Theory-guided machine learning to predict configurational energies of high distortion alloy systems

• 主要作者: Huang, Xufa
• 其他作者: Kedar Hippalgaonkar
• 格式: Final Year Project
• 語言: English
• 出版: Nanyang Technological University 2023
• 主題: Engineering::Materials; Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
• 在線閱讀: https://hdl.handle.net/10356/165985

Cluster expansion (CE) is a popular surrogate model to density functional theory (DFT) for modeling the stability of alloy systems through configurational energies. However, since CE is a lattice-based model, its accuracy is often poor when applied to high-entropy alloys (HEAs) with significan...