Short range order and network connectivity in amorphous AsTe₃: a first principles, machine learning, and XRD study.

Area of Science:

• Materials Science
• Condensed Matter Physics
• Computational Chemistry

Background:

• Understanding the atomic structure of amorphous materials is crucial for predicting their properties.
• Arsenic telluride (AsTe3) is a chalcogenide glass with potential applications in electronics and photonics.

Purpose of the Study:

• To elucidate the atomic-scale structure of amorphous AsTe3.
• To investigate the coordination numbers and bonding characteristics of arsenic and tellurium atoms.

Main Methods:

• X-ray diffraction (XRD) for experimental validation.
• First-principles molecular dynamics (FPMD) simulations.
• Machine learning-based interatomic potentials (ML-GAP) trained on ab initio data.

Main Results:

• Good agreement between experimental and simulated diffraction patterns.
• Arsenic (As) and Tellurium (Te) atoms generally follow the 8-N rule with average coordination numbers of 3 and 2, respectively.
• Presence of under/over-coordinated atoms and Te3 species; As atoms form pyramidal structures linked by Te chains, exhibiting significant chemical disorder and homopolar bonds.

Conclusions:

• The atomic structure of amorphous AsTe3 is characterized by pyramidal As environments and Te chains.
• High chemical disorder and homopolar As-As bonds are prevalent, even at low As concentrations.
• Computational methods accurately model the experimental structure, providing insights into chalcogenide glass properties.