biostudies-literatureUnknown3(10)Xie SSearching for novel carbon allotropes with excellent mechanical and interesting electronic properties is valuable, but such a large structural search remains a challenge if purely based on the traditional density functional theory (DFT) combined with Monte-Carlo (MC) methods. Herein, the neural network potential is utilized to accelerate the sampling of the stochastic surface walking algorithm for a global structural search of ordered carbons from carbon nanotubes (CNTs) under pressure. A variety of unreported ordered carbons are obtained, among which CNTs with diameters smaller than 0.7 nm are more sensitive to pressure than bigger tubes. Most ordered carbons obtained show great thermodynamical and kinetic stability, verified by <i>ab initio</i> molecular dynamics simulations and phonon sPrecision chemistry612-618https://www.ebi.ac.uk/biostudies/studies/S-EPMC12569948biostudies-literatureNanotube Derived Ordered Carbons Predicted by Neural Network Potential.PMC12569948Ni KZhu YXie SfalseNanotube Derived Ordered Carbons Predicted by Neural Network Potential.Searching for novel carbon allotropes with excellent mechanical and interesting electronic properties is valuable, but such a large structural search remains a challenge if purely based on the traditional density functional theory (DFT) combined with Monte-Carlo (MC) methods. Herein, the neural network potential is utilized to accelerate the sampling of the stochastic surface walking algorithm for a global structural search of ordered carbons from carbon nanotubes (CNTs) under pressure. A variety of unreported ordered carbons are obtained, among which CNTs with diameters smaller than 0.7 nm are more sensitive to pressure than bigger tubes. Most ordered carbons obtained show great thermodynamical and kinetic stability, verified by <i>ab initio</i> molecular dynamics simulations and phonon s2025-01-01T00:00:00Z2025 Oct2026-06-05T09:35:15.017Z2026-05-15T03:13:01.524ZS-EPMC125699484117015510.1021/prechem.5c00020