2020 • Emil Annevelink, Rachel Kurchin, Eric Muckley, Lance Kavalsky, Vinay I. Hegde, Valentin Sulzer, Shang Zhu, Jiankun Pu, David Farina, Matthew Johnson, Dhairya Gandhi, Adarsh Dave, Hongyi Lin, Alan Edelman, Bharath Ramsundar, James Saal, Christopher Rackauckas, Viral Shah, Bryce Meredig, Venkatasubramanian Viswanathan

Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation.

Management Physics-informed machine learning

Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning

no code implementations • 22 Oct 2019 • Adarsh Dave, Jared Mitchell, Kirthevasan Kandasamy, Sven Burke, Biswajit Paria, Barnabas Poczos, Jay Whitacre, Venkatasubramanian Viswanathan

Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases.

BIG-bench Machine Learning