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
  • [21] A study of particle swarm optimization particle trajectories
    van den Bergh, F
    Engelbrecht, AP
    INFORMATION SCIENCES, 2006, 176 (08) : 937 - 971
  • [22] Kinetic study of transesterification using particle swarm optimization method
    Kadi, M. A.
    Akkouche, N.
    Awad, S.
    Loubar, K.
    Tazerout, M.
    HELIYON, 2019, 5 (08)
  • [23] Using Particle Swarm Optimization Algorithm for Transformer Transient Study
    Rashtchi, Vahid
    Rahimpour, Ebrahim
    Mirzaei, Jaber
    INTERNATIONAL REVIEW OF ELECTRICAL ENGINEERING-IREE, 2011, 6 (03): : 1174 - 1180
  • [24] Inversion seismic refraction data using particle swarm optimization: a case study of Tabriz, Iran
    Rashed Poormirzaee
    Rasoul Hamidzadeh Moghadam
    Ahmad Zarean
    Arabian Journal of Geosciences, 2015, 8 : 5981 - 5989
  • [25] Inversion seismic refraction data using particle swarm optimization: a case study of Tabriz, Iran
    Poormirzaee, Rashed
    Moghadam, Rasoul Hamidzadeh
    Zarean, Ahmad
    ARABIAN JOURNAL OF GEOSCIENCES, 2015, 8 (08) : 5981 - 5989
  • [26] Optimized Convolutional Gabor Using Particle Swarm Optimization Case Study: Vehicle Classification Tasks
    Abdillah, Bariqi
    Jati, Grafika
    Alhamidi, Machmud R.
    Jatmiko, Wisnu
    2018 INTERNATIONAL SYMPOSIUM ON MICRO-NANOMECHATRONICS AND HUMAN SCIENCE (MHS), 2018,
  • [27] The estimation of the electricity energy demand using particle swarm optimization algorithm: A case study of Turkey
    Gulcu, Saban
    Kodaz, Halife
    8TH INTERNATIONAL CONFERENCE ON ADVANCES IN INFORMATION TECHNOLOGY, 2017, 111 : 64 - 70
  • [28] Identification of the reservoir using seismic inversion based on particle swarm optimization method: A case study
    Kant, Ravi
    Kumar, Brijesh
    Maurya, S. P.
    Verma, Nitin
    Singh, Ajay P.
    Hema, G.
    Singh, Raghav
    Singh, K. H.
    Sarkar, Piyush
    JOURNAL OF EARTH SYSTEM SCIENCE, 2024, 133 (04)
  • [29] Particle Swarm Optimization for Test Case Prioritization Using String Distance
    Khatibsyarbini, Muhammad
    Isa, Mohd Adham
    Jawawi, Dayang Norhayati Abang
    ADVANCED SCIENCE LETTERS, 2018, 24 (10) : 7221 - 7226
  • [30] Theory of particle swarm optimization: A survey of the power of the swarm's potential
    Bassimir, Bernd
    Rass, Alexander
    Schmitt, Manuel
    IT-INFORMATION TECHNOLOGY, 2019, 61 (04): : 169 - 176