Luca Ricci

Luca Ricci is a doctoral researcher at KU Leuven, where his work centres on the development of MXene-based nanocomposite materials for neuromorphic computing applications. He holds a Master’s degree in Artificial Intelligence and brings over three years of research experience in robotics systems, providing him with a distinctive perspective that spans theoretical materials science, computational modelling, and applied intelligent systems.

His interdisciplinary profile bridges experimental nanoscience and the formal study of brain-inspired computation, with a particular focus on exploiting the intrinsic memristive and conductive properties of two-dimensional transition metal carbides.

Ricci’s primary research investigates Ti₃C₂Tₓ MXene synthesised and dispersed within polyvinyl alcohol (PVA) matrices — including structures formed from 100 picolitre PVA nanodroplets — as functional substrates for neuromorphic device architectures. A central theme of this work is the quantitative modelling of electron tunnelling between MXene flakes and lamellae, connecting atomic-scale conductance mechanisms to emergent memristive behaviour observable at the device level.

On the computational side, he develops molecular dynamics simulation frameworks to characterise ionic intercalation dynamics, thermal transport, and electrostatic field distributions in nanostructured materials. This includes applying percolation theory to composite films and studying how flake morphology and interlayer spacing govern collective electronic properties.

Ricci is equally invested in the development of AI agent architectures and self-hosted computational infrastructure to support reproducible, automated research pipelines. His open-source work on neuromorphic research is hosted publicly on GitHub.

His principal research interests include MXene chemistry and surface functionalisation, memristive and synaptic device engineering, molecular dynamics simulation of ionic and electronic transport, percolation phenomena in nanocomposite films, and the application of Maxwell’s equations to nanoscale electrostatic modelling. He is further interested in the theoretical foundations of neural network architectures and their physical implementation in hardware.

Beyond device-level research, Ricci is motivated by the prospect of deploying neuromorphic and AI-driven technologies toward measurable sustainability outcomes — integrating advances from electrical engineering, robotics, and materials science to address systemic challenges in the European research and industrial landscape. He approaches problems with rigorous analytical depth while maintaining an orientation toward practical, translatable impact.