Pre-Conference Tutorial Programme

Advanced materials are essential for solving critical global challenges such as energy, sustainability, healthcare, and resource scarcity. Traditional discovery methods, however, remain slow, costly, and inefficient. This tutorial introduces theoretical frameworks and methodologies behind combinatorial material studies, exploring how data-driven techniques and automated atomic manufacturing can dramatically accelerate materials discovery. Participants will learn how combinatorial approaches—such as rapid prototyping, high-throughput screening, and accelerated design of experiment—can enable faster identification and optimization of functional materials, paving the way for transformative innovations across diverse fields.

Experimental research in chemistry, biology, and materials science is often limited by slow, manual workflows that demand significant time, expertise, and physical resources. Self-driving laboratories offer a powerful solution by combining automation with artificial intelligence to accelerate discovery and improve reproducibility. This tutorial provides a practical introduction to the core principles behind self-driving labs, aimed at researchers who may not have prior experience with laboratory automation, programming, or statistical modelling. The tutorial is structured into four sections. The first introduces laboratory automation, focusing on how to design an automated experimental system, select appropriate devices, and establish communication between components. The second section explores Bayesian optimisation as a method for guiding experimental decisions, with a focus on intuitive understanding rather than formal theory. The third section demonstrates how automation and optimisation can be integrated to create a fully autonomous self-optimising flow reactor for chemical synthesis. The final section includes a live demonstration using AMLearn, a platform developed by the presenter, to illustrate how the ideas discussed can be implemented in practice.

This tutorial will explore how artificial intelligence (AI), particularly Graph Neural Networks (GNNs), is transforming scientific workflows for complex materials research. Attendees will gain practical insights into automating computational experiments, training GNNs on real-world datasets, and accelerating materials discovery. The session will include hands-on exercises, equipping researchers with the skills to integrate AI-enhanced scientific workflows into various domains, including chemistry and biology. This tutorial aims to provide a balanced and insightful exploration of AI-driven workflows in materials science, with a focus on reproducibility and accessibility for the AI4X community.

Climate change poses an existential threat to humanity, with the growing climate energy imbalance driving more frequent and severe extreme weather events. Accurate forecasting of such events is crucial for timely action but remains computationally expensive using traditional methods. Recent advances in AI Numerical Weather Prediction (AINWP) models offer a transformative approach—delivering forecasts thousands of times faster than numerical simulations—enabling large ensemble predictions for extreme events. However, identifying the precursors to these extremes, and understanding how they evolve under climate change, remains an open challenge. In this tutorial, we will first demonstrate how AINWP models can predict the track and intensity of a tropical cyclone. We will then introduce an interpretable machine learning framework, XAI4Extremes, which generates relevance weather maps to highlight key extreme-weather precursors identified by deep learning models.

This tutorial presents an overview of how large language models (LLMs) are reshaping the landscape of scientific discovery. We cover critical advancements in applying LLMs to fundamental tasks in the scientific discovery process, including: (1) initial hypothesis direction search, (2) fine-grained hypothesis formulation from coarse-grained hypothesis direction, (3) interaction between experimental results and hypothesis refinement, (4) automatic novelty evaluation of discovered hypotheses, and (5) benchmarking LLMs for scientific discovery. Participants will gain a bird’s-eye view of the current field, as well as in-depth insights into each of these directions. Hands-on experience will also be provided through interactive demos showcasing LLMs for scientific discovery.

Continuation of the LLMs for Scientific Discovery tutorial. Includes hands-on exercises, group discussions, and interactive demos, deepening understanding of LLMs for scientific research and benchmarking.