Engineering topics

Wong, Chun Yin

Publications and source records attributed to Wong, Chun Yin.

3 records

DOE OSTI2022

AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy

Over the past several decades, electron and scanning probe microscopes have become critical components of condensed matter physics, materials science and chemistry research. At the same time, the infrastructure for establishing a connection between microscopy observations and materials behaviour over a broader parameter space is lacking. In this work, we introduce AtomAI, an open-source software package bridging instrument-specific Python libraries, deep learning and simulation tools into a single ecosystem. AtomAI allows direct applications of deep neural networks for atomic and mesoscopic image segmentation converting image and spectroscopy data into class-based local descriptors for downstream tasks such as statistical and graph analysis. For atomically resolved imaging data, the output is types and positions of atomic species, with an option for subsequent refinement. AtomAI further allows the implementation of a broad range of image and spectrum analysis functions, including invariant variational autoencoders for disentangling structural factors of variation and im2spec type of encoder–decoder models for mapping structure–property relationships. Finally, our framework allows seamless connection to the first principles modelling with a Python interface on the inferred atomic positions.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Understanding effects of chemical complexity on helium bubble formation in Ni-based concentrated solid solution alloys based on elemental segregation measurements

Here, helium bubble formation and swelling were systematically studied in Ni-based concentrated solid solution alloys containing different numbers and types of elements. Our microscopy analysis showed that although increasing the alloy chemical complexity helps suppress bubble formation in general, there is no monotonic relationship between the bubble growth rate and the number of alloying elements. Certain elements (e.g., Fe and Pd) are more effective in suppressing bubble growth than others (e.g., Cr and Mn). Atom probe tomography was applied to accurately measure elemental segregation around bubbles, revealing unique effects of certain alloying elements on vacancy migration towards bubbles. More specifically, the high vacancy mobility via Cr sites leads to a large vacancy flux and an increased bubble size, while the high degree of atomic size mismatch introduced by Pd helps deflect vacancy flow away from bubbles and decrease the amount of swelling. The effects identified in this study provide new strategies to design concentrated solid solutions with superior resistance to swelling.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Disentangling Ferroelectric Wall Dynamics and Identification of Pinning Mechanisms via Deep Learning

Field-induced domain-wall dynamics in ferroelectric materials underpins multiple applications ranging from actuators to information technology devices and necessitates a quantitative description of the associated mechanisms including giant electromechanical couplings, controlled nonlinearities, or low coercive voltages. While the advances in dynamic piezoresponse force microscopy measurements over the last two decades have rendered visualization of polarization dynamics relatively straightforward, the associated insights into the local mechanisms have been elusive. This work explores the domain dynamics in model polycrystalline materials using a workflow combining deep-learning-based segmentation of the domain structures with nonlinear dimensionality reduction using multilayer rotationally invariant autoencoders (rVAE). The former allows unambiguous identification and classification of the ferroelectric and ferroelastic domain walls. Here, the rVAE discovers the latent representations of the domain wall geometries and their dynamics, consequently providing insight into the intrinsic mechanisms of polarization switching, that can further be compared to simple physical models. The rVAE disentangles the factors affecting the pinning efficiency of ferroelectric walls, offering insights into the correlation of ferroelastic wall distribution and ferroelectric wall pinning.

36 MATERIALS SCIENCE↗