Papers
9
Total Citations
227
H-Index
7
About
Kedar Hippalgaonkar is a pioneering researcher at the intersection of materials science, machine learning, and laboratory automation, with a focus on accelerating the discovery and optimization of functional materials. His work is perhaps best known for advancing knowledge-integrated machine learning frameworks for materials discovery, a contribution that has already garnered over 134 citations since 2023, reflecting its significant influence on the field. Hippalgaonkar has been instrumental in developing self-driving laboratories and materials acceleration platforms, designing automated, closed-loop systems that dramatically reduce human intervention in complex experimental workflows — from high-throughput graphene film screening to photo-electrocatalytic materials discovery for renewable energy applications. His robotics-driven approaches address real-world challenges such as viscous liquid handling and pH adjustment, combining physics-informed machine learning with automated pipetting systems to solve problems that have long relied on tedious manual trial-and-error. Beyond automation, his research extends into the thermoelectric properties of novel 2D materials such as PdSe₂, demonstrating breadth across both computational and experimental domains. Collectively, his contributions are reshaping how scientists design experiments, positioning autonomous, data-driven platforms as the future of materials research.
Research Focus
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Top Papers
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