Learning reaction coordinates via cross-entropy minimization: Application to alanine dipeptide
Yusuke Mori, Kei-ichi Okazaki, Toshifumi Mori, Kang Kim, Nobuyuki Matubayasi
备注：
8 pages, 5 figures, 1 table for main text, 5 pages for supplementary materials, to appear in J. Chem. Phys. for "Classical Molecular Dynamics (MD) Simulations: Codes, Algorithms, Force Fields, and Applications"
We propose a cross-entropy minimization method for finding the reaction coordinate from a large number of collective variables in complex molecular systems. This method is an extension of the likelihood maximization approach describing the committor function with a sigmoid. By design, the reaction coordinate as a function of various collective variables is optimized such that the distribution of the committor $p_\mathrm{B}^*$ values generated from molecular dynamics simulations can be described in a sigmoidal manner. We also introduce the $L_2$-norm regularization used in the machine learning field to prevent overfitting when the number of considered collective variables is large. The current method is applied to study the isomerization of alanine dipeptide in vacuum, where 45 dihedral angles are used as candidate variables. The regularization parameter is determined by cross-validation using training and test datasets. It is demonstrated that the optimal reaction coordinate involves important dihedral angles, which are consistent with the previously reported results. Furthermore, the points with $p_\mathrm{B}^*\sim 0.5$ clearly indicate a separatrix distinguishing reactant and product states on the potential of mean force using the extracted dihedral angles.
Exploring the conformational dynamics of alanine dipeptide in solution subjected to an external electric field: A nonequilibrium molecular dynamics simulation
Reinforced dynamics for enhanced sampling in large atomic and molecular systems
Princeton University（普林斯顿大学）；Institute of Applied Physics and Computational Mathematics（应用物理与计算数学研究所）；CAEP Software Center for High Performance Numerical Simulation（中国工程物理研究院高性能数值模拟软件中心）；Beijing Institute of Big Data Research（北京大数据研究院）
