Molina-Taborda Ana, Cossio Pilar, Lopez-Acevedo Olga, Gabrié Marylou
Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, 050010 Medellin, Colombia.
Grupo de Física Atómica y Molecular, Instituto de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Antioquia UdeA, 050010 Medellin, Colombia.
J Chem Theory Comput. 2024 Oct 22;20(20):8833-8843. doi: 10.1021/acs.jctc.4c00506. Epub 2024 Oct 6.
Extracting consistent statistics between relevant free energy minima of a molecular system is essential for physics, chemistry, and biology. Molecular dynamics (MD) simulations can aid in this task but are computationally expensive, especially for systems that require quantum accuracy. To overcome this challenge, we developed an approach combining enhanced sampling with deep generative models and active learning of a machine learning potential (MLP). We introduce an adaptive Markov chain Monte Carlo framework that enables the training of one normalizing flow (NF) and one MLP per state, achieving rapid convergence toward the Boltzmann distribution. Leveraging the trained NF and MLP models, we compute thermodynamic observables such as free energy differences and optical spectra. We apply this method to study the isomerization of an ultrasmall silver nanocluster belonging to a set of systems with diverse applications in the fields of medicine and catalysis.
提取分子系统相关自由能极小值之间的一致统计数据对物理学、化学和生物学至关重要。分子动力学（MD）模拟有助于完成这项任务，但计算成本高昂，特别是对于需要量子精度的系统。为了克服这一挑战，我们开发了一种将增强采样与深度生成模型以及机器学习势（MLP）的主动学习相结合的方法。我们引入了一个自适应马尔可夫链蒙特卡罗框架，该框架能够针对每个状态训练一个归一化流（NF）和一个MLP，实现朝着玻尔兹曼分布的快速收敛。利用训练好的NF和MLP模型，我们计算诸如自由能差和光谱等热力学可观测量。我们将此方法应用于研究一种超小银纳米团簇的异构化，该纳米团簇属于在医学和催化领域有多种应用的一组系统。
