Atomistic Simulations, Italian Institute of Technology, 16156 Genova, Italy.
Department of Materials Science, Università di Milano-Bicocca, 20126 Milano, Italy.
J Chem Phys. 2023 Jul 7;159(1). doi: 10.1063/5.0156343.
Identifying a reduced set of collective variables is critical for understanding atomistic simulations and accelerating them through enhanced sampling techniques. Recently, several methods have been proposed to learn these variables directly from atomistic data. Depending on the type of data available, the learning process can be framed as dimensionality reduction, classification of metastable states, or identification of slow modes. Here, we present mlcolvar, a Python library that simplifies the construction of these variables and their use in the context of enhanced sampling through a contributed interface to the PLUMED software. The library is organized modularly to facilitate the extension and cross-contamination of these methodologies. In this spirit, we developed a general multi-task learning framework in which multiple objective functions and data from different simulations can be combined to improve the collective variables. The library's versatility is demonstrated through simple examples that are prototypical of realistic scenarios.
确定一组简化的集体变量对于理解原子模拟并通过增强采样技术加速它们至关重要。最近，已经提出了几种从原子数据中直接学习这些变量的方法。根据可用数据的类型，学习过程可以被框定为降维、亚稳状态的分类或慢模式的识别。在这里，我们介绍了 mlcolvar，这是一个 Python 库，通过为 PLUMED 软件提供一个贡献接口，简化了这些变量的构建，并在增强采样的上下文中使用它们。该库采用模块化组织，便于扩展和交叉污染这些方法。本着这种精神，我们开发了一个通用的多任务学习框架，其中可以组合多个目标函数和来自不同模拟的数据，以提高集体变量的性能。该库的多功能性通过简单的示例得到了证明，这些示例是现实场景的典型代表。
