Amp: A modular approach to machine learning in atomistic simulations

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018) Abstract Electronic structure calculations, such as those employing Kohn–Sham density functional theory or ab initio wavefunction theories, have allowed for atomistic-level understandings of a wide variety of phenomena and properties of matter at small scales. However, the computational cost of electronic structure methods drastically increases with length and time scales, which makes these methods difficult for long time-scale molecular dynamics simulations or large-sized systems. Machine-learning te... Title of program: Amp Catalogue Id: AFAK_v1_0 Nature of problem Atomic interactions within many-body systems typically have complicated functional forms, difficult to represent in simple pre-decided closed-forms. Versions of this program held in the CPC repository in Mendeley Data AFAK_v1_0; Amp; 10.1016/j.cpc.2016.05.010

本程序源自贝尔法斯特女王大学（Queen's University Belfast）馆藏的CPC程序库（1969-2018）。
摘要：电子结构计算，例如采用Kohn-Sham密度泛函理论（Kohn–Sham density functional theory）或从头算波函数理论的计算，可帮助我们在原子尺度上理解多种微观物质现象与性质。然而，电子结构方法的计算成本随空间与时间尺度的扩大而急剧升高，使得此类方法难以应用于长时标分子动力学模拟或大型体系。机器学习技术……
程序名称：Amp
目录编号：AFAK_v1_0
问题本质：多体系统内的原子相互作用通常具有复杂的函数形式，难以通过预先设定的简单闭合形式进行表征。
本程序在Mendeley数据平台的CPC库中的版本信息：AFAK_v1_0；Amp；DOI: 10.1016/j.cpc.2016.05.010