# Workshop media

**Introductory talks and Tutorials**

**Bjørk Hammer: ****Machine learning acceleration of global structure optimization **Part I | Part II | pdf | tutorial notebook

**Armi Tiihonen: ****Gaussian processes and Bayesian optimization **Lecture | tutorial | pdf | tutorial notebook (solution notebook)

**Matthew Evans: ****Open databases integration for materials design (OPTIMADE) **Lecture | tutorial | pdf | tutorial notebook

**Yury Lysogorskiy: Active learning for interatomic potentials development **Lecture | tutorial | pdf | tutorial notebook

**Wednesday talks**

**Philippe Schwaller: Bayesian optimisation for chemical reactions [pdf]**

**Simona Capponi: Batched Bayesian optimization for molecular structures [pdf]**

**Rasmus Kronberg: Machine learning and materials science on LUMI [pdf]**

**Tonio Buonassisi: Inverse Design: Why Aren’t We There Yet? [pdf]**

**Kristian Berland: Screening of low lattice thermal conductivity materials using active learning [pdf]**

**Maria Chan: Theory-informed AI for experimental data interpretation [pdf]**

**Thursday talks**

**Adam Foster: Machine learning in scanning probe microscopy [pdf]**

**Xin Yang: Investigating oxygen reduction kinetics at gold-water interface using machine learning potentials [pdf]**

**Leonard Ng: High-throughput, single-experiment optimisation of roll-to-roll fabricated non-fullerene acceptor photovoltaics using machine learning [pdf]**

**Amirhossein Naghdi Dorabati: Neural network interatomic potentials for nanoindentation MD simulations [pdf]**

**Gareth Conduit: The modern-day blacksmith [pdf]**

*break*

**Kim Jelfs: Remembering the lab in computational molecular material discovery [pdf]**

**Venkat Kapil: The first-principles phase diagram of monolayer nanoconfined water [pdf]**

**Claudio Zeni: Exploring the robust extrapolation of high-dimensional machine learning potentials [pdf]**

**Ming-Chiang Chang: Integrated autonomous and user-guided active learning for targeted material synthesis [pdf]**

**Joakim Löfgren: Bayesian optimization for experimental materials design [pdf]**

**Juha Koivisto: Mapping fluid state properties of biofoams to solid state using Bayesian optimization [pdf]**

**Shijing Sun: Application of machine learning in EV battery R&D [pdf]**

**Friday talks**

**Samuel Kaski: Collaborative AI for assisting virtual laboratories [pdf]**

**Ulpu Remes: Multi-fidelity Bayesian optimization structure search [pdf]**

**Sašo Džeroski: Semi-supervised and active multi-target regression for material design [pdf]**

**Martin Pimon: Electrons go nuclear: a unique interaction with Thorium-229 [pdf]**

**Julija Zavadlav: Deep molecular modeling: the impact of training objective [pdf]**