Changxin Wang

Tongji University — China

Materials Science · Physical Sciences

13h-index2.1kcitations25works5.12yr avg
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Research Topics

Machine Learning in Materials Science(6), Advanced Photocatalysis Techniques(5), MXene and MAX Phase Materials(5), Ferroelectric and Piezoelectric Materials(4), High-Temperature Coating Behaviors(4)

Publications25 total

Optimized Design of the Overall Scheme for Eddy Current In-Line Inspection Tool for Pipeline Cracks
2025
Machine learning assisted screening of non-metal doped MXenes catalysts for hydrogen evolution reaction
International Journal of Hydrogen Energy·2025· 10 cited
MVSRF: Point cloud semantic segmentation and optimization method for granular construction objects
Applied Intelligence·2025
First-Principles and Machine Learning-Driven Design of Efficient Electrocatalysts for Lithium-Sulfur Batteries: Metal Atom-Doped Tin/Ti3c2br Heterostructures
SSRN Electronic Journal·2025Open Access
Real-time Semantic Segmentation and Grid-space Optimization for Sparse Point Cloud
2024
Machine-Learning-Assisted Compositional Design of Refractory High-Entropy Alloys with Optimal Strength and Ductility
Engineering·2024· 25 citedOpen Access
Compositional design of compounds with elements not in training data using supervised learning
Journal of Materiomics·2024· 1 citedOpen Access
Indexing high-noise electron backscatter diffraction patterns using convolutional neural network and transfer learning
Computational Materials Science·2023· 3 cited
Developing and optimizing novel Cr<sup>3+</sup>-activated inorganic NIR phosphors by combining triple-objective optimization and crystal field engineering
Inorganic Chemistry Frontiers·2023· 19 cited
Regulation mechanisms of electron-delocalized single transition metal-doped <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>Mo</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi mathvariant="normal">C</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> MXene hydrogen evolution reaction catalysts
Physical Review Materials·2023· 6 cited
Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics
Journal of Advanced Ceramics·2023· 17 citedOpen Access
Machine Learning-Based Quantitative Analysis of Metal Ductile Fracture Surface
SSRN Electronic Journal·2023Open Access
Indexing High-Noise Electron Backscatter Diffraction Patterns Using Convolutional Neural Network and Transfer Learning
SSRN Electronic Journal·2023Open Access
Rapid Discovery of Efficient Long-Wavelength Emission Garnet:Cr NIR Phosphors via Multi-Objective Optimization
ACS Applied Materials & Interfaces·2022· 51 cited
Machine learning assisted predictions of multi-component phase diagrams and fine boundary information
Acta Materialia·2022· 42 cited
Multiobjective Machine Learning-Assisted Discovery of a Novel Cyan–Green Garnet: Ce Phosphors with Excellent Thermal Stability
ACS Applied Materials & Interfaces·2022· 65 cited
Symbolic regression in materials science via dimension-synchronous-computation
Journal of Material Science and Technology·2022· 35 cited
Machine Learning Assisted Predictions of Multi-Component Phase Diagrams and Fine Boundary Information
SSRN Electronic Journal·2022· 3 citedOpen Access
A descriptor for the design of 2D MXene hydrogen evolution reaction electrocatalysts
Journal of Materials Chemistry A·2022· 66 cited
Ultrasensitive Frequency Shifting of Dielectric Mie Resonance near Metallic Substrate
Research·2022· 4 citedOpen Access
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Frequent Co-authors

Yanjing Su(22), Turab Lookman(12), Yang Bai(9), Dezhen Xue(8), Cheng Wen(6), Chuanbao Liu(6), Ping Qian(5), Jingjin He(5), Lijie Qiao(5), Xiao‐Xu Wang(4), Jiangli Cao(4), Stoichko Antonov(3), Junjie Li(3), Lipeng Jiang(3), Mei Yang(3), Yan Zhang(2), Yan Zhang(2), Yan Zhang(2), Xue Jiang(2), Pei Liu(2)