Machine Learning Interatomic Potential for Modeling the Mechanical and Thermal Properties of Naphthyl-Based Nanotubes

Area of Science:

• Materials Science
• Nanotechnology
• Computational Chemistry

Background:

• Two-dimensional (2D) nanomaterials, particularly carbon allotropes like graphene, offer significant potential for technological advancements.
• Exploration of new carbon allotropes and their synthesis is an active area of research.
• Dihydropyrene (DHQ)-based materials represent a novel class of carbon allotropes with unique ring structures (4-, 6-, and 10-membered rings).

Purpose of the Study:

• To investigate the mechanical and thermal properties of dihydropyrene (DHQ)-based monolayers and nanotubes.
• To explore the potential of DHQ-based materials as novel carbon allotropes.
• To establish a synthesis route for DHQ-based materials using naphthalene as a molecular precursor.

Main Methods:

• Development of a machine-learned interatomic potential (MLIP) for simulating DHQ-based nanomaterials.
• Training the MLIP using data from density functional theory (DFT/Perdew-Burke-Ernzerhof) calculations and ab initio molecular dynamics (AIMD).
• Conducting classical molecular dynamics (CMD) simulations with the trained MLIP to analyze mechanical and thermal behaviors.

Main Results:

• DHQ-based nanotubes exhibit a Young's modulus ranging from 127 to 243 N/m, influenced by chirality and diameter.
• These nanotubes demonstrate fracture resilience, with failure occurring at strains between 13.6% and 17.4%.
• A critical temperature of 2200 K was identified, above which DHQ-based materials transition to an amorphous phase.

Conclusions:

• DHQ-based carbon nanomaterials possess tunable mechanical properties and a defined thermal stability limit.
• The developed MLIP enables efficient large-scale simulations of these novel nanomaterials.
• DHQ-based materials show promise for applications requiring specific mechanical and thermal characteristics.