Theoretical investigations into the controlled growth of carbon films are essential for guiding the experimental fabrication of carbon-based devices.However,accurately simulating the deposition process remains a signi...Theoretical investigations into the controlled growth of carbon films are essential for guiding the experimental fabrication of carbon-based devices.However,accurately simulating the deposition process remains a significant challenge.In this work,we developed an active learning workflow to construct a machine learning-based neuroevolution potential(NEP)for investigating carbon atoms deposition growth on various substrates.By integrating molecular dynamics and time-stamped forcebiased Monte Carlo simulations,we studied the growth of amorphous carbon films on Si(111)and found that deposition energy strongly influenced bonding topology and film morphology.The NEP reliably captured the surface diffusion of carbon atoms,the formation of carbon chains and rings.We revealed a new growth mechanism of adhesion-driven growth at low energies and peening-induced densification at high energies of carbon atoms on Si(111)substrates.To evaluate the transferability of fitting workflow,we extended the NEP to simulate carbon deposition on Cu(111)and Al_(2)O_(3)(0001)surface.Simulation results demonstrate that the NEP can reproduce the subprocesses of graphene formation during carbon growth on the Cu(111)substrate.In contrast,only disordered carbon chains are observed on the Al_(2)O_(3)(0001)substrate.This work provides atomistic insights into the growth mechanisms of carbon films on representative substrates and establishes a robust computational framework for synthesis of diverse carbon nanostructures.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52262021,and 51761004)Industry and Education Combination Innovation Platform of Intelligent Manufacturing and Graduate Joint Training Base at Guizhou University(Grant No.2020-520000-83-01-324061)+2 种基金the Guizhou Province Science and Technology Fund,China(Grant Nos.ZK[2021]051,ZK[2023]013)High-level Creative Talent Training Program in Guizhou Province of China(Grant No.[2015]4015)Guizhou Engineering Research Center forsmart services(Grant No.2203-520102-04-04-298868).
文摘Theoretical investigations into the controlled growth of carbon films are essential for guiding the experimental fabrication of carbon-based devices.However,accurately simulating the deposition process remains a significant challenge.In this work,we developed an active learning workflow to construct a machine learning-based neuroevolution potential(NEP)for investigating carbon atoms deposition growth on various substrates.By integrating molecular dynamics and time-stamped forcebiased Monte Carlo simulations,we studied the growth of amorphous carbon films on Si(111)and found that deposition energy strongly influenced bonding topology and film morphology.The NEP reliably captured the surface diffusion of carbon atoms,the formation of carbon chains and rings.We revealed a new growth mechanism of adhesion-driven growth at low energies and peening-induced densification at high energies of carbon atoms on Si(111)substrates.To evaluate the transferability of fitting workflow,we extended the NEP to simulate carbon deposition on Cu(111)and Al_(2)O_(3)(0001)surface.Simulation results demonstrate that the NEP can reproduce the subprocesses of graphene formation during carbon growth on the Cu(111)substrate.In contrast,only disordered carbon chains are observed on the Al_(2)O_(3)(0001)substrate.This work provides atomistic insights into the growth mechanisms of carbon films on representative substrates and establishes a robust computational framework for synthesis of diverse carbon nanostructures.
