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

@article{Wang2019DeepLI, title={Deep learning inter-atomic potential model for accurate irradiation damage simulations}, author={Hao Wang and Xun Guo and Linfeng Zhang and Han Wang and Jianming Xue}, journal={Applied Physics Letters}, year={2019} }

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…

## 18 Citations

Deep-learning interatomic potential for irradiation damage simulations in MoS2 with ab initial accuracy

- Materials Science
- 2020

Potentials that could accurately describe the irradiation damage processes are highly desired to figure out the atomic-level response of various newly-discovered materials under irradiation…

A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment

- Materials Science
- 2021

Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the understanding of the…

A Tungsten Deep Potential with High Accuracy and Generalization Ability based on a Newly Designed Three-body Embedding Formalism

- Materials Science
- 2021

Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the understanding of the…

Machine-learning interatomic potential for radiation damage and defects in tungsten

- Materials SciencePhysical Review B
- 2019

We introduce a machine-learning interatomic potential for tungsten using the Gaussian Approximation Potential framework. We specifically focus on properties relevant for simulations of…

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

- Computer ScienceComput. Phys. Commun.
- 2021

Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning

- Computer ScienceSC20: International Conference for High Performance Computing, Networking, Storage and Analysis
- 2020

A machine learningbased simulation protocol (Deep Potential Molecular Dynamics), while retaining ab initio accuracy, can simulate more than 1 nanosecond-long trajectory of over 100 million atoms per day, using a highly optimized code (GPU DeePMD-kit) on the Summit supercomputer.

Deep Learning Inter-atomic Potential for Thermal and Phonon Behaviour of Silicon Carbide with Quantum Accuracy

- Materials Science
- 2021

Silicon carbide (SiC) is an essential material for next generation semiconductors and components for nuclear plants. It’s applications are strongly dependent on its thermal conductivity, which is…

On the role of ion potential energy in low energy HiPIMS deposition: An atomistic simulation

- PhysicsSurface and Coatings Technology
- 2021

Deep Neural Network for Accurate and Efficient Atomistic Modeling of Phase Change Memory

- Computer ScienceIEEE Electron Device Letters
- 2020

This letter presents a general-purpose fully-atomistic method to simulate phase change memory (PCM), by combining density functional theory (DFT) and deep neural network (DNN) and its efficiency and accuracy may be useful to develop next-generation atomistic modeling tools to enable in-depth optimization of PCM.

nap: A molecular dynamics package with parameter-optimization programs for classical and machine-learning potentials

- Computer ScienceJ. Open Source Softw.
- 2021

The nap is a package for molecular dynamics (MD) simulation consisting of an MD program (pmd) that can perform large-scale simulation using a spatial-decomposition technique and two…

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