论文标题
无原子间无数据驱动的分子动力学
Interatomic-Potential-Free, Data-Driven Molecular Dynamics
论文作者
论文摘要
我们提出了一个数据驱动的（DD）范式，该范式使能够直接从采样的力场数据中直接执行分子动力学计算，例如从从头开始计算中获得的，从而避免了完全由原子质跨性潜能对数据进行建模的常规步骤。 DD求解器所需的数据由局部原子构构和相应的原子力组成，因此，基本上是基本的，即对任何特定模型都不是。所得的DD求解器，包括完全显式的DD-Verlet算法，可证明是收敛的，并且相对于所选测试用例的数据表现出强大的收敛性。我们提出了在C60 Buckminsterfullerenes中应用的示例，该示例展示了DD分子动力学范式的可行性，范围和范围。
We present a Data-Driven (DD) paradigm that enables molecular dynamics calculations to be performed directly from sampled force-field data such as obtained, e.g., from ab initio calculations, thereby eschewing the conventional step of modeling the data by empirical interatomic potentials entirely. The data required by the DD solvers consists of local atomic configurations and corresponding atomic forces and is, therefore, fundamental, i.e., it is not beholden to any particular model. The resulting DD solvers, including a fully explicit DD-Verlet algorithm, are provably convergent and exhibit robust convergence with respect to the data in selected test cases. We present an example of application to C60 buckminsterfullerenes that showcases the feasibility, range and scope of the DD molecular dynamics paradigm.
