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Coarse graining techniques play an essential role in accelerating molecularsimulations of systems with large length and time scales. Theoreticallygrounded bottom-up models are appealing due to their thermodynamic consistencywith the underlying all-atom models. In this direction, machine learningapproaches hold great promise to fitting complex many-body data. However,training models may require collection of large amounts of expensive data.Moreover, quantifying trained model accuracy is challenging, especially incases of non-trivial free energy configurations, where training data may besparse. We demonstrate a path towards uncertainty-aware models of coarsegrained free energy surfaces. Specifically, we show that principled Bayesianmodel uncertainty allows for efficient data collection through an on-the-flyactive learning framework and open the possibility of adaptive transfer ofmodels across different chemical systems. Uncertainties also characterizemodels' accuracy of free energy predictions, even when training is performedonly on forces. This work helps pave the way towards efficient autonomoustraining of reliable and uncertainty aware many-body machine learned coarsegrain models.

Uncertainty Driven Active Learning of Coarse Grained Free Energy Models