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Yani Chen

Publications and source records attributed to Yani Chen.

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Electron-induced non-monotonic pressure dependence of the lattice thermal conductivity of {\theta}-TaN

Recent theoretical and experimental research suggests that $\theta$-TaN is a semimetal with high thermal conductivity ($\kappa$), primarily due to the contribution of phonons ($\kappa_\texttt{ph}$). By using first-principles calculations, we show a non-monotonic pressure dependence of the $\kappa$ of $\theta$-TaN. $\kappa_\texttt{ph}$ first increases until it reaches a maximum at around 60~GPa, and then decreases. This anomalous behaviour is a consequence of the competing pressure responses of phonon-phonon and phonon-electron interactions, in contrast to the known materials BAs and BP, where the non-monotonic pressure dependence is caused by the interplay between different phonon-phonon scattering channels. Although TaN has phonon dispersion features similar to BAs at ambient pressure, its response to pressure is different and an overall stiffening of the phonon branches takes place. Consequently, the relevant phonon-phonon scattering weakens as pressure increases. However, the increased electronic density of states near the Fermi level, and specifically the emergence of additional pockets of the Fermi surface at the high-symmetry L point in the Brillouin zone, leads to a substantial increase in phonon-electron scattering at high pressures, driving a decrease in $\kappa_{\mathrm{ph}}$. At intermediate pressures ($\sim$~20$-$70~GPa), the $\kappa$ of TaN surpasses that of BAs. Our work provides deeper insight into phonon transport in semimetals and metals where phonon-electron scattering is relevant.

cond-mat.mtrl-sci

A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models

In this paper, we explore the capabilities of a number of deep neural network models in generating whole-brain 3T-like MR images from clinical 1.5T MRIs. The models include a fully convolutional network (FCN) method and three state-of-the-art super-resolution solutions, ESPCN [26], SRGAN [17] and PRSR [7]. The FCN solution, U-Convert-Net, carries out mapping of 1.5T-to-3T slices through a U-Net-like architecture, with 3D neighborhood information integrated through a multi-view ensemble. The pros and cons of the models, as well the associated evaluation metrics, are measured with experiments and discussed in depth. To the best of our knowledge, this study is the first work to evaluate multiple deep learning solutions for whole-brain MRI conversion, as well as the first attempt to utilize FCN/U-Net-like structure for this purpose.

eess.IV