A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables (1) smooth estimates of generalized forces in sparsely sampled regions, (2) force estimates in previously unexplored regions, and (3) continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. The method is also found to provide improvements over previous implementations of neural network assisted algorithms.

中文翻译：

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使用神经网络通过力偏置进行自适应增强采样

提出了一种机器学习辅助方法，用于对具有崎free自由能格局的系统进行分子模拟。该方法是通用的，可以与其他高级采样技术结合使用。在此处提出的特定实施方式中，在自适应偏压力方法的上下文中进行了说明，其中可以依靠自调节人工神经网络来生成连续的，估计的广义力，而不是依赖离散的力估计。通过这样做，所提出的方法解决了自适应偏置力和其他算法共有的几个缺点。具体而言，神经网络能够（1）对稀疏采样区域中的广义力进行平滑估计，（2）在先前未探索区域中的力估计，以及（3）对模拟产生偏差的连续力估计，与在离散网格的特定点处产生的偏差相反。通过三个不同的示例来说明该方法的实用性，选择这些示例以突出显示基础概念的广泛适用性。在所有这三种情况下，都发现了新方法，大大增强了潜在的传统自适应偏压力方法。还发现该方法相对于神经网络辅助算法的先前实现提供了改进。