DeePMD-kit v2: A software package for deep potential models

The Journal of Chemical Physics, Journal Year: 2023, Volume and Issue: 159(5)

Published: Aug. 1, 2023

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version offers numerous advanced features, such DeepPot-SE, attention-based hybrid descriptors, ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support customized operators, compression, non-von Neumann dynamics, improved usability, including documentation, compiled binary packages, graphical user interfaces, application programming interfaces. article presents an overview major highlighting its features technical details. Additionally, this comprehensive procedure conducting representative application, benchmarks accuracy efficiency different models, discusses ongoing developments.

Language: Английский

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Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials

Materials Horizons, Journal Year: 2023, Volume and Issue: 10(6), P. 1956 - 1968

Published: Jan. 1, 2023

This minireview highlights the superiority of machine learning interatomic potentials over conventional empirical and density functional theory calculations for analysis mechanical failure responses.

Language: Английский

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MAGUS: machine learning and graph theory assisted universal structure searcher

National Science Review, Journal Year: 2023, Volume and Issue: 10(7)

Published: May 8, 2023

ABSTRACT Crystal structure predictions based on first-principles calculations have gained great success in materials science and solid state physics. However, the remaining challenges still limit their applications systems with a large number of atoms, especially complexity conformational space cost local optimizations for big systems. Here, we introduce crystal prediction method, MAGUS, evolutionary algorithm, which addresses above machine learning graph theory. Techniques used program are summarized detail benchmark tests provided. With intensive tests, demonstrate that on-the-fly machine-learning potentials can be to significantly reduce expensive calculations, decomposition theory efficiently decrease required configurations order find target structures. We also representative this method several research topics, including unexpected compounds interior planets exotic states at high pressure temperature (superionic, plastic, partially diffusive state, etc.); new functional (superhard, high-energy-density, superconducting, photoelectric materials), etc. These successful demonstrated MAGUS code help accelerate discovery interesting phenomena, as well significant value general.

Language: Английский

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Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations

Physical review. B./Physical review. B, Journal Year: 2023, Volume and Issue: 107(5)

Published: Feb. 6, 2023

Amorphous silicon (a-Si) is an important thermal-management material and also serves as ideal playground for studying heat transport in strongly disordered materials. Theoretical prediction of the thermal conductivity a-Si a wide range temperatures sample sizes still challenge. Herein we present systematic investigation properties by employing large-scale molecular dynamics (MD) simulations with accurate efficient machine learned neuroevolution potential (NEP) trained against abundant reference data calculated at quantum-mechanical density-functional-theory level. The high efficiency NEP allows us to study effects finite size quenching rate formation great detail. We find that simulation cell up $64\phantom{\rule{0.16em}{0ex}}000$ atoms (a cubic linear 11 nm) down ${10}^{11}$ K ${\mathrm{s}}^{\ensuremath{-}1}$ are required almost convergent conductivity. Structural properties, including short- medium-range order characterized pair-correlation function, angular-distribution coordination number, ring statistics, structure factor studied demonstrate accuracy further evaluate role rate. Using both heterogeneous homogeneous nonequilibrium MD methods related spectral decomposition techniques, calculate temperature- thickness-dependent values show they agree well available experimental results from 10 room temperature. Our highlight importance quantum support quantum-correction method based on

Language: Английский

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Anisotropic and high thermal conductivity in monolayer quasi-hexagonal fullerene: A comparative study against bulk phase fullerene

International Journal of Heat and Mass Transfer, Journal Year: 2023, Volume and Issue: 206, P. 123943 - 123943

Published: Feb. 15, 2023

Language: Английский

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General-purpose machine-learned potential for 16 elemental metals and their alloys

