Interatomic potential parameterization using particle swarm optimization: Case study of glassy silica

被引:9
|
作者
Christensen, Rasmus [1 ]
Sorensen, Soren S. [1 ]
Liu, Han [2 ]
Li, Kevin [2 ]
Bauchy, Mathieu [2 ]
Smedskjaer, Morten M. [1 ]
机构
[1] Aalborg Univ, Dept Chem & Biosci, Aalborg, Denmark
[2] Univ Calif Los Angeles, Dept Civil & Environm Engn, Los Angeles, CA 90095 USA
来源
JOURNAL OF CHEMICAL PHYSICS | 2021年 / 154卷 / 13期
基金
美国国家科学基金会;
关键词
EMPIRICAL FORCEFIELDS; MOLECULAR-DYNAMICS; SIO2;
D O I
10.1063/5.0041183
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Classical molecular dynamics simulations of glassy materials rely on the availability of accurate yet computationally efficient interatomic force fields. The parameterization of new potentials remains challenging due to the non-convex nature of the accompanying optimization problem, which renders the traditional optimization methods inefficient or subject to bias. In this study, we present a new parameterization method based on particle swarm optimization (PSO), which is a stochastic population-based optimization method. Using glassy silica as a case study, we introduce two interatomic potentials using PSO, which are parameterized so as to match structural features obtained from ab initio simulations and experimental neutron diffraction data. We find that the PSO algorithm is highly efficient at searching for and identifying viable potential parameters that reproduce the structural features used as the target in the parameterization. The presented approach is very general and can be easily applied to other interatomic potential parameterization schemes.
引用
收藏
页数:12
相关论文
共 50 条
  • [31] Traffic Light Signal Parameters Optimization Using Particle Swarm Optimization Case Study of Ooe Toroku Road Network Optimization
    Wijaya, I. Gede Pasek Suta
    Uchimura, Keiichi
    Koutaki, Gou
    2015 INTERNATIONAL SEMINAR ON INTELLIGENT TECHNOLOGY AND ITS APPLICATIONS (ISITIA), 2015, : 11 - 16
  • [32] Oil Field Optimization Using Particle Swarm Optimization
    Gaikwad, Ganesh
    Ahire, Prashant
    2019 5TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION, CONTROL AND AUTOMATION (ICCUBEA), 2019,
  • [33] Construction Schedule Optimization Using Particle Swarm Optimization
    Xin, Fangxu
    Xin, Zhanhong
    ICPOM2008: PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE OF PRODUCTION AND OPERATION MANAGEMENT, VOLUMES 1-3, 2008, : 1200 - 1202
  • [34] Construction Schedule Optimization Using Particle Swarm Optimization
    Fang Xu
    Xin Zhanhong
    LOGISTICS RESEARCH AND PRACTICE IN CHINA, 2008, : 664 - 668
  • [35] Optimization of Network Reconfiguration by using Particle Swarm Optimization
    Reddy, A. V. Sudhakara
    Reddy, M. Damodar
    PROCEEDINGS OF THE FIRST IEEE INTERNATIONAL CONFERENCE ON POWER ELECTRONICS, INTELLIGENT CONTROL AND ENERGY SYSTEMS (ICPEICES 2016), 2016,
  • [36] Optimization of modular structures using Particle Swarm Optimization
    Duran, Orlando
    Perez, Luis
    Batocchio, Antonio
    EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (03) : 3507 - 3515
  • [37] Turning Parameters Optimization using Particle Swarm Optimization
    Marko, Hrelja
    Simon, Klancnik
    Tomaz, Irgolic
    Matej, Paulic
    Joze, Balic
    Miran, Brezocnik
    24TH DAAAM INTERNATIONAL SYMPOSIUM ON INTELLIGENT MANUFACTURING AND AUTOMATION, 2013, 2014, 69 : 670 - 677
  • [38] Construction Schedule Optimization Using Particle Swarm Optimization
    Zhao, Hongbo
    Ru, Zhongliang
    2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-31, 2008, : 7840 - 7843
  • [39] Stability analysis of particle swarm optimization using swarm activity
    Su, Shou-Bao
    Cao, Xi-Bin
    Kong, Min
    Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2010, 27 (10): : 1411 - 1417
  • [40] An Adaptive Approach to Swarm Surveillance using Particle Swarm Optimization
    Srivastava, Roopak
    Budhraja, Akshit
    Pradhan, Pyari Mohan
    2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), 2016, : 3780 - 3783