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Han, Mengjiao

Publications and source records attributed to Han, Mengjiao.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Accelerated Depth Computation for Surface Boxplots with Deep Learning

Functional depth is a well-known technique used to derive descriptive statistics (e.g., median, quartiles, and outliers) for 1D data. Surface boxplots extend this concept to ensembles of images, helping scientists and users identify representative and outlier images. However, the computational time for surface boxplots increases cubically with the number of ensemble members, making it impractical for integration into visualization tools. In this paper, we propose a deep-learning solution for efficient depth prediction and computation of surface boxplots for time-varying ensemble data. Our deep learning framework accurately predicts member depths in a surface boxplot, achieving average speedups of 6X on a CPU and 15X on a GPU for the 2D Red Sea dataset with 50 ensemble members compared to the traditional depth computation algorithm. Our approach achieves at least a 99% level of rank preservation, with order flipping occurring only at pairs with extremely similar depth values that pose no statistical differences. This local flipping does not significantly impact the overall depth order of the ensemble members.

Han, Mengjiao↗
DOE OSTI2022

Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles

Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of en-semble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.

Han, Mengjiao↗
DOE OSTI2021

Preferential hole defect formation in monolayer W Se 2 by electron-beam irradiation

Monolayer transition-metal dichalcogenides (TMDCs) have been extensively studied due to their wide range of physical properties and applications. It has been demonstrated that the electron beam in a transmission electron microscope (TEM) or scanning TEM (STEM) generates chalcogen vacancies that agglomerate into dispersed linelike or holelike defects. Here we employ a STEM electron beam and demonstrate that, in WSe 2 , beam-induced chalcogen vacancies initially form certain dispersed multivacancy structures, as seen in TMDCs in prior work. However, with suitable control of the STEM focused electron beam, these multivacancies gradually evolve into a dense network of ten-, 12-, 14-, and 16-member ring round holes, whereas the same process leads predominantly to chalcogen-vacancy line defects in other trigonal-prismatic TMDCs. Density functional theory calculations find that the underlying atomic-scale processes lead preferentially to defect structures that lower the total energy so that we are able to track the formation of the observed multivacancy complexes, which then lead to the formation of dense large round holes in WSe 2 . The same processes in WS 2 , however, are energetically unfavorable, while linear multivacancy defects are preferred, as observed. The demonstrated control of the formation of unique high-density round holes in WSe 2 has potential for applications such as atomic and molecular sieving.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Covalent 2D Cr 2 Te 3 ferromagnet

To broaden the scope of van der Waals 2D magnets, we report the synthesis and magnetism of covalent 2D magnetic Cr 2 Te 3 with a thickness down to one-unit-cell. The 2D Cr 2 Te 3 crystals exhibit robust ferromagnetism with a Curie temperature of 180 K, a large perpendicular anisotropy of 7 × 10 5 J m -3 , and a high coercivity of ~4.6 kG at 20 K. First principles calculations further show a transition from canted to collinear ferromagnetism, a transition from perpendicular to in-plane anisotropy, and emergent half-metallic behavior in atomically-thin Cr 2 Te 3 , suggesting its potential application for injecting carriers with high spin polarization into spintronic devices.

2D magnets↗
DOE OSTI2019

Realization of BaZrS 3 chalcogenide perovskite thin films for optoelectronics

BaZrS 3 is a prototypical chalcogenide perovskite, an emerging class of unconventional semiconductor. Recent results on powder samples reveal that it is a material with a direct band gap of 1.7–1.8 eV, a very strong light-matter interaction, and a high chemical stability. Due to the lack of quality thin films, however, many fundamental properties of chalcogenide perovskites remain unknown, hindering their applications in optoelectronics. In this work, we report the fabrication of BaZrS 3 thin films, by sulfurization of oxide films deposited by pulsed laser deposition. We show that these films are n-type with carrier densities in the range of 10 19 -10 20 cm -3 . Depending on the processing temperature, the Hall mobility ranges from 2.1 to 13.7 cm 2 /Vs. The absorption coefficient is > 10 5 cm -1 at photon energy >1.97 eV. Temperature dependent conductivity measurements suggest shallow donor levels. By assuring that BaZrS 3 is a promising candidate, these results potentially unleash the family of chalcogenide perovskites for optoelectronics such as photodetectors, photovoltaics, and light emitting diodes.

36 MATERIALS SCIENCE↗