Jun Zhang

TL;DR: An adaptive particle swarm optimization that features better search efficiency than classical particle Swarm optimization (PSO) is presented and can perform a global search over the entire search space with faster convergence speed.

TL;DR: The phase diagram for an FeSe monolayer grown on a SrTiO3 substrate is reported, by tuning the charge carrier concentration over a wide range through an extensive annealing procedure, and strong indications of superconductivity are observed with a transition temperature of 65±5 K.

TL;DR: Compared with other PSO algorithms, the comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness.

TL;DR: In this article, a hybrid algorithm combining particle swarm optimization (PSO) algorithm with back-propagation (BP) algorithm, also referred to as PSO-BP algorithm, is proposed to train the weights of feedforward neural network (FNN), the hybrid algorithm can make use of not only strong global searching ability of the PSOA, but also strong local searching capability of the BP algorithm.

TL;DR: In this article, a phase diagram in the single-layer FeSe films grown on SrTiO3 substrate by an annealing procedure to tune the charge carrier concentration over a wide range is presented.