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

Abstract

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.

arXiv comment29 pages, 15 figures, code and data available

References in corpus (82)

• Canonical sampling through velocity-rescaling
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• On representing chemical environments
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• SchNet - a deep learning architecture for molecules and materials
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
• Machine Learning Force Fields
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
• Pressure control using stochastic cell rescaling
• Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS
• Machine learning for molecular simulation
• Less is more: sampling chemical space with active learning
• Active learning of linearly parametrized interatomic potentials
• Machine-learning based interatomic potential for amorphous carbon
• Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport
• Physics-inspired structural representations for molecules and materials
• Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
• Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
• Machine learning a general purpose interatomic potential for silicon
• The hiphive package for the extraction of high-order force constants by machine learning
• Accurate sampling using Langevin dynamics
• Machine-learning interatomic potentials for materials science
• Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
• FCHL revisited: faster and more accurate quantum machine learning
• Force and heat current formulas for many-body potentials in molecular dynamics simulation with applications to thermal conductivity calculations
• Efficient molecular dynamics simulations with many-body potentials on graphics processing units
• WACSF - Weighted Atom-Centered Symmetry Functions as Descriptors in Machine Learning Potentials
• Homogeneous nonequilibrium molecular dynamics method for heat transport and spectral decomposition with many-body potentials
• Robust and efficient configurational molecular sampling via Langevin Dynamics
• sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
• Modeling heat transport in crystals and glasses from a unified lattice-dynamical approach
• Influence of Boundaries and Thermostatting on Nonequilibrium Molecular Dynamics Simulations of Heat Conduction in Solids
• On the Completeness of Atomic Structure Representations
• Thermal conductivity decomposition in two-dimensional materials: Application to graphene
• Atom-Density Representations for Machine Learning
• Growth Mechanism and Origin of High $s{p}^3$ Content in Tetrahedral Amorphous Carbon
• Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials
• End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
• Efficient nonparametric $n$-body force fields from machine learning
• Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
• Improving the accuracy of the neuroevolution machine learning potential for multi-component systems
• Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground
• N-Graphdiyne two-dimensional nanomaterials: Semiconductors with low thermal conductivity and high stretchability
• Amorphized graphene: A stiff material with low thermal conductivity
• A revisit to phonon-phonon scattering in single-layer graphene
• Physically Motivated Recursively Embedded Atom Neural Networks: Incorporating Local Completeness and Nonlocality
• Equivalence of the equilibrium and the nonequilibrium molecular dynamics methods for thermal conductivity calculations: From bulk to nanowire silicon
• Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations
• Spectral Decomposition of Thermal Conductivity: Comparing Velocity Decomposition Methods in Homogeneous Molecular Dynamics Simulations
• Accelerated molecular dynamics force evaluation on graphics processing units for thermal conductivity calculations
• PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials
• Recursive evaluation and iterative contraction of $N$-body equivariant features
• Thermal Transport in MoS${}_2$ from Molecular Dynamics using Different Empirical Potentials
• Kapitza thermal resistance across individual grain boundaries in graphene
• Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space
• PANNA: Properties from Artificial Neural Network Architectures
• A generalized decay law for particle- and wave-like thermal phonons
• Reduced Thermal Conductivity of Supported and Encased Monolayer and Bilayer MoS${}_2$
• Heat transport in pristine and polycrystalline single-layer hexagonal boron nitride
• Bimodal grain-size scaling of thermal transport in polycrystalline graphene from large-scale molecular dynamics simulations
• Ultrahigh convergent thermal conductivity of carbon nanotubes from comprehensive atomistic modeling
• PyXtal FF: a Python Library for Automated Force Field Generation
• Thermal and electronic transport characteristics of highly stretchable graphene kirigami
• Thermal transport properties of single-layer black phosphorous from extensive molecular dynamics simulations
• Thermal conductivity reduction in carbon nanotube by fullerene encapsulation: A molecular dynamics study
• Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
• Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions
• Phonon Confinement and Transport in Ultrathin Films
• KLIFF: A framework to develop physics-based and machine learning interatomic potentials
• Through the eyes of a descriptor: Constructing complete, invertible descriptions of atomic environments
• Mode Localization and Suppressed Heat Transport in Amorphous Alloys
• Inter-layer and Intra-layer Heat Transfer in Bilayer/Monolayer Graphene van der Waals Heterostructure: Is There a Kapitza Resistance Analogous?
• A minimal Tersoff potential for diamond silicon with improved descriptions of elastic and phonon transport properties
• Heat transport across graphene/hexagonal-BN tilted grain boundaries from phase-field crystal model and molecular dynamics simulations
• Interpretation of apparent thermal conductivity in finite systems from equilibrium molecular dynamics simulations
• Exactly equivalent thermal conductivity in finite systems from equilibrium and nonequilibrium molecular dynamics simulations
• REANN: A PyTorch-based End-to-End Multi-functional Deep Neural Network Package for Molecular, Reactive and Periodic Systems

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• DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
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• Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine-learning molecular dynamics simulations
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• The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials
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• Copper delocalization leads to ultralow thermal conductivity in chalcohalide CuBiSeCl2
• Dynasor 2: From Simulation to Experiment Through Correlation Functions
• Efficient moment tensor machine-learning interatomic potential for accurate description of defects in Ni-Al Alloys
• Exploring the energy landscape of aluminas through machine learning interatomic potential
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• Optical line shapes of color centers in solids from classical autocorrelation functions
• Unique Hierarchical Rotational Dynamics Induces Ultralow Lattice Thermal Conductivity in Cyanide-bridged Framework Materials
• Consistency between the Green-Kubo formula and Lorentz model for predicting the infrared dielectric function of polar materials
• Predicting neutron experiments from first principles: A workflow powered by machine learning
• Anisotropic and isotropic elasticity and thermal transport in monolayer C${}_{24}$ networks from machine-learning molecular dynamics
• Guest Editorial: Special Topic on Software for Atomistic Machine Learning
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• Modular hybrid machine learning and physics-based potentials for scalable modeling of van der Waals heterostructures
• Role of octahedral tilting induced acoustic softening on limiting thermal transport in SrSnO3
• Accelerating the Discovery of Materials with Expected Thermal Conductivity via a Synergistic Strategy of DFT and Interpretable Deep Learning
• Decoding lithium's subtle phase stability with a machine learning force field
• Uniaxial stress tuning of interfacial thermal conductance in cubic BAs/4H-SiC heterostructures
• Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations
• LLM-driven discovery for carbon allotropes with bond-network entropy
• Suitability of available interatomic potentials for Sn to model its 2D allotropes
• Interface phonon modes governing the ideal limit of thermal transport across diamond/cubic boron nitride interfaces
• Sliding and superlubric moiré twisting ferroelectric transition in HfO2
• A review of simulation, measurement techniques, and development in chip thermal design
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