Nonlinear machine learning in simulations of soft and biological materials
Nonlinear machine learning in simulations of soft and biological materials
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DOI：
10.1080/08927022.2017.1400164
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发表时间：
2018-09
影响因子：
2.1
通讯作者：
Jiang Wang;Andrew L. Ferguson
中科院分区：
文献类型：
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作者：
Jiang Wang;Andrew L. Ferguson
从分子模拟计算的软材料和生物材料的自由能景观的可解释参数，需要有能够区分系统亚稳态和它们之间的势垒的好的集体变量(CV)的可用性。如果这些CV与控制长期动态演化的缓慢集体模式一致，那么它们也提供了很好的坐标，在其中执行增强采样，以跨越高自由能障碍，有效地探索和恢复景观。非线性流形学习技术通过识别和提取隐藏在高维坐标空间中的低维流形，提供了一种从分子模拟轨迹系统地提取此类CV的方法。我们综述了数据驱动的CV发现和使用非线性流形学习的增强采样的最新进展，描述了这些技术的数学和理论基础，并提供了分子折叠和胶体自组装的说明性例子。最后，我们对这一快速发展领域的未来进展进行展望和展望。
Abstract Interpretable parameterisations of free energy landscapes for soft and biological materials calculated from molecular simulation require the availability of ‘good’ collective variables (CVs) capable of discriminating the metastable states of the system and the barriers between them. If these CVs are coincident with the slow collective modes governing the long-time dynamical evolution, then they also furnish good coordinates in which to perform enhanced sampling to surmount high free energy barriers and efficiently explore and recover the landscape. Non-linear manifold learning techniques provide a means to systematically extract such CVs from molecular simulation trajectories by identifying and extracting low-dimensional manifolds lying latent within the high-dimensional coordinate space. We survey recent advances in data-driven CV discovery and enhanced sampling using non-linear manifold learning, describe the mathematical and theoretical underpinnings of these techniques, and present illustrative examples to molecular folding and colloidal self-assembly. We close with our outlook and perspective on future advances in this rapidly evolving field.