Chen Ming
Department of Chemistry, Purdue University, West Lafayette, IN 47907 USA.
Eur Phys J B. 2021;94(10):211. doi: 10.1140/epjb/s10051-021-00220-w. Epub 2021 Oct 20.
Collective variable-based enhanced sampling methods have been widely used to study thermodynamic properties of complex systems. Efficiency and accuracy of these enhanced sampling methods are affected by two factors: constructing appropriate collective variables for enhanced sampling and generating accurate free energy surfaces. Recently, many machine learning techniques have been developed to improve the quality of collective variables and the accuracy of free energy surfaces. Although machine learning has achieved great successes in improving enhanced sampling methods, there are still many challenges and open questions. In this perspective, we shall review recent developments on integrating machine learning techniques and collective variable-based enhanced sampling approaches. We also discuss challenges and future research directions including generating kinetic information, exploring high-dimensional free energy surfaces, and efficiently sampling all-atom configurations.
基于集体变量的增强采样方法已被广泛用于研究复杂系统的热力学性质。这些增强采样方法的效率和准确性受两个因素影响：为增强采样构建合适的集体变量以及生成准确的自由能面。最近，已开发出许多机器学习技术来提高集体变量的质量和自由能面的准确性。尽管机器学习在改进增强采样方法方面取得了巨大成功，但仍存在许多挑战和未解决的问题。从这个角度出发，我们将回顾机器学习技术与基于集体变量的增强采样方法相结合的最新进展。我们还将讨论挑战和未来的研究方向，包括生成动力学信息、探索高维自由能面以及有效地对全原子构型进行采样。
