LaCàN, Universitat Politècnica de Catalunya - BarcelonaTech, Campus Nord, 08034 Barcelona, Spain.
J Chem Phys. 2013 Dec 7;139(21):214101. doi: 10.1063/1.4830403.
Collective variables (CVs) are low-dimensional representations of the state of a complex system, which help us rationalize molecular conformations and sample free energy landscapes with molecular dynamics simulations. Given their importance, there is need for systematic methods that effectively identify CVs for complex systems. In recent years, nonlinear manifold learning has shown its ability to automatically characterize molecular collective behavior. Unfortunately, these methods fail to provide a differentiable function mapping high-dimensional configurations to their low-dimensional representation, as required in enhanced sampling methods. We introduce a methodology that, starting from an ensemble representative of molecular flexibility, builds smooth and nonlinear data-driven collective variables (SandCV) from the output of nonlinear manifold learning algorithms. We demonstrate the method with a standard benchmark molecule, alanine dipeptide, and show how it can be non-intrusively combined with off-the-shelf enhanced sampling methods, here the adaptive biasing force method. We illustrate how enhanced sampling simulations with SandCV can explore regions that were poorly sampled in the original molecular ensemble. We further explore the transferability of SandCV from a simpler system, alanine dipeptide in vacuum, to a more complex system, alanine dipeptide in explicit water.
集体变量（CVs）是复杂系统状态的低维表示，有助于我们通过分子动力学模拟合理化分子构象并采样自由能景观。鉴于它们的重要性，需要有系统的方法来有效地为复杂系统识别 CVs。近年来，非线性流形学习已经显示出其自动描述分子集体行为的能力。不幸的是，这些方法无法提供增强采样方法所需的将高维构象映射到其低维表示的可微函数。我们引入了一种方法，该方法从分子柔韧性的代表集合开始，从非线性流形学习算法的输出构建平滑和非线性数据驱动的集体变量（SandCV）。我们使用标准基准分子丙氨酸二肽演示了该方法，并展示了如何将其非侵入性地与现成的增强采样方法（此处为自适应偏置力方法）结合使用。我们说明了如何使用 SandCV 进行增强采样模拟可以探索原始分子集合中采样不佳的区域。我们进一步探索了 SandCV 从更简单的系统（真空中的丙氨酸二肽）到更复杂的系统（明水中的丙氨酸二肽）的可转移性。
