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CEST masters student Manuel Kuchelmeister was awarded one of the two best poster prizes for his presentation "at Physics Days 2022, organized virtually by Aalto University.

Bayesian optimization (BO) is a sample-efficient method for the exploration of large search spaces. In this work, BO is used to find stable configurations on material energy landscapes. Finding such structures is a challenge, due to high-dimensional search spaces and costly quantum mechanical calculations. Kuchelmeister approached this by constructing a multi-fidelity machine learning model. By using a transfer learning approach, it was possible to use less accurate but inexpensive calculations, to accelerate the exploration phases of BO.

The approach reduced the computational cost of a conformer search problem by 70%, serving as a first benchmark for the great potential that multi-fidelity learning can have to accelerate expensive structure-search problems.