Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design
Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design
复制标题
DOI：
10.1063/1.5023804
复制
发表时间：
2018-08-21
影响因子：
4.4
通讯作者：
Ferguson, Andrew L.
中科院分区：
文献类型：
--
作者：
Chen, Wei;Tan, Aik Rui;Ferguson, Andrew L.
自联想神经网络（“自动编码器”）提供了一种强大的非线性降维技术，以从分子模拟轨迹中挖掘数据驱动的集体变量。该技术为非线性集体变量提供了显式和可微的表达式，使其非常适合与增强的采样技术集成，以加速对构型空间的探索。在这项工作中，我们描述了一些简化的神经网络架构，以改善和推广的过程中交错集体变量发现和增强采样。我们采用循环网络节点来适应集体变量的周期性，分层网络架构来对集体变量进行排序，以及广义编码器-解码器架构来支持网络训练的定制误差函数，以结合先验知识。我们展示了我们的方法，在盲目的集体变量发现和增强采样的构型自由能景观的丙氨酸二肽和色氨酸笼使用开源插件开发的OpenMM分子模拟包。由AIP出版社出版。
Auto-associative neural networks ("autoencoders") present a powerful nonlinear dimensionality reduction technique to mine data-driven collective variables from molecular simulation trajectories. This technique furnishes explicit and differentiable expressions for the nonlinear collective variables, making it ideally suited for integration with enhanced sampling techniques for accelerated exploration of configurational space. In this work, we describe a number of sophistications of the neural network architectures to improve and generalize the process of interleaved collective variable discovery and enhanced sampling. We employ circular network nodes to accommodate periodicities in the collective variables, hierarchical network architectures to rank-order the collective variables, and generalized encoder-decoder architectures to support bespoke error functions for network training to incorporate prior knowledge. We demonstrate our approach in blind collective variable discovery and enhanced sampling of the configurational free energy landscapes of alanine dipeptide and Trp-cage using an open-source plugin developed for the OpenMM molecular simulation package. Published by AIP Publishing.