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]

Panel Discussion I

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

Gareth Conduit: The modern-day blacksmith [pdf]


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]

Panel Discussion II

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

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

Closing remarks and Prizes