Mechanical energy harvesting based on the piezoelectric materials: recent advances and future perspectives

X Pan, Y Wu, Y Wang, G Zhou, H Cai - Chemical Engineering Journal, 2024 - Elsevier
Against the backdrop of growing global energy demand, the drive to obtain sustainable
energy from ambient mechanical sources has become increasingly significant. In recent …

A systematic review of recent advances in piezocatalysis–Synergetic heterojunctions for organic pollutants removal, immobilization, and scope of machine learning …

SS Jeyabalan, OS Ekande, B Mainali… - Chemical Engineering …, 2024 - Elsevier
Piezocatalysis is an emerging advanced oxidation process (AOP) that converts mechanical
energy to chemical form producing reactive oxygen species (ROS) for mineralizing organic …

Toward ultra-high strength high entropy alloys via feature engineering

Y Zhang, C Wen, P Dang, T Lookman, D Xue… - Journal of Materials …, 2024 - Elsevier
Abstract Machine learning assisted design of materials is so far based on features selected
by considering the accuracy of model predictions, and those features do not necessarily …

Machine Learning Accelerated Discovery of Functional MXenes with Giant Piezoelectric Coefficients

X Li, J Qiu, H Cui, X Chen, J Yu… - ACS applied materials & …, 2024 - ACS Publications
Efficient and rapid screening of target materials in a vast material space remains a
significant challenge in the field of materials science. In this study, first-principles …

An interpretable machine learning strategy for pursuing high piezoelectric coefficients in (K0.5Na0.5)NbO3-based ceramics

B Ma, X Wu, C Zhao, C Lin, M Gao, B Sa… - npj Computational …, 2023 - nature.com
Perovskite-type lead-free piezoelectric ceramics allow access to illustrious piezoelectric
coefficients (d 33) through intricate composition design and experimental modulation …

Autoencoded chemical feature interaction machine learning method boosting performance of piezoelectric catalytic process

W Zhuang, X Zhao, Y Zhang, Q Luo, L Zhang, M Sui - Nano Energy, 2024 - Elsevier
Piezoelectric catalytic process can reduce energy consumption in water treatment
processes. However, the design of high-performance piezoelectric materials and the search …

[HTML][HTML] Predicting the stacking fault energy in FCC high-entropy alloys based on data-driven machine learning

X Zhang, R Dong, Q Guo, H Hou, Y Zhao - Journal of Materials Research …, 2023 - Elsevier
The properties of high-entropy alloys (HEAs) depend primarily on the composition and
content of elements. However, getting the optimal composition of alloying elements through …

Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

J Gao, J Zhong, G Liu, S Zhang, J Zhang… - … and Technology of …, 2023 - Taylor & Francis
Scandium is the best alloying element to improve the mechanical properties of industrial Al-
Si-Mg casting alloys. Most literature reports devote to exploring/designing optimal Sc …

Machine learning aided accelerated prediction and experimental validation of functional properties of K1-xNaxNbO3-based piezoelectric ceramics

S Sapkal, B Kandasubramanian, P Dixit… - Materials Today Energy, 2023 - Elsevier
The functional properties of piezoelectric ceramics are vital to design materials for energy
harvesting applications. In the present study, to accelerate the design process with …

Machine learning-assisted design of Al2O3–SiO2 porous ceramics based on few-shot datasets

Z Sun, N Hu, L Ke, Y Lv, Y Liu, Y Bai, Z Ou, J Li - Ceramics International, 2023 - Elsevier
A machine learning model was proposed to accelerate the preparation of Al 2 O 3–SiO 2
porous ceramics (ASPC) based on few-shot datasets. Phosphate tailings and bauxite were …