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5.00/5 · 1 review
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5.05/4/2026

Always kind, respectful, and approachable.

About Kevin

Kevin Rossi is an Assistant Professor in the Department of Materials Science and Engineering within the Faculty of Mechanical Engineering at Delft University of Technology. He leads the Materials Intelligence research group as Principal Investigator, focusing on computational modeling and artificial intelligence to discover, understand, and engineer high-performance materials for green energy, sustainable chemistry, and circular metallurgy. His work integrates machine learning with advanced simulations to predict material properties, uncover reaction mechanisms, and design sustainable processes that minimize environmental impact and address raw material criticality. Affiliated with the Climate Safety and Security Research Center at TU Delft Campus The Hague, Rossi serves as academic lead for Materials Security, bridging atomic-scale insights to planetary-scale policy implications for material safety, security, and sustainability.

With a background including postdoctoral research at the École Polytechnique Fédérale de Lausanne, Rossi has authored numerous influential publications in computational materials science, catalysis, and nanotechnology. Key works include 'Alloying as a Strategy to Boost the Stability of Copper Nanocatalysts during the Electrochemical CO2 Reduction Reaction' (Journal of the American Chemical Society, 2023), 'Shaping Copper Nanocatalysts to Steer Selectivity in the Electrochemical CO2 Reduction Reaction' (Accounts of Chemical Research, 2022), 'Well-Defined Copper-Based Nanocatalysts for Selective Electrochemical Reduction of CO2 to C2 Products' (ACS Energy Letters, 2022), 'Data-driven simulation and characterisation of gold nanoparticle melting' (Nature Communications, 2021), 'Uncertainty estimation for molecular dynamics and sampling' (The Journal of Chemical Physics, 2021), and recent contributions such as 'Uncertainty in the era of machine learning for atomistic modeling' (Digital Discovery, 2025) and 'Machine Learning-Based Predictions of Henry Coefficients for Long-Chain Alkanes in One-Dimensional Zeolites: Application to Hydroisomerization' (The Journal of Physical Chemistry C, 2025). His research has garnered significant recognition through citations exceeding 1400 and invitations to speak on critical raw materials at events across Europe. Rossi has also secured grants for collaborative networks on materials security and climate transition.