Self-organisation sculpts the topology and physicochemical properties of biological, organic, inorganic, and hybrid materials. Its explainability emerges from understanding the core principles and preconditions that dictate synthetic outcomes. A formal, machine-actionable representation of these blueprints is crucial for developing explainable AI systems that explore both immediate and deep chemical spaces. In this talk, we first show how molecular modelling uncovers elusive self-organisation patterns in cluster materials, some of which can be used as scaffolds for designing extended material systems. Next, we examine the development of dynamic knowledge-graph systems that accelerate the discovery of self-organised reticular material architectures.
Zoom link: https://zoom.us/j/93830536346?pwd=zGN6ZSGZau7jQnzY9nOc7nX6HUwHxs.1