A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A 2 B+ B 3+ X 6

X Wei, Y Zhang, X Liu, J Peng, S Li, R Che… - Journal of Materials …, 2023 - pubs.rsc.org
The heat of formation and band gap are important properties of halide double perovskites,
which can dictate the range of application. However, traditional laborious experiments and …

CORROSION PREDICTION OFMAGNESIUM IMPLANT USING MULTISCALE MODELING BASED ON MACHINE LEARNING ALGORITHMS

S Mondal, R Samanta, S Shit, A Biswas… - International Journal …, 2024 - dl.begellhouse.com
Significant thoughtful research is really necessary to improve the patient outcomes and
reduce the social and financial burdens associated with implant failure. The primary focus of …

Center-environment feature model for machine learning study of spinel oxides based on first-principles computations

Y Li, B Xiao, Y Tang, F Liu, X Wang… - The Journal of Physical …, 2020 - ACS Publications
Spinel oxides have attracted extensive attention due to their unique physical, chemical,
optical, and electronic properties with their applications in lithium batteries, photocatalysts …

Feature-Assisted Machine Learning for Predicting Band Gaps of Binary Semiconductors

S Huo, S Zhang, Q Wu, X Zhang - Nanomaterials, 2024 - mdpi.com
The band gap is a key parameter in semiconductor materials that is essential for advancing
optoelectronic device development. Accurately predicting band gaps of materials at low cost …

ThermoEPred-EL: Robust bandgap predictions of chalcogenides with diamond-like structure via feature cross-based stacked ensemble learning

X Wang, Y Xu, J Yang, J Ni, W Zhang, W Zhu - Computational Materials …, 2019 - Elsevier
Implementation on rapid and accurate bandgap prediction has great practical implications
for a range of applications. While quantum mechanical computations are enormously …

[PDF][PDF] 机器学习在热电材料领域中的应用

盛晔, 宁金妍, 杨炯 - 硅酸盐学报, 2023 - researching.cn
热电材料是环境友好型能源转换材料, 涉及的体系十分多样. 其性能优化是一个多参数协调的
复杂问题, 一直是研究者们关注的热点. 虽然热电的计算模拟方法和实验方法发展迅速 …

Accurate prediction of dielectric properties and bandgaps in materials with a machine learning approach

Y Hu, M Wu, M Yuan, Y Wen, P Ren, S Ye, F Liu… - Applied Physics …, 2024 - pubs.aip.org
The conventional approach to exploring suitable dielectrics for future logic and memory
devices relies on first-principle calculations, which are expensive and time-consuming. In …

[HTML][HTML] Using Machine Learning Techniques to Discover Novel Thermoelectric Materials

E Yildirim, ÖC Yelgel - New Materials and Devices for …, 2023 - intechopen.com
Thermoelectric materials can be utilized to build devices that convert waste heat to power or
vice versa. In the literature, the best-known thermoelectrics, however, are based on rare …

尖晶石氧化物能量和结构的第一性原理计算和机器学习.

李一航, 肖斌, 唐宇超, 刘馥… - Journal of Shanghai …, 2021 - search.ebscohost.com
The formal spinel oxides AB2O4 can have 5 329 configurations by substituting A and B with
73 elements. The first-principles method was applied to calculate the formation energies and …

Property Prediction of Medical Magnesium Alloy based on Machine Learning

N Li, S Zhao, Z Zhang - 2021 IEEE 6th International …, 2021 - ieeexplore.ieee.org
In the research on the performance of magnesium alloy materials as medical implant
materials, which is generally measured by plenty of mechanical experiments and corrosion …