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Download the BOOK OF ABSTRACTS.

Event Registration Deadline:               20.01.2023
Abstract Submission Deadline:           20.01.2023
Oral contribution confirmation:         30.01.2023

Active learning (AL) algorithms, such as Bayesan optimisation (BO), are revolutionising experiment design, efficient traversals of complex spaces, hyperparameter optimization, high throughput screening and automated laboratories discoveries.

The strength of AL techniques is that the machine learning model selects the data to include into the dataset via acquisition strategies. The requested data points can then be evaluated via computation or experiment, and included into the model iteratively, until the optimal solution converges. The resulting compact, maximally informative datasets make AL particularly suitable for applications where data is scarce or data acquisition expensive. In this way, AL has helped accelerate materials discovery away from big-data and free of human bias.

The event will bring together young researchers interested to explore the field, and recognised leaders in material science, chemistry, physics, and computer science. In doing so, it will offer the participants both a pedagogical (first part of the event) and an advanced perspective (second part of the event) the state of the art and beyond in active learning for materials science.