Uncertainty driven active learning of coarse grained free energy models
Uncertainty driven active learning of coarse grained free energy models
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DOI：
10.1038/s41524-023-01183-5
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发表时间：
2024-01-08
影响因子：
9.7
通讯作者：
Kozinsky，Boris
中科院分区：
文献类型：
--
作者：
Duschatko，Blake R.;Vandermause，Jonathan;Kozinsky，Boris
粗粒化技术在加速大长度、大时间尺度系统的分子模拟中起着至关重要的作用。基于理论的自下而上模型由于其与底层全原子模型的热力学一致性而具有吸引力。在这个方向上，机器学习方法在拟合复杂的多体数据方面具有很大的前景。然而，训练模型可能需要收集大量昂贵的数据。此外，量化训练模型的准确性具有挑战性，特别是在非平凡自由能配置的情况下，其中训练数据可能是稀疏的。我们展示了一条通往粗粒度自由能表面的不确定性感知模型的道路。具体来说，我们表明原则贝叶斯模型不确定性允许通过动态主动学习框架进行有效的数据收集，并打开了跨不同化学系统的模型自适应转移的可能性。即使只对力进行训练，不确定性也会影响模型对自由能预测的准确性。这项工作有助于为可靠和不确定性感知的多体机器学习粗粮模型的有效自主训练铺平道路。
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 opens 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.