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Nov. 25, 2024

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum elements and their alloys limits applicability. Here, we present promising approach constructing unified MLP numerous elements, demonstrated through model (UNEP-v1) 16 elemental metals alloys. To achieve complete representation chemical space, show, via principal component analysis diverse test datasets, that employing one-component two-component systems suffices. Our UNEP-v1 exhibits superior performance across various physical properties compared to widely used embedded-atom method potential, while maintaining efficiency. We demonstrate our approach's effectiveness reproducing experimentally observed order stable phases, large-scale simulations plasticity primary radiation damage in MoTaVW

Language: Английский

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Decoding Electrochemical Processes of Lithium‐Ion Batteries by Classical Molecular Dynamics Simulations

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: 14(22)

Published: March 19, 2024

Abstract Lithium‐ion batteries (LIBs) have played an essential role in the energy storage industry and dominated power sources for consumer electronics electric vehicles. Understanding electrochemistry of LIBs at molecular scale is significant improving their performance, stability, lifetime, safety. Classical dynamics (MD) simulations could directly capture atomic motions thus provide dynamic insights into electrochemical processes ion transport during charging discharging that are usually challenging to observe experimentally, which momentous developing with superb performance. This review discusses developments MD approaches using non‐reactive force fields, reactive machine learning potential modeling chemical reactions reactants electrodes, electrolytes, electrode‐electrolyte interfaces. It also comprehensively how interactions, structures, transport, reaction affect electrode capacity, interfacial properties. Finally, remaining challenges envisioned future routes commented on high‐fidelity, effective simulation methods decode invisible interactions LIBs.

Language: Английский

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Machine Learning for Polaritonic Chemistry: Accessing Chemical Kinetics

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(8), P. 5402 - 5413

Published: Feb. 14, 2024

Altering chemical reactivity and material structure in confined optical environments is on the rise, yet, a conclusive understanding of microscopic mechanisms remains elusive. This originates mostly from fact that accurately predicting vibrational reactive dynamics for soluted ensembles realistic molecules no small endeavor, adding (collective) strong light–matter interaction does not simplify matters. Here, we establish framework based combination machine learning (ML) models, trained using density-functional theory calculations molecular to accelerate such simulations. We then apply this approach evaluate coupling, changes reaction rate constant, their influence enthalpy entropy deprotection 1-phenyl-2-trimethylsilylacetylene, which has been studied previously both experimentally ab initio While find qualitative agreement with critical experimental observations, especially regard kinetics, also differences comparison previous theoretical predictions. The features ML-accelerated simulations agree show estimated kinetic behavior. Conflicting indicate contribution dynamic electronic polarization process more relevant than currently believed. Our work demonstrates practical use ML polaritonic chemistry, discusses limitations common approximations, paves way holistic description chemistry.

Language: Английский

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Million-atom heat transport simulations of polycrystalline graphene approaching first-principles accuracy enabled by neuroevolution potential on desktop GPUs

Journal of Applied Physics, Journal Year: 2025, Volume and Issue: 137(1)

Published: Jan. 2, 2025

First-principles molecular dynamics simulations of heat transport in systems with large-scale structural features are challenging due to their high computational cost. Here, using polycrystalline graphene as a case study, we demonstrate the feasibility simulating near first-principles accuracy containing over 1.4×106 atoms, achievable even consumer desktop GPUs. This is enabled by highly efficient neuroevolution potential (NEP) approach, implemented open-source GPUMD package. Leveraging NEP model’s and efficiency, quantify reduction thermal conductivity grain boundaries varying sizes, resolving contributions from in-plane out-of-plane (flexural) phonon modes. Additionally, find that can lead finite under significant tensile strain, contrast divergent behavior observed pristine similar conditions, indicating may play crucial role low-dimensional momentum-conserving systems. These findings could offer insights into interpreting experimental observations, given widespread presence both external strains real materials. The demonstrated ability simulate millions atoms near-first-principles on GPUs approach will help make high-fidelity atomistic more accessible broader research community.

Language: Английский

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Efficient crystal structure prediction based on the symmetry principle

Nature Computational Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

Language: Английский

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