Uncertainty Driven Active Learning of Coarse Grained Free Energy Models

TL;DR

This paper introduces an uncertainty-aware active learning framework for developing coarse-grained free energy models in molecular simulations, enabling efficient data collection and transferability across systems.

Contribution

It presents a Bayesian approach to quantify model uncertainty, facilitating active learning and adaptive transfer in coarse-grained free energy modeling.

Findings

Uncertainty quantification improves free energy prediction accuracy.

Active learning reduces data requirements for training.

Models can transfer across different chemical systems effectively.

Abstract

Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the underlying all-atom models. In this direction, machine learning approaches 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 in cases of non-trivial free energy configurations, where training data may be sparse. We demonstrate a path towards uncertainty-aware models of coarse grained free energy surfaces. Specifically, we show that principled Bayesian model uncertainty allows for efficient data collection through an on-the-fly active learning framework and open the possibility of adaptive transfer of models across different chemical systems. Uncertainties also characterize models' accuracy of free energy predictions, even when training is performed only on forces. This work helps pave the way towards efficient autonomous training of reliable and uncertainty aware many-body machine learned coarse grain models.