Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(6)

Published: Feb. 12, 2025

Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity often makes it challenging to address potential finite-size effects. Here, we present a specialized graphics processing units (GPUs) implementation of PIMD methods, including ring-polymer (RPMD) and thermostatted (TRPMD), into the open-source Graphics Processing Units Molecular Dynamics (GPUMD) package, combined with highly accurate efficient machine-learned neuroevolution (NEP) models. This approach achieves almost accuracy first-principles calculations efficiency empirical potentials, enabling large-scale atomistic that incorporate effects, effectively overcoming limitations at relatively affordable cost. We validate demonstrate efficacy NEP-PIMD by examining various thermal properties diverse materials, lithium hydride (LiH), three porous metal–organic frameworks (MOFs), liquid water, elemental aluminum. For LiH, our successfully capture isotope effect, reproducing experimentally observed dependence lattice parameter on reduced mass. MOFs, results reveal achieving good agreement experimental data requires consideration both dispersive interactions. significant impact its microscopic structure. aluminum, TRPMD method captures expansion phonon properties, aligning well mechanical predictions. GPU-accelerated GPUMD package provides an alternative, accessible, accurate, scalable tool exploring complex material influenced applications across broad range

Language: Английский

Advances in modeling complex materials: The rise of neuroevolution potentials

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 1, 2025

Interatomic potentials are essential for driving molecular dynamics (MD) simulations, directly impacting the reliability of predictions regarding physical and chemical properties materials. In recent years, machine-learned (MLPs), trained against first-principles calculations, have become a new paradigm in materials modeling as they provide desirable balance between accuracy computational cost. The neuroevolution potential (NEP) approach, implemented open-source GPUMD software, has emerged promising potential, exhibiting impressive exceptional efficiency. This review provides comprehensive discussion on methodological practical aspects NEP along with detailed comparison other representative state-of-the-art MLP approaches terms training accuracy, property prediction, We also demonstrate application approach to perform accurate efficient MD addressing complex challenges that traditional force fields typically cannot tackle. Key examples include structural liquid amorphous materials, order alloy systems, phase transitions, surface reconstruction, material growth, primary radiation damage, fracture two-dimensional nanoscale tribology, mechanical behavior compositionally alloys under various loadings. concludes summary perspectives future extensions further advance this rapidly evolving field.

Language: Английский

The Dynamic Diversity and Invariance of Ab Initio Water

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 19, 2024

Comprehending water dynamics is crucial in various fields, such as desalination, ion separation, electrocatalysis, and biochemical processes. While ab initio molecular (AIMD) accurately portray water's structure, computing its dynamic properties over nanosecond time scales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to determine the of liquid with accuracy. Our findings reveal diversity calculated diffusion coefficient (D) viscosity (η) across different methodologies. Specifically, while GGA, meta-GGA, hybrid functional methods struggle predict under ambient conditions, on higher level Jacob's ladder DFT approximation perform significantly better. Intriguingly, we discovered that both D η adhere established Stokes–Einstein (SE) relation for all water. The observed can be attributed distinct structural entropy, affirming applicability excess entropy scaling relations functionals. correlation between provides valuable insights identifying ideal temperature replicate Furthermore, our validate rationale behind employing artificially high temperatures simulation via AIMD. These outcomes not only pave path designing better functionals but also underscore significance many-body characteristics.

Language: Английский