Random Batch Algorithms for Quantum Monte Carlo Simulations

Jin, Shi; Li, Xiantao

doi:10.4208/cicp.oa-2020-0168

Cited by 10 publications

(1 citation statement)

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“…This random mini batch idea was the key component of the so-called stochastic gradient descent (SGD) [37,3] in machine learning. Due to the simplicity and scalability, it already has a variety of applications in solving the Poisson-Nernst-Planck, Poisson-Boltzmann and Fokker-Planck-Landau equations [29,5], efficient sampling [31], molecular simulation [24,32], and quantum Monte-Carlo method [26]. Readers can refer to the review article [20].…”

Section: Introductionmentioning

confidence: 99%

School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China

Zhuang

Huang

et al. 2019

Mathematical Biosciences and Engineering

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The crystallization kinetics and melting behavior of nylon 10,10 in neat nylon 10,10 and in nylon 10,10 -montmorillonite (MMT) nanocomposites were systematically investigated by differential scanning calorimetry. The crystallization kinetics results show that the addition of MMT facilitated the crystallization of nylon 10,10 as a heterophase nucleating agent; however, when the content of MMT was high, the physical hindrance of MMT layers to the motion of nylon 10,10 chains retarded the crystallization of nylon 10,10, which was also confirmed by polarized optical microscopy. However, both nylon 10,10 and nylon 10,10 -MMT nanocomposites exhibited multiple melting be-havior under isothermal and nonisothermal crystallization conditions. The temperature of the lower melting peak (peak I) was independent of MMT content and almost remained constant; however, the temperature of the highest melting peak (peak II) decreased with increasing MMT content due to the physical hindrance of MMT layers to the motion of nylon 10,10 chains.

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