KLA-Tencor, One Technology Drive, Milpitas, California 95035, USA.
Department of Chemistry, University of California, Berkeley, California 94720, USA.
Phys Rev Lett. 2018 Jul 6;121(1):010601. doi: 10.1103/PhysRevLett.121.010601.
Collective variable (CV) or order parameter based enhanced sampling algorithms have achieved great success due to their ability to efficiently explore the rough potential energy landscapes of complex systems. However, the degeneracy of microscopic configurations, originating from the orthogonal space perpendicular to the CVs, is likely to shadow "hidden barriers" and greatly reduce the efficiency of CV-based sampling. Here we demonstrate that systematic machine learning CV, through enhanced sampling, can iteratively lift such degeneracies on the fly. We introduce an active learning scheme that consists of a parametric CV learner based on deep neural network and a CV-based enhanced sampler. Our active enhanced sampling algorithm is capable of identifying the least informative regions based on a historical sample, forming a positive feedback loop between the CV learner and sampler. This approach is able to globally preserve kinetic characteristics by incrementally enhancing both sample completeness and CV quality.
基于整体变量 (CV) 或序参量的增强采样算法由于其能够有效地探索复杂系统的粗糙势能景观而取得了巨大的成功。然而，源自 CV 正交空间的微观构型的简并性很可能会遮蔽“隐藏的障碍”，并大大降低基于 CV 的采样效率。在这里，我们证明了系统的机器学习 CV 通过增强采样可以在飞行中迭代地消除这种简并性。我们引入了一种主动学习方案，它由一个基于深度神经网络的参数 CV 学习器和一个基于 CV 的增强采样器组成。我们的主动增强采样算法能够根据历史样本识别出信息量最少的区域，在 CV 学习器和采样器之间形成正反馈循环。这种方法能够通过增量地提高样本完整性和 CV 质量来全局地保持动力学特性。
