We are pleased to highlight our colleagues’ participation in MIND-MATTER, a research project focused on the Al-guided study of materials with computational functionality.
The project is led by Andrey Ustyuzhanin (Constructor Knowledge Labs) and brings together an interdisciplinary team including Wilfred van der Wiel (University of Twente), Roman Novikov (Constructor Tech), and Mariia Snigireva (Constructor Knowledge Labs).
MIND-MATTER: AI-Driven Discovery of Self-Learning Materials applies an established Al Scientist workflow to the design and analysis of reconfigurable nonlinear processing units – a class of physical computing systems whose behavior is governed by underlying charge-transport mechanisms. The project employs Al-guided hypothesis generation, experimental design, and data analysis to systematically investigate how different charge-transport processes give rise to distinct computational behaviors.
A central objective of the research is to discriminate between variable-range hopping and space-charge-limited current as the physical mechanisms underlying static and temporal computation in these systems.
Through iterative cycles of hypothesis formulation, targeted measurement, and model refinement, the project investigates how physical charge transport can enable concrete computational capabilities, including adaptation, memory, and pattern recognition. The aim is to develop material systems that can learn from input signals and autonomously adjust their response to recognise complex patterns, effectively embedding learning and computation into the material itself.
“There is a long-standing gap between material physics and computation. In MIND-MATTER, we address this gap head-on by combining AI-guided scientific reasoning with discriminative experiments, aiming to make the
computational capabilities of physical systems transparent, testable, and predictable.”
— Andrey Ustyuzhanin, Project Lead, Constructor Knowledge Labs
By addressing the lack of transparent, experimentally testable links between material physics and computation, the project contributes to a deeper understanding of physical computing and the foundations of computation in material systems.
This work is funded by the Advanced Research + Invention Agency (ARIA) as part of its AI Scientist portfolio dedicated to accelerating scientific discovery.
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