Title: Machine learning potentials for complex aqueous systems made simple Show Chinese title

Title: 用于复杂水系统的机器学习势能的简化方法

Abstract: Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex aqueous systems such as solid-liquid interfaces. Here, we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally-optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterwards be applied in exhaustive simulations to provide reliable answers for the scientific question at hand. We showcase this methodology on a diverse set of aqueous systems with increasing degrees of complexity. The systems chosen here comprise bulk water with different ions in solution, water on a titanium dioxide surface, as well as water confined in nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detail with an automated validation protocol that includes structural and dynamical properties and the precision of the force prediction of the models. Finally, we demonstrate the capabilities of our approach for the description of water on the rutile titanium dioxide (110) surface to analyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicated but accurate extension of simulation time and length scales for complex systems. Show Chinese abstract

Abstract: 基于对势能面的精确和高效表示的模拟技术，对于理解复杂的水体系（如固-液界面）是迫切需要的。 在这里，我们提出一种机器学习框架，能够高效地开发和验证复杂水体系的模型。 而不是试图提供一个全局最优的机器学习势能，我们提出了一种简单且用户友好的过程，以开发适用于特定热力学状态点的模型。 在初始的第一性原理模拟之后，通过数据驱动的主动学习协议，以最小的人工努力构建机器学习势能。 这些模型之后可以用于全面的模拟，以提供针对当前科学问题的可靠答案。 我们在这个多样化的一系列水体系上展示了该方法，这些体系具有不断增加的复杂度。 这里选择的体系包括含有不同离子的体相水、在二氧化钛表面的水，以及在纳米管中和二硫化钼片层之间的受限水。 强调我们的方法相对于底层第一性原理参考的准确性，这些模型通过包含结构和动力学性质以及模型力预测精度的自动化验证协议进行了详细评估。 最后，我们展示了我们的方法在描述锐钛矿二氧化钛(110)表面上水的能力，以分析该表面上水的结构和迁移性。 这样的机器学习模型为复杂系统提供了直接且简便但准确的模拟时间和长度尺度的扩展。