Deep learning inter-atomic potential model for accurate irradiation damage simulations

Abstract

We propose a hybrid scheme that interpolates smoothly the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a newly developed deep learning potential energy model. The resulting DP-ZBL model can not only provide overall good performance on the predictions of near-equilibrium material properties but also capture the right physics when atoms are extremely close to each other, an event that frequently happens in computational simulations of irradiation damage events. We applied this scheme to the simulation of the irradiation damage processes in the face-centered-cubic aluminium system, and found better descriptions in terms of the defect formation energy, evolution of collision cascades, displacement threshold energy, and residual point defects, than the widely-adopted ZBL modified embedded atom method potentials and its variants. Our work provides a reliable and feasible scheme to accurately simulate the irradiation damage processes and opens up new opportunities to solve the predicament of lacking accurate potentials for enormous newly-discovered materials in the irradiation effect field.

References in corpus (16)

• 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
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• Quantum-Chemical Insights from Deep Tensor Neural Networks
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
• MEAM potentials for Al, Si, Mg, Cu, and Fe alloys
• Deep Potential: a general representation of a many-body potential energy surface
• Extending the Accuracy of the SNAP Interatomic Potential Form
• A learning scheme to predict atomic forces and accelerate materials simulations
• Reinforced dynamics for enhanced sampling in large atomic and molecular systems
• Diffusion quantum Monte Carlo study of the equation of state and point defects in aluminum
• Energetics of point defects in aluminum via orbital-free density functional theory
• Adaptive coupling of a deep neural network potential to a classical force field

Cited by in corpus (12)

• DeePMD-kit v2: A software package for Deep Potential models
• 86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy
• Deep Potentials for Materials Science
• Machine-learning interatomic potential for radiation damage and defects in tungsten
• A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment
• Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
• Complex Spin Hamiltonian Represented by Artificial Neural Network
• Measuring transferability issues in machine-learning force fields: The example of Gold-Iron interactions with linearized potentials
• Pattern Matching and Classification of Clusters in Collision Cascades
• On the role of ion potential energy in low energy HiPIMS deposition: An atomistic simulation
• Deep-learning interatomic potential for irradiation damage simulations in MoS2 with ab initial accuracy
• Improved capabilities of the TurboGAP code for radiation induced cascade simulations: an illustration with silicon