2024/01/15(Mon) 10:00 -11:00 七樓研討室 7F, P7A Seminar Room
Title
Strong-Coupling Phases of Electron-Hole Bilayers at Commensurate Densities
Speaker
Mr. David D. Dai (Massachusetts Institute of Technology)Abstract
Jan 15 (Monday)
10am-11am
David Dai (MIT)
Title: Strong-Coupling Phases of Electron-Hole Bilayers at
Commensurate Densities
Abstract: In this talk, we introduce density-imbalanced electron-hole
bilayers at a commensurate 2:1 density ratio as a platform for
realizing novel phases of electrons, excitons, and trions. Through the
independently tunable carrier densities and interlayer spacing,
competition between kinetic energy, intralayer repulsion, and
interlayer attraction yields a rich phase diagram of strong-coupling
phases. By a combination of theoretical analysis and numerical
calculation, we find a variety of phases in different parameter
regions, including quantum crystals of electrons, excitons, and
trions. We also propose an “excitonic supersolid” phase featuring an
exciton superfluid permeating through an electron Wigner crystal. The
material realization and experimental signatures of our phases are
discussed in the context of recent experiments on semiconductor
transition metal dichalcogenide bilayers. We also discuss recent
computational work applying variational neural network wavefunctions
to mapping out the phase diagram of strongly correlated systems such
as the electron-hole bilayer.
11am-11:30am
Hao-Jen You (Academia Sinica)
Predicting Phonon Transport in Disordered Alloys from a Highly
Accurate Machine Learning Interatomic Potential
The Ⅱ2Ⅳ family of materials, such as Mg2Si, Mg2Sn, Sr2Si, and Sr2Ge,
among others, are highly regarded as promising high-performance
thermoelectric materials. In our previous research, we calculated the
maximum figure of merit ZT for the promising II-IV family
thermoelectric compounds Sr2Si and Sr2Ge, yielding values of 1.15 and
1.44 at 900 K through first-principles calculations. To improve
thermoelectric performance, the common practice involves alloying to
reduce lattice thermal conductivity and enhance the Seebeck
coefficient. Nevertheless, determining the optimal alloy ratios
through first-principles calculations can be quite challenging because
disordered effects require a large supercell in the computations.
Here, we introduce a highly accurate machine learning interatomic
potential (MLIP) for Sr2Si1-xGex disordered alloys. This MLIP is
created through a machine learning technique trained on
first-principles density functional theory (DFT) data, and it attains
accuracy levels comparable to those achieved with DFT. This approach
empowers us to carry out efficient molecular dynamics simulations for
entire alloy concentration 0 ≤ x ≤ 1 in Sr2Si1-xGex and make accurate
thermal property predictions. Our work provides a solution to explore
compositions that offer the most potential for high-performance
thermoelectric disordered alloys while assessing the contributions of
phonon modes to phonon transport.
CV
Language
演講語言 (Language): in English
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