.png){kind=icon}
返回
Force-based gradient descent method for ab initio atomic structure relaxation
.png){kind=icon}
OA
AI
DOI：10.1103/PhysRevB.106.104101.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Force-based algorithms for ab initio atomic structure relaxation, such as conjugate gradient methods, usually get stuck in the line minimization processes along search directions, where expensive ab initio calculations are triggered frequently to test trial positions before locating the next iterate. We present a force-based gradient de-scent method, WANBB, that circumvents the deficiency. At each iteration, WANBB enters the line minimization process with a trial step size capturing the local curvature of the energy surface. The exit is controlled by a nonrestrictive criterion that tends to accept early trials. These two ingredients streamline the line minimization process in WANBB. The numerical simulations on nearly 80 systems with good universality demonstrate the considerable compression of WANBB on the cost for the unaccepted trials compared with conjugate gradient methods. We also observe across the board significant and universal speedups as well as the superior robustness of WANBB over several widely used methods. The latter point is theoretically established. The implementation of WANBB is pretty simple, in that no a priori physical knowledge is required and only three parameters are present without tuning.
Keyword:
TOTAL-ENERGY CALCULATIONS
LINE SEARCH TECHNIQUE
OPTIMIZATION
CONVERGENCE
期刊
IF:
3.7
论文数:
15.4W
被引数:
41.0W
机构
引用论文
Optimum Unit Sizing of Stand-Alone PV-WT-Battery Hybrid System Components Using Jaya使用Jaya的独立pv-wt-电池混合系统组件的最佳单位尺寸
PREAI
分享收藏分享收藏Commentary: The Materials Project: A materials genome approach to accelerating materials innovation评论: 材料项目: 加速材料创新的材料基因组方法.png)
APL MATERIALSOAAI
APL MATERIALSIF4.5
OAAI分享收藏
Crystal structure prediction using ab initio evolutionary techniques:: Principles and applications使用从头算进化技术预测晶体结构: 原理和应用OAAI
OAAI分享收藏
查看更多内容