Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events
Автор: Cambridge Materials
Загружено: 2023-01-11
Просмотров: 848
Lennard-Jones Centre discussion group seminar by Dr Lixin Sun from Microsoft Research Cambridge.
This talk presents a data-driven machine learning algorithm that is devised to learn collective variables with a multitask neural network. Additionally, new ways of labeling atomic configurations and approximating committor function are proposed. The resulting ML-learned collective variable is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.
The seminar was held on 3rd October 2022.
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