• Corpus ID: 219401531

Automatic machine-learning potential generation scheme and simulation protocol for the LiGePS-type superionic conductors

  title={Automatic machine-learning potential generation scheme and simulation protocol for the LiGePS-type superionic conductors},
  author={Jianxing Huang and Linfeng Zhang and Han Wang and Jinbao Zhao and Jun Cheng and E Weinan},
  journal={arXiv: Computational Physics},
It has been a challenge to accurately simulate Li-ion diffusion processes in battery materials at room temperature using {\it ab initio} molecular dynamics (AIMD) due to its high computational cost. This situation has changed drastically in recent years due to the advances in machine learning-based interatomic potentials. Here we implement the Deep Potential Generator scheme to \textit{automatically} generate interatomic potentials for LiGePS-type solid-state electrolyte materials. This… 

Figures and Tables from this paper



Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

It is well-known that the calculation of thermal conductivity using classical molecular dynamics (MD) simulations strongly depends on the choice of the appropriate interatomic potentials. As proven

An electrostatic spectral neighbor analysis potential for lithium nitride

Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms of prediction

De novo exploration and self-guided learning of potential-energy surfaces

Interatomic potential models based on machine learning (ML) are rapidly developing as tools for material simulations. However, because of their flexibility, they require large fitting databases that

Simulating Diffusion Properties of Solid‐State Electrolytes via a Neural Network Potential: Performance and Training Scheme

The results show that the DeePMD model may be a successful component of a framework to identify novel solid-state electrolytes and find good agreement with previous computations.

Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation

Application to the sample systems of Al, Mg and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.

Data-driven learning and prediction of inorganic crystal structures.

This paper presents a GAP-RSS interatomic potential model for elemental phosphorus, which identifies and correctly "learns" the orthorhombic black phosphorus structure without prior knowledge of any crystalline allotropes.

Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning

A methodology based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials actively learning on-the-fly allows for an automated construction of an interatomic interaction model from scratch, replacing the expensive density functional theory (DFT) and giving a speedup of several orders of magnitude.

Machine-learned multi-system surrogate models for materials prediction

Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the computational

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures.

Gaussian approximation potential (GAP) models for the interaction of lithium atoms with graphene, graphite, and disordered carbon nanostructures, based on reference density functional theory data are generated.

Study of Li atom diffusion in amorphous Li3PO4 with neural network potential.

The NN potential was used together with the nudged elastic band, kinetic Monte Carlo, and molecular dynamics methods to characterize Li vacancy diffusion behavior in the amorphous Li3PO4 model, and the formation of P2O7 units was observed, which is consistent with the experimental characterization.