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About

Emil Annevelink is an emerging researcher at the intersection of computational materials science, electrochemistry, and machine learning. His work focuses on accelerating the discovery of novel materials for electrochemical systems — particularly batteries and electrolytes — through the creative application of artificial intelligence and automation. Annevelink's most influential contribution, "Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning" (2024, 25 citations), demonstrates his pioneering use of geometric deep learning to model and optimize complex chemical mixtures, directly addressing one of the central challenges in next-generation battery design. Complementing this, his AutoMat framework — developed across two publications in 2020 and 2022 (totaling 21 citations) — established an automated computational pipeline for electrochemical materials discovery, helping to tackle the urgent need for electrification solutions in transportation and industry. Beyond electrochemistry, Annevelink has also explored the mechanical behavior of 2D materials, investigating how topological defects govern three-dimensional deformation in graphene. With a growing citation record and research spanning deep learning, battery science, and nanomaterials, Annevelink represents a new generation of computationally driven materials scientists poised to make meaningful contributions to the clean energy transition.