arXiv subjects

Yihao Hu

Publications and source records attributed to Yihao Hu.

23 records · Page 2Linked to original sources

arXiv2023

Depolarization Induced III-V Triatomic Layers with Tristable Polarization States

The integration of ferroelectrics that exhibit high dielectric, piezoelectric, and thermal susceptibilities with the mainstream semiconductor industry will enable novel device types for widespread applications, and yet there are few silicon-compatible ferroelectrics suitable for device downscaling. We demonstrate with first-principles calculations that the enhanced depolarization field at the nanoscale can be utilized to soften unswitchable wurtzite III-V semiconductors, resulting in ultrathin two-dimensional (2D) sheets possessing reversible polarization states. A 2D sheet of AlSb consisting of three atomic planes is identified to host both ferroelectricity and antiferroelectricity, and the tristate switching is accompanied by a metal-semiconductor transition. The thermodynamics stability and potential synthesizability of the triatomic layer are corroborated with phonon spectrum calculations, ab initio molecular dynamics, and variable-composition evolutionary structure search. We propose a 2D AlSb-based homojunction field effect transistor that supports three distinct and nonvolatile resistance states. This new class of III-V semiconductor-derived 2D materials with dual ferroelectricity and antiferroelectricity opens up the possibility for nonvolatile multibit-based integrated nanoelectronics.

cond-mat.mtrl-sci↗

arXiv2022

Origin of ferroelectricity in magnesium doped zinc oxide

Recent experiments demonstrated robust ferroelectricity in Mg-doped ZnO (ZMO) of the wurtzite structure, hinting at a promising strategy to substantially expand the list of ferroelectrics by doping conventional piezoelectrics. We investigate the origin of ferroelectricity in ZMO with first-principles density functional theory (DFT). The general argument that the Mg alloying could soften the ionic potential energy surface of ZMO for polarization reversal is overly simplified. Our DFT calculations reveal that even at a high Mg concentration, the energy difference ($ΔU$) between the polar and nonpolar phases remains prohibitively large for ZMO systems when the strain is fixed to the polar phase. Interestingly, the magnitude of $ΔU$ becomes substantially smaller when the strain relaxation is allowed, approaching the value of typical perovskite ferroelectrics such as PbTiO$_3$ with increasing Mg doping concentrations. The enabled switchability of ZMO systems is attributed to a hexagonal phase of MgO that is much lower in energy than its wurtzite counterpart. Detailed orbital and bonding analysis supports that the intra-atomic $3d_{z^2}$-$4p_z$ orbital self-mixing of Zn plays an important role in stabilizing the polar wurtzite phase, the lack of which is responsible for the low-energy nonpolar hexagonal phase of MgO.

cond-mat.mtrl-sci↗

arXiv2022

Neural-PDE: A RNN based neural network for solving time dependent PDEs

Partial differential equations (PDEs) play a crucial role in studying a vast number of problems in science and engineering. Numerically solving nonlinear and/or high-dimensional PDEs is often a challenging task. Inspired by the traditional finite difference and finite elements methods and emerging advancements in machine learning, we propose a sequence deep learning framework called Neural-PDE, which allows to automatically learn governing rules of any time-dependent PDE system from existing data by using a bidirectional LSTM encoder, and predict the next n time steps data. One critical feature of our proposed framework is that the Neural-PDE is able to simultaneously learn and simulate the multiscale variables.We test the Neural-PDE by a range of examples from one-dimensional PDEs to a high-dimensional and nonlinear complex fluids model. The results show that the Neural-PDE is capable of learning the initial conditions, boundary conditions and differential operators without the knowledge of the specific form of a PDE system.In our experiments the Neural-PDE can efficiently extract the dynamics within 20 epochs training, and produces accurate predictions. Furthermore, unlike the traditional machine learning approaches in learning PDE such as CNN and MLP which require vast parameters for model precision, Neural-PDE shares parameters across all time steps, thus considerably reduces the computational complexity and leads to a fast learning algorithm.

math.NA↗

arXiv2021

A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales

This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series forecasting applied to environmental datasets. The framework was evaluated across multiple sensors and for three different oceanic variables: current speed, temperature, and dissolved oxygen. Network implementation proceeded in two directions that are nominally separated but connected as part of a natural environmental system -- across the spatial (between individual sensors) and temporal components of the sensor data. Data from four sensors sampling current speed, and eight measuring both temperature and dissolved oxygen evaluated the framework. Results were compared against RF and XGB baseline models that learned on the temporal signal of each sensor independently by extracting the date-time features together with the past history of data using sliding window matrix. Results demonstrated ability to accurately replicate complex signals and provide comparable performance to state-of-the-art benchmarks. Notably, the novel framework provided a simpler pre-processing and training pipeline that handles missing values via a simple masking layer. Enabling learning across the spatial and temporal directions, this paper addresses two fundamental challenges of ML applications to environmental science: 1) data sparsity and the challenges and costs of collecting measurements of environmental conditions such as ocean dynamics, and 2) environmental datasets are inherently connected in the spatial and temporal directions while classical ML approaches only consider one of these directions. Furthermore, sharing of parameters across all input steps makes SPATIAL a fast, scalable, and easily-parameterized forecasting framework.

stat.ML↗

arXiv2019

Collaborative Privacy for Web Applications

Real-time, online-editing web apps provide free and convenient services for collaboratively editing, sharing and storing files. The benefits of these web applications do not come for free: not only do service providers have full access to the users' files, but they also control access, transmission, and storage mechanisms for them. As a result, user data may be at risk of data mining, third-party interception, or even manipulation. To combat this, we propose a new system for helping to preserve the privacy of user data within collaborative environments. There are several distinct challenges in producing such a system, including developing an encryption mechanism that does not interfere with the back-end (and often proprietary) control mechanisms utilized by the service, and identifying transparent code hooks through which to obfuscate user data. Toward the first challenge, we develop a character-level encryption scheme that is more resilient to the types of attacks that plague classical substitution ciphers. For the second challenge, we design a browser extension that robustly demonstrates the feasibility of our approach, and show a concrete implementation for Google Chrome and the widely-used Google Docs platform. Our example tangibly demonstrates how several users with a shared key can collaboratively and transparently edit a Google Docs document without revealing the plaintext directly to Google.

cs.CR↗