6 papers · 1 filter
Oblate Spheroid Excitation Theory: A Unified, Lattice-Free Foundation for Plastic Deformation from Which Dislocations Emerge as Collective Excitations
Albert Linda, K. A. Padmanabhan
Dislocation theory has underpinned crystal plasticity for a century, yet its lattice-dependent definition cannot describe plastic flow in grain boundaries, glasses, ceramics, or na…
Physics Aware Representation Learning on Electronic Charge Density for Materials Property Prediction
Kammampati Sai Kumar, Albert Linda, Shubham Kumar Maurya +1
The fundamental quantity governing the mechanical and thermodynamic properties of a crystalline solid is its electronic charge density. Yet, its direct use for the rapid prediction…
Multiscale Modeling of Abnormal Grain Growth: Role of Solute Segregation and Grain Boundary Character
Albert Linda, Rajdip Mukherjee, Somnath Bhowmick
Abnormal grain growth (AGG) influences the properties of polycrystalline materials; however, the underlying mechanisms, particularly the role of solute segregation at the grain bou…
Deep Learning Assisted Denoising of Experimental Micrographs
Owais Ahmad, Albert Linda, Saumya Ranjan Jha +1
Microstructure imaging is crucial in materials science, but experimental images often introduce noise that obscures critical structural details. This study presents a novel deep le…
Effect of Cr Segregation on Grain Growth in Nanocrystalline α-Fe Alloy: A Multiscale Modelling Approach
Sandip Guin, Albert Linda, Yu-Chieh Lo +2
We present a multiscale modelling framework that integrates density functional theory (DFT) with a phase-field model (PFM) to explore the intricate dynamics of grain growth in nano…
Accelerating the prediction of stacking fault energy by combining ab initio calculations and machine learning
Albert Linda, Md. Faiz Akhtar, Shaswat Pathak +1
Stacking fault energies (SFEs) are vital parameters for understanding the deformation mechanisms in metals and alloys, with prior knowledge of SFEs from ab initio calculations bein…