Structure and Pore Size Distribution in Nanoporous Carbon

Wang, Yanzhou; Fan, Zheyong; Qian, Ping; Ala-Nissilä, Tapio; Miguel, A.

doi:10.1021/acs.chemmater.1c03279

Cited by 54 publications

(47 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The carbon data set 10 is particularly suitable for studying liquid and amorphous carbon. 10,137,138 In this example, we use a melt-quench-anneal protocol similar to that used in Ref. 10 (but with ten times longer simulation time for the relaxation at each temperature and an extra relaxation at 1000 K) to generate amorphous carbon.…”

Section: Quenchingmentioning

confidence: 99%

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

Fan

Wang

Ying

et al. 2022

The Journal of Chemical Physics

119

112

View full text Add to dashboard Cite

We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package GPUMD.We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor using a linear combination of Chebyshev basis functions and by extending the angular descriptor with some four-body and five-body contributions as in the atomic cluster expansion approach.We also detail our efficient implementation of the NEP approach in graphics processing units as well as our workflow for the construction of NEP models, and we demonstrate their application in large-scale atomistic simulations.By comparing to state-of-the-art MLPs, we show that the NEP approach not only achieves above-average accuracy but also is far more computationally efficient.These results demonstrate that the GPUMD package is a promising tool for solving challenging problems requiring highly accurate, large-scale atomistic simulations.To enable the construction of MLPs using a minimal training set, we propose an active-learning scheme based on the latent space of a pre-trained NEP model.Finally, we introduce three separate Python packages, GPYUMD, CALORINE, and PYNEP, which enable the integration of GPUMD into Python workflows.

show abstract

Section: Quenchingmentioning

confidence: 99%

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

Fan

Wang

Ying

et al. 2022

The Journal of Chemical Physics

119

112

View full text Add to dashboard Cite

show abstract

Section: Quenchingmentioning

confidence: 99%

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

Fan,

Wang,

Ying

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package gpumd. We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor using a linear combination of Chebyshev basis functions and by extending the angular descriptor with some four-body and five-body contributions as in the atomic cluster expansion approach. We also detail our efficient implementation of the NEP approach in graphics processing units as well as our workflow for the construction of NEP models, and we demonstrate their application in large-scale atomistic simulations. By comparing to state-of-the-art MLPs, we show that the NEP approach not only achieves above-average accuracy but also is far more computationally efficient. These results demonstrate that the gpumd package is a promising tool for solving challenging problems requiring highly accurate, large-scale atomistic simulations. To enable the construction of MLPs using a minimal training set, we propose an active-learning scheme based on the latent space of a pre-trained NEP model. Finally, we introduce two separate Python packages, gpyumd and calorine, which enable the integration of gpumd into Python workflows.

show abstract

“…In 2020, a large number of diverse carbon structures were employed to obtain a widely transferable GAP (GAP20) [46]. A closely related TurboGAP parameterization used a more efficient implementation of the SOAP descriptor [47,48]. These GAPs were applied to study complex phenomena, such as vapour deposition of amorphous carbon films [49] or the effect of defects on the corrugation of graphene [50].…”

Section: Introductionmentioning

confidence: 99%

Atomic cluster expansion for quantum-accurate large-scale simulations of carbon

Qamar¹,

Mrovec²,

Lysogorskiy³

et al. 2022

Preprint

View full text Add to dashboard Cite

We present an atomic cluster expansion (ACE) for carbon that improves over available classical and machine learning potentials. The ACE is parameterized from an exhaustive set of important carbon structures at extended volume and energy range, computed using density functional theory (DFT). Rigorous validation reveals that ACE predicts accurately a broad range of properties of both crystalline and amorphous carbon phases while being several orders of magnitude more computationally efficient than available machine learning models. We demonstrate the predictive power of ACE on three distinct applications, brittle crack propagation in diamond, evolution of amorphous carbon structures at different densities and quench rates and nucleation and growth of fullerene clusters under high pressure and temperature conditions.

show abstract

Structure and Pore Size Distribution in Nanoporous Carbon

Cited by 54 publications

References 52 publications

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

Atomic cluster expansion for quantum-accurate large-scale simulations of carbon

Contact Info

Product

Resources

About