Browsing by Author "Wang, Yanzhou"
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- Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2024-06-19) Fan, Zheyong; Xiao, Yang; Wang, Yanzhou; Ying, Penghua; Chen, Shunda; Dong, HaikuanWe propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential (NEP) is trained using reference data from quantum-mechanical density-functional theory calculations. This trained potential is then applied in large-scale molecular dynamics simulations, enabling the generation of realistic structures and accurate characterization of thermal transport properties. In addition, molecular dynamics simulations of atoms and linear-scaling quantum transport calculations of electrons are coupled to account for the electron-phonon scattering and other disorders that affect the charge carriers governing the electronic transport properties. We demonstrate the usefulness of this unified approach by studying electronic transport in pristine graphene and thermoelectric transport properties of a graphene antidot lattice, with a general-purpose NEP developed for carbon systems based on an extensive dataset. - General-purpose machine-learned potential for 16 elemental metals and their alloys
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2024-12) Song, Keke; Zhao, Rui; Liu, Jiahui; Wang, Yanzhou; Lindgren, Eric; Wang, Yong; Chen, Shunda; Xu, Ke; Liang, Ting; Ying, Penghua; Xu, Nan; Zhao, Zhiqiang; Shi, Jiuyang; Wang, Junjie; Lyu, Shuang; Zeng, Zezhu; Liang, Shirong; Dong, Haikuan; Sun, Ligang; Chen, Yue; Zhang, Zhuhua; Guo, Wanlin; Qian, Ping; Sun, Jian; Erhart, Paul; Ala-Nissila, Tapio; Su, Yanjing; Fan, ZheyongMachine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a promising approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach’s effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys. - GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2022-09-21) Fan, Zheyong; Wang, Yanzhou; Ying, Penghua; Song, Keke; Wang, Junjie; Wang, Yong; Zeng, Zezhu; Xu, Ke; Lindgren, Eric; Rahm, J. Magnus; Gabourie, Alexander J.; Liu, Jiahui; Dong, Haikuan; Wu, Jianyang; Chen, Yue; Zhong, Zheng; Sun, Jian; Erhart, Paul; Su, Yanjing; Ala-Nissila, TapioWe 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 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, viz., gpyumd, calorine, and pynep, that enable the integration of gpumd into Python workflows. - Molecular dynamics simulations of heat transport using machine-learned potentials : A mini-review and tutorial on GPUMD with neuroevolution potentials
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2024-04-28) Dong, Haikuan; Shi, Yongbo; Ying, Penghua; Xu, Ke; Liang, Ting; Wang, Yanzhou; Zeng, Zezhu; Wu, Xin; Zhou, Wenjiang; Xiong, Shiyun; Chen, Shunda; Fan, ZheyongMolecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably predicting heat transport properties is the use of accurate and efficient interatomic potentials. Recently, machine-learned potentials (MLPs) have shown great promise in providing the required accuracy for a broad range of materials. In this mini-review and tutorial, we delve into the fundamentals of heat transport, explore pertinent MD simulation methods, and survey the applications of MLPs in MD simulations of heat transport. Furthermore, we provide a step-by-step tutorial on developing MLPs for highly efficient and predictive heat transport simulations, utilizing the neuroevolution potentials as implemented in the GPUMD package. Our aim with this mini-review and tutorial is to empower researchers with valuable insights into cutting-edge methodologies that can significantly enhance the accuracy and efficiency of MD simulations for heat transport studies. - Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2021-09-18) Fan, Zheyong; Zeng, Zezhu; Zhang, Cunzhi; Wang, Yanzhou; Song, Keke; Dong, Haikuan; Chen, Yue; Ala-Nissila, TapioWe develop a neuroevolution-potential (NEP) framework for generating neural network-based machine-learning potentials. They are trained using an evolutionary strategy for performing large-scale molecular dynamics (MD) simulations. A descriptor of the atomic environment is constructed based on Chebyshev and Legendre polynomials. The method is implemented in graphic processing units within the open-source gpumd package, which can attain a computational speed over atom-step per second using one Nvidia Tesla V100. Furthermore, per-atom heat current is available in NEP, which paves the way for efficient and accurate MD simulations of heat transport in materials with strong phonon anharmonicity or spatial disorder, which usually cannot be accurately treated either with traditional empirical potentials or with perturbative methods. - Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2023-02-01) Wang, Yanzhou; Fan, Zheyong; Qian, Ping; Caro, Miguel A.; Ala-Nissila, TapioAmorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials. Theoretical prediction of the thermal conductivity of a-Si in a wide range of temperatures and sample sizes is still a challenge. Herein we present a systematic investigation of the thermal transport properties of a-Si by employing large-scale molecular dynamics (MD) simulations with an accurate and efficient machine learned neuroevolution potential (NEP) trained against abundant reference data calculated at the quantum-mechanical density-functional-theory level. The high efficiency of NEP allows us to study the effects of finite size and quenching rate in the formation of a-Si in great detail. We find that a simulation cell up to 64000 atoms (a cubic cell with a linear size of 11 nm) and a quenching rate down to 1011 K s-1 are required for almost convergent thermal conductivity. Structural properties, including short- and medium-range order as characterized by the pair-correlation function, angular-distribution function, coordination number, ring statistics, and structure factor are studied to demonstrate the accuracy of NEP and to further evaluate the role of quenching rate. Using both the heterogeneous and homogeneous nonequilibrium MD methods and the related spectral decomposition techniques, we calculate the temperature- and thickness-dependent thermal conductivity values of a-Si and show that they agree well with available experimental results from 10 K to room temperature. Our results also highlight the importance of quantum effects in the calculated thermal conductivity and support the quantum-correction method based on the spectral thermal conductivity. - Structure and Pore Size Distribution in Nanoporous Carbon
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2021-01-04) Wang, Yanzhou; Fan, Zheyong; Qian, Ping; Ala-Nissila, Tapio; Caro, Miguel A.We study the structural and mechanical properties of nanoporous (NP) carbon materials by extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To this end, we retrain a ML Gaussian approximation potential (GAP) for carbon by recalculating the a-C structural database of Deringer and Csányi adding van der Waals interactions. Our GAP enables a notable speedup and improves the accuracy of energy and force predictions. We use the GAP to thoroughly study the atomistic structure and pore-size distribution in computational NP carbon samples. These samples are generated by a melt-graphitization-quench MD procedure over a wide range of densities (from 0.5 to 1.7 g/cm3) with structures containing 131 072 atoms. Our results are in good agreement with experimental data for the available observables and provide a comprehensive account of structural (radial and angular distribution functions, motif and ring counts, X-ray diffraction patterns, pore characterization) and mechanical (elastic moduli and their evolution with density) properties. Our results show relatively narrow pore-size distributions, where the peak position and width of the distributions are dictated by the mass density of the materials. Our data allow further work on computational characterization of NP carbon materials, in particular for energy-storage applications, as well as suggest future experimental characterization of NP carbon-based materials.