Bonati Luigi, Rizzi Valerio, Parrinello Michele
Department of Physics, ETH Zurich, 8092 Zurich, Switzerland.
Institute of Computational Sciences, Università della Svizzera italiana, via Buffi 13, 6900 Lugano, Switzerland.
J Phys Chem Lett. 2020 Apr 16;11(8):2998-3004. doi: 10.1021/acs.jpclett.0c00535. Epub 2020 Apr 2.
Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from information limited to the metastable states. We characterize these states by a large set of descriptors and employ neural networks to compress this information in a lower-dimensional space, using Fisher's linear discriminant as an objective function to maximize the discriminative power of the network. We test this method on alanine dipeptide, using the nonlinearly separable data set composed by atomic distances. We then study an intermolecular aldol reaction characterized by a concerted mechanism. The resulting variables are able to promote sampling by drawing nonlinear paths in the physical space connecting the fluctuations between metastable basins. Lastly, we interpret the behavior of the neural network by studying its relation to the physical variables. Through the identification of its most relevant features, we are able to gain chemical insight into the process.
设计一组合适的集体变量对于几种增强采样方法的成功至关重要。在这里，我们专注于如何从限于亚稳态的信息中获取此类变量。我们用一大组描述符来表征这些状态，并利用神经网络在低维空间中压缩这些信息，以Fisher线性判别作为目标函数来最大化网络的判别能力。我们在丙氨酸二肽上测试了这种方法，使用由原子距离组成的非线性可分数据集。然后，我们研究了一个以协同机制为特征的分子间羟醛反应。由此产生的变量能够通过在连接亚稳盆地之间波动的物理空间中绘制非线性路径来促进采样。最后，我们通过研究神经网络与物理变量的关系来解释其行为。通过识别其最相关的特征，我们能够深入了解该过程的化学本质。
