An AI framework for time series microstructure prediction from processing parameters
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Science & Technology - Other Topics↗
Engineering topics
Mao, Yuwei
Publications and source records attributed to Mao, Yuwei.
An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems
Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties. This paper proposes an end-to-end artificial intelligence (AI)-driven microstructure optimization framework for elastic properties of materials. In this work, the microstructure is represented by the Orientation Distribution Function (ODF) that determines the volume densities of crystallographic orientations. The framework was evaluated on two crystal systems, cubic and hexagonal, for Titanium (Ti) in Joint Automated Repository for Various Integrated Simulations (JARVIS) database and is expected to be widely applicable for materials with multiple crystal systems. The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time.
36 MATERIALS SCIENCE↗
MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design
Abstract There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.
36 MATERIALS SCIENCE↗
High-Voltage Charging-Induced Strain, Heterogeneity, and Micro-Cracks in Secondary Particles of a Nickel-Rich Layered Cathode Material
Nickel-rich layered materials LiNi 1-x-y Mn x Co y O 2 are promising candidates for high-energy-density lithium-ion battery cathodes. Unfortunately, they suffer from capacity fading upon cycling, especially with high-voltage charging. It is critical to have a mechanistic understanding of such fade. Synchrotron-based techniques (including scattering, spectroscopy, and microcopy) and finite element analysis are utilized to understand the LiNi 0.6 Mn 0.2 Co 0.2 O 2 material from structural, chemical, morphological, and mechanical points of view. The lattice structural changes are shown to be relatively reversible during cycling, even when 4.9 V charging is applied. However, local disorder and strain are induced by high-voltage charging. Nano-resolution 3D transmission X-ray microscopy data analyzed by machine learning methodology reveal that high-voltage charging induced significant oxidation state inhomogeneities in the cycled particles. Regions at the surface have a rock salt–type structure with lower oxidation state and build up the impedance, while regions with higher oxidization state are scattered in the bulk and are likely deactivated during cycling. In addition, the development of micro-cracks is highly dependent on the pristine state morphology and cycling conditions. Hollow particles seem to be more robust against stress-induced cracks than the solid ones, suggesting that morphology engineering can be effective in mitigating the crack problem in these materials.
25 ENERGY STORAGE↗
Automatic projection image registration for nanoscale X-ray tomographic reconstruction
Novel developments in X-ray sources, optics and detectors have significantly advanced the capability of X-ray microscopy at the nanoscale. Depending on the imaging modality and the photon energy, state-of-the-art X-ray microscopes are routinely operated at a spatial resolution of tens of nanometres for hard X-rays or ~ 10 nm for soft X-rays. The improvement in spatial resolution, however, has led to challenges in the tomographic reconstruction due to the fact that the imperfections of the mechanical system become clearly detectable in the projection images.Without proper registration of the projection images, a severe point spread function will be introduced into the tomographic reconstructions, causing the reduction of the three-dimensional (3D) spatial resolution as well as the enhancement of image artifacts. In this work, the development of a method that iteratively performs registration of the experimentally measured projection images to those that are numerically calculated by reprojecting the 3D matrix in the corresponding viewing angles is shown. Multiple algorithms are implemented to conduct the registration, which corrects the translational and/ or the rotational errors. A sequence that offers a superior performance is presented and discussed. Going beyond the visual assessment of the reconstruction results, the morphological quantification of a battery electrode particle that has gone through substantial cycling is investigated. The results show that the presented method has led to a better quality tomographic reconstruction, which, subsequently, promotes the fidelity in the quantification of the sample morphology.
42 ENGINEERING↗
Mesoscale Battery Science: The Behavior of Electrode Particles Caught on a Multispectral X-ray Camera
Here, functional materials and devices are usually morphologically complex and chemically heterogeneous. Their structures are often designed to be hierarchical because of the desired functionalities, which usually require many different components to work together in a coherent manner. The lithium ion battery, as an energy storage device, is a very typical example of this kind of structure. In a lithium ion battery, the cathode, anode, and separator are soaked in a liquid electrolyte, facilitating the back and forward shuttling of the lithium ions for energy storage and release. The desired performance of a lithium ion battery has many different aspects that need to be engineered and balanced depending on the targeted applications. In most cases, the cathode material has become the limiting factor for further improvements and, thus, has attracted intense attention from the research community.
25 ENERGY STORAGE↗