Electronic Structure of Amorphous and Glassy Chalcogenides: State-of-the-Art Simulations, Machine Learning Force Fields, and AI-Driven Advances

Submission Deadline: 20 April 2027 View: 113 Submit to Special Issue

Guest Editor(s)

Dr. Ali Kachmar

Email: a.kachmar@squ.edu.om

Affiliation: Physics, Sultan Qaboos University, Muscat, Oman

Homepage:

Research Interests: first principles simulations of chalcogenides glasses and liquids, their structure and dynamical properties, and Raman/Infrared properties

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Summary

Title: Electronic Structure of Amorphous and Glassy Chalcogenides: State-of-the-Art Simulations, Machine Learning Force Fields, and AI-Driven Advances

Chalcogenide glasses are technologically important disordered semiconductors, underpinning phase-change memory, infrared photonics, sensing, and optoelectronic devices, yet their functional behavior is governed by a complex, disordered electronic structure that remains challenging to model accurately. This Special Issue aims to bring together recent advances in the theoretical and computational study of the electronic structure of amorphous and glassy chalcogenides, with particular emphasis on state-of-the-art simulation techniques, including first-principles/DFT and ab initio molecular dynamics, as well as the growing role of machine learning and artificial intelligence algorithms in generating interatomic potentials and force fields, accelerating structure generation, and predicting structure-property relationships. Contributions addressing new methodologies, benchmarking of ML/AI potentials against experiment and ab initio references, and applications to real chalcogenide systems are welcome.

Suggested themes:
· Ab initio and DFT studies of the electronic structure of chalcogenide glasses
· Machine learning interatomic potentials and force-field generation for amorphous chalcogenides
· AI-driven generation and prediction of amorphous/glassy network structures
· Classical and ab initio molecular dynamics simulations of glass formation and relaxation
· Structure-property correlations: bonding, defects, and disorder in chalcogenide glasses
· Simulation-guided design for phase-change memory and photonic applications
· Benchmarking and validation of ML/AI potentials against experimental and first-principles data

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Keywords

chalcogenide glasses, amorphous semiconductors, electronic structure, density functional theory, machine learning interatomic potentials, molecular dynamics simulation, ab initio simulations, artificial intelligence, force field generation, structural disorder

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