Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties

• URL: http://arxiv.org/abs/2411.10911v1
• Date: Sat, 16 Nov 2024 23:16:59 GMT
• Title: Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
• Authors: Junlan Liu, Qian Yin, Mengshu He, Jun Zhou,
• Abstract summary: We introduce a neuroevolution potential (NEP) trained on a dataset generated from ab initio molecular dynamics (AIMD) simulations. We calculate the phonon density of states (DOS) and radial distribution function (RDF) using both machine learning potentials. While the MTP potential offers slightly higher accuracy, the NEP achieves a remarkable 41-fold increase in computational speed.
• Score: 6.875235178607604
• License:
• Abstract: The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications. In this study, we introduce a neuroevolution potential (NEP), trained on a dataset generated from ab initio molecular dynamics (AIMD) simulations, using the moment tensor potential (MTP) as a reference. The low root mean square errors (RMSEs) for total energy and atomic forces demonstrate the high accuracy and transferability of both the MTP and NEP. We further calculate the phonon density of states (DOS) and radial distribution function (RDF) using both machine learning potentials, comparing the results to density functional theory (DFT) calculations. While the MTP potential offers slightly higher accuracy, the NEP achieves a remarkable 41-fold increase in computational speed. These findings provide detailed microscopic insights into the dynamics and rapid Cu-ion diffusion, paving the way for future studies on Cu-based solid electrolytes and their applications in energy devices.

Related papers

• Predicting ionic conductivity in solids from the machine-learned potential energy landscape [68.25662704255433]
  Superionic materials are essential for advancing solid-state batteries, which offer improved energy density and safety. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. We propose an approach for the quick and reliable evaluation of ionic conductivity through the analysis of a universal interatomic potential.
  arXiv  Detail & Related papers  (2024-11-11T09:01:36Z)
• Reliable machine learning potentials based on artificial neural network for graphene [2.115174610040722]
  Special 2D structure of graphene enables it to exhibit a wide range of peculiar material properties. molecular dynamics (MD) simulations are widely adopted for understanding the microscopic origins of their unique properties. An artificial neural network based interatomic potential has been developed for graphene to represent the potential energy surface.
  arXiv  Detail & Related papers  (2023-06-12T17:12:08Z)
• KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory [13.437597619451568]
  KineticNet is an equivariant deep neural network architecture based on point convolutions adapted to the prediction of quantities on molecular quadrature grids. For the first time, chemical accuracy of the learned functionals is achieved across input densities and geometries of tiny molecules.
  arXiv  Detail & Related papers  (2023-05-08T17:43:31Z)
• Spin Current Density Functional Theory of the Quantum Spin-Hall Phase [59.50307752165016]
  We apply the spin current density functional theory to the quantum spin-Hall phase. We show that the explicit account of spin currents in the electron-electron potential of the SCDFT is key to the appearance of a Dirac cone.
  arXiv  Detail & Related papers  (2022-08-29T20:46:26Z)
• Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials [0.0]
  We propose a machine learning method for constructing high-dimensional potential energy surfaces based on feed-forward neural networks. The accuracy of the developed approach in representing both chemical and configurational spaces is comparable to the one of several established machine learning models.
  arXiv  Detail & Related papers  (2021-09-15T16:46:46Z)
• Comparison of Optical Response from DFT Random Phase Approximation and Low-Energy Effective Model: Strained Phosphorene [0.0]
  We compare and contrast the dispersive permittivity tensor, using both a low-energy effective model and density functional theory (DFT) Our results suggest that the random-phase approximation employed in widely used DFT packages should be revisited and improved to be able to predict these fundamental electronic characteristics of a given material with confidence.
  arXiv  Detail & Related papers  (2021-09-01T18:00:06Z)
• Relativistic aspects of orbital and magnetic anisotropies in the chemical bonding and structure of lanthanide molecules [60.17174832243075]
  We study the electronic and ro-vibrational states of heavy homonuclear lanthanide Er2 and Tm2 molecules by applying state-of-the-art relativistic methods. We were able to obtain reliable spin-orbit and correlation-induced splittings between the 91 Er2 and 36 Tm2 electronic potentials dissociating to two ground-state atoms.
  arXiv  Detail & Related papers  (2021-07-06T15:34:00Z)
• Computing molecular excited states on a D-Wave quantum annealer [52.5289706853773]
  We demonstrate the use of a D-Wave quantum annealer for the calculation of excited electronic states of molecular systems. These simulations play an important role in a number of areas, such as photovoltaics, semiconductor technology and nanoscience.
  arXiv  Detail & Related papers  (2021-07-01T01:02:17Z)
• BIGDML: Towards Exact Machine Learning Force Fields for Materials [55.944221055171276]
  Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 atoms.
  arXiv  Detail & Related papers  (2021-06-08T10:14:57Z)
• Controlled coherent dynamics of [VO(TPP)], a prototype molecular nuclear qudit with an electronic ancilla [50.002949299918136]
  We show that [VO(TPP)] (vanadyl tetraphenylporphyrinate) is a promising system suitable to implement quantum computation algorithms. It embeds an electronic spin 1/2 coupled through hyperfine interaction to a nuclear spin 7/2, both characterized by remarkable coherence.
  arXiv  Detail & Related papers  (2021-03-15T21:38:41Z)
• Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks [2.7035666571881856]
  We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum.
  arXiv  Detail & Related papers  (2020-10-10T05:38:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.