Interpretable structure-property correlation in X-ray diffraction patterns of HfZrO thin films via machine learning

Japanese Journal of Applied Physics, Journal Year: 2024, Volume and Issue: 63(4), P. 04SP44 - 04SP44

Published: Feb. 22, 2024

Abstract The X-ray diffraction (XRD) patterns of materials contain important and rich information in terms structure, strain state, grain size, etc. XRD can become a powerful fingerprint for material characterizations when it is combined with machine learning techniques. Attempts utilizing machine-learning-based methods mainly focus on phase identification mixture compounds. Herein, we applied method linking HfZrO thin films directly to their electronic properties experiments. In accordance conventional understanding, the model suggests that non-monoclinic (NM) phases HfO 2 ZrO are among main contributors higher relative permittivity lower leakage current. Furthermore, some minor interfacial like TiO x ZrN also proposed be even more our target properties. Our research demonstrates has potential reveal signals from sub-1 nm layers have long been considered undetectable thus ignored by human interpretation.

Language: Английский

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Machine learning in neutron scattering data analysis

Journal of Radiation Research and Applied Sciences, Journal Year: 2024, Volume and Issue: 17(2), P. 100870 - 100870

Published: Feb. 28, 2024

Neutron scattering is one of the state-of-the-art techniques for detecting structural and dynamic properties materials. The data analysis neutron an inverse process that extracts hidden features from correlates them with information about structure samples. With global popularity machine learning, its powerful automatic feature extraction capability was noticed by scientists. In recent years, integration learning methods has seen significant development. this paper, applications in common techniques, including diffraction, small angle scattering, reflectometry, imaging, were systematically reviewed. We classified research into different themes each technique. Building upon review, we discussed application paradigms current challenges associated analysis.

Language: Английский

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Optimization of Ultrasound-Assisted Extraction of Faba Bean Protein Isolate: Structural, Functional, and Thermal Properties. Part 1/2

Published: Jan. 1, 2024

Faba bean seeds have been traditionally used as a source of animal feed and minimally in food systems. However, it is an excellent high-quality proteins other bioactive compounds. Extraction using conventional extraction requires long processing time with lower yield protein purity. Here, optimized ultrasound-assisted parameters were adopted compared the process. Under optimum conditions Power (123 W), solute/solvent ratio (0.06) (1:15 g/mL), sonication (41 min), total volume (623 mL)), maximum 19.75% content 92.87% was obtained. Conventionally extracted found to 16.41 ± 0.02% 89. 88 0.40%. No significant changes on primary structure ultrasound obtained Bean Proteins Isolate (FBPI) observed from electrophoresis. Fourier-transform infrared spectroscopy analysis showed that able modify secondary FBPI. Ultrasound treatment resulted improvement water oil absorption capacity but adversely affected foaming capacity. Minor differences also thermal properties method. This shows produced FBPI under these could be useful for different industrial production functional ingredient.

Language: Английский

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Advances in Boron Nitride

Advances in chemical and materials engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 87 - 111

Published: July 18, 2024

This chapter discusses recent advances in boron nitride (BN), focusing on synthesis, characterization, and coating techniques. Various synthesis methods like chemical vapor deposition (CVD), sol-gel, ball milling are explored, emphasizing their roles tailoring BN materials for specific applications. Characterization techniques such as scanning electron microscopy (SEM), transmission (TEM), X-ray diffraction (XRD), spectroscopic utilized to elucidate BN's structural properties. Coating including physical (PVD), atomic layer (ALD), electrodeposition reviewed effectiveness depositing coatings with precise control over thickness uniformity. The also addresses hardness corrosion resistance. Future research directions outlined, novel process optimization enhance BN's.

Language: Английский

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Bayesian inference for peak feature extraction and prediction of material property in X-ray diffraction data

Science and Technology of Advanced Materials Methods, Journal Year: 2024, Volume and Issue: 4(1)

Published: Aug. 5, 2024

To advance the development of materials through data-driven scientific methods, appropriate methods for building machine learning (ML)-ready feature tables from measured and computed data must be established. In development, X-ray diffraction (XRD) is an effective technique analysing crystal structures other microstructural features that have information can explain material properties. Therefore, fully automated extraction peak XRD without bias analyst a significant challenge. This study aimed to establish efficient robust approach constructing follow ML standards (ML-ready) data. We challenge in situation where only function profile known priori, knowledge measurement or structure factor. utilized Bayesian estimation extract subsequently performed regression analysis with selection predict property. The proposed method focused on tops peaks within localized regions interest (ROIs) extracted quickly accurately. process facilitated rapid extracting major construction ML-ready table. then applied linear maximum energy product (BH)max, using as explanatory variable. outcomes yielded reasonable results. Thus, findings this indicated 004 height area were important predicting (BH)max.

Language: Английский

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