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70 papers · 1 filter
Coordination-Sensitive Nanoscale Analysis of Defect-Driven Phase Transformation in Si-Doped (AlXGa1-X)2O3
Shaon Das, Jith Sarker, Christopher Chae +5
Defect-driven phase instability critically influences the structural reliability of ultrawide bandgap oxides, yet direct nanoscale metrics linking local chemistry to structural tra…
Geometry-Based Neural-Network Prediction of Electron Localization Function Topology in Dense Hydrogen
Xiaoyu Wang, Miriam Marqués, Sergio Gómez +3
We develop a machine-learning framework to predict the electron localization function (ELF) of pure, dense hydrogen directly from atomic geometry, bypassing explicit electronic-str…
Many-body description of two-dimensional van der Waals ferroelectric InSe
Denzel Ayala, Dimitar Pashov, Tong Zhou +3
Two-dimensional (2D) van der Waals ferroelectrics are recognized for enabling many applications, from memory and logic to neuromorphic computing, as well as transforming other mate…
Damage Prediction of Sintered α-SiC Using Thermo-mechanical Coupled Fracture Model
Jason Sun, Yu Chen, Joseph J. Marziale +3
A three-way coupled thermo-mechanical fracture model is presented to predict the damage of brittle ceramics, in particular α-SiC, over a wide range of temperatures (20-1400 C). Pre…
The effect of chemical vapor infiltration process parameters on flexural strength of porous α-SiC: A numerical model
Joseph J. Marziale, Jason Sun, Eric A. Walker +3
The flexural strength variability of α-SiC based ceramics at elevated temperatures creates the need for an Integrated Computational Materials Engineering (ICME) framework that rela…
A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks
Brandon Schoener, Yuting Hu, Pasit Wanlapha +6
Incorporating Machine Learning (ML) into material property prediction has become a crucial step in accelerating materials discovery. A key challenge is the severe lack of training…