Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables
Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables
复制标题
DOI：
10.1021/acs.jctc.3c00923
复制
发表时间：
2023-12-27
影响因子：
5.5
通讯作者：
Ferguson，Andrew L.
中科院分区：
文献类型：
--
作者：
Herringer，Nicholas S. M.;Dasetty，Siva;Ferguson，Andrew L.
由于存在高自由能势垒，分子自由能景观的典型崎岖性质可能会阻碍对化学相关相空间的有效采样。增强的采样技术可以通过加速沿沿着特定集合变量（CV）的采样来改进相空间探索。存在许多技术用于CV的数据驱动发现，参数化系统的重要大规模运动。CV发现的一个挑战是学习CV对分子系统的对称性、经常是刚性平移、刚性旋转和相同粒子的排列重新标记不变性。其中，置换不变性已被证明是一个持续的挑战，在挫败的数据驱动的发现多分子CV的自组装粒子和溶剂化系统的溶剂包容CV的系统。在这项工作中，我们将置换不变向量（PIV）特征化与自动编码神经网络相结合，以学习对平移，旋转和置换不变的非线性CV，并执行CV发现和增强采样的交错轮次，以迭代扩展配置相空间的采样，并获得收敛的CV和自由能景观。我们展示了增强采样的置换不变网络（PINES）方法在应用程序中的自组装的13原子氩簇，在水中的NaCl离子对的缔合/解离，和疏水崩溃的C45 H92 n-pentatetracontane聚合物链。我们将该方法作为PLUMED 2增强型采样库中的一个新模块免费提供。
The typically rugged nature of molecular free-energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free-energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for the data-driven discovery of CVs parametrizing the important large-scale motions of the system. A challenge to CV discovery is learning CVs invariant to the symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance has proved a persistent challenge in frustrating the data-driven discovery of multimolecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate permutation invariant vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand the sampling of configurational phase space and obtain converged CVs and free-energy landscapes. We demonstrate the permutationally invariant network for enhanced sampling (PINES) approach in applications to the self-assembly of a 13-atom argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C45H92n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries.