First-Principles and Machine Learning Investigation of the Structural and Optoelectronic Properties of Dodecaphenylyne: A Novel Carbon Allotrope
Kleuton A. L. Lima, Jose A. S. Laranjeira, Nicolas F. Martins, Julio R. Sambrano, Alexandre C. Dias, Luiz A. Ribeiro Junior, Douglas S. Galvao

TL;DR
This study computationally discovers a new stable carbon allotrope, Dodecaphenylyne, with unique structural, electronic, and optical properties, showing promise for advanced optoelectronic applications.
Contribution
It introduces Dodecaphenylyne as a novel carbon allotrope with detailed first-principles and machine learning-based characterization, highlighting its stability and exceptional electronic and optical features.
Findings
High thermodynamic stability with formation energy of -7.98 eV/atom.
Pronounced anisotropic electronic mobility up to 30.6×10^4 cm^2/V·s.
Significant optical absorption in visible and ultraviolet spectra.
Abstract
We report the computational discovery and characterization of Dodecaphenylyne (DP), a novel carbon allotrope with a distinctive geometric arrangement. DP structural, thermodynamic, mechanical, electronic, and optical properties were evaluated using density functional theory and a machine learning interatomic potential trained explicitly for this material. The formation energy of -7.98 eV/atom indicates high thermodynamic stability, further supported by the absence of imaginary phonon modes and the preservation of structural integrity up to 1000 K in ab initio molecular dynamics simulations. Mechanical analysis reveals high in-plane stiffness with directional dependence: Young's modulus values of 469.09 GPa and 600.41 GPa along the x and y directions, respectively. Electronic band structure and projected density of states analyses confirm the DP semiconducting character. Calculations of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning in Materials Science
