Adarsh Dave
Papers
9
Total Citations
451
H-Index
7
About
Adarsh Dave is a pioneering researcher at the intersection of autonomous experimentation and battery science. His work centers on accelerating the discovery and optimization of next-generation battery electrolytes through the innovative coupling of robotic test-stands with machine learning algorithms. Dave’s major contributions include developing autonomous platforms that can perform hundreds of sequential experiments, guided by Bayesian optimization, to efficiently explore vast chemical spaces. His landmark 2022 paper on optimizing non-aqueous Li-ion battery electrolytes has garnered 180 citations, while his foundational 2020 work on autonomous electrolyte discovery has been cited 164 times. These studies demonstrate how robotic systems like "Clio" can dramatically reduce the years-long timelines traditionally required for battery innovation. Dave has also advanced the field through differentiable geometric deep learning models for chemical mixtures, enabling more accurate property predictions. His work on the Advanced Electrolyte Model (AEM) for aqueous systems further showcases his commitment to developing robust computational tools. By merging robotics, AI, and electrochemistry, Dave is fundamentally transforming how we discover and optimize the critical materials needed for electrifying transportation and aviation.
Research Focus
Key Achievements
Top Papers
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- 5AutoMat: Automated materials discovery for electrochemical systems19 citations · 2022
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- 9AutoMat: Accelerated Computational Electrochemical systems Discovery2 citations · 2020