Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks

Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks

Abstract This article introduces an innovative approach that utilizes machine learning (ML) to address the computational challenges of accurate atomistic simulations in materials science. Focusing on the field of molecular dynamics (MD), which offers insight into material behavