Characterization and efficient Monte Carlo sampling of disordered microphases

被引:2
|
作者
Zheng, Mingyuan [1 ]
Charbonneau, Patrick [1 ,2 ]
机构
[1] Duke Univ, Dept Chem, Durham, NC 27708 USA
[2] Duke Univ, Dept Phys, Durham, NC 27708 USA
来源
JOURNAL OF CHEMICAL PHYSICS | 2021年 / 154卷 / 24期
基金
美国国家科学基金会;
关键词
BLOCK-COPOLYMER LITHOGRAPHY; COLLOIDAL SYSTEMS; VIRIAL-COEFFICIENTS; CLUSTER FORMATION; PHASE-BEHAVIOR; EQUILIBRIUM; RELAXATION; DYNAMICS; PATTERNS; FLUIDS;
D O I
10.1063/5.0052114
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
The disordered microphases that develop in the high-temperature phase of systems with competing short-range attractive and long-range repulsive (SALR) interactions result in a rich array of distinct morphologies, such as cluster, void cluster, and percolated (gel-like) fluids. These different structural regimes exhibit complex relaxation dynamics with marked heterogeneity and slowdown. The overall relationship between these structures and configurational sampling schemes, however, remains largely uncharted. Here, the disordered microphases of a schematic SALR model are thoroughly characterized, and structural relaxation functions adapted to each regime are devised. The sampling efficiency of various advanced Monte Carlo sampling schemes-Virtual-Move (VMMC), Aggregation-Volume-Bias (AVBMC), and Event-Chain (ECMC)-is then assessed. A combination of VMMC and AVBMC is found to be computationally most efficient for cluster fluids and ECMC to become relatively more efficient as density increases. These results offer a complete description of the equilibrium disordered phase of a simple microphase former as well as dynamical benchmarks for other sampling schemes. Published under an exclusive license by AIP Publishing.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] LMProt:: An efficient algorithm for Monte Carlo sampling of protein conformational space
    da Silva, RA
    Degrève, L
    Caliri, A
    BIOPHYSICAL JOURNAL, 2004, 87 (03) : 1567 - 1577
  • [22] Automated Parameter Blocking for Efficient Markov Chain Monte Carlo Sampling
    Turek, Daniel
    de Valpine, Perry
    Paciorek, Christopher J.
    Anderson-Bergman, Clifford
    BAYESIAN ANALYSIS, 2017, 12 (02): : 465 - 490
  • [23] Efficient sampling using macrocanonical Monte Carlo and density of states mapping
    Ding, Jiewei
    Su, Jiahao
    Tang, Ho-Kin
    Yu, Wing Chi
    PHYSICAL REVIEW RESEARCH, 2024, 6 (04):
  • [24] Efficient Quasi-Monte Carlo Sampling for Quantum Random Walks
    Atanassov, E.
    Durchova, M.
    APPLICATION OF MATHEMATICS IN TECHNICAL AND NATURAL SCIENCES (AMITANS 2020), 2020, 2302
  • [25] Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification
    Shields, Michael D.
    Teferra, Kirubel
    Hapij, Adam
    Daddazio, Raymond P.
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2015, 142 : 310 - 325
  • [26] Hamiltonian Monte Carlo for efficient Gaussian sampling: long and random steps
    Apers, Simon
    Gribling, Sander
    Szilagyi, Daniel
    JOURNAL OF MACHINE LEARNING RESEARCH, 2024, 25
  • [27] Efficient low temperature Monte Carlo sampling using quantum annealing
    Sandt, Roland
    Spatschek, Robert
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [28] Efficient low temperature Monte Carlo sampling using quantum annealing
    Roland Sandt
    Robert Spatschek
    Scientific Reports, 13
  • [29] Efficient Monte Carlo sampling by direct flattening of free energy barriers
    Besold, G
    Risbo, J
    Mouritsen, OG
    COMPUTATIONAL MATERIALS SCIENCE, 1999, 15 (03) : 311 - 340
  • [30] Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation
    Smidl, Vaclav
    Hofman, Radek
    TECHNOMETRICS, 2014, 56 (04) : 514 - 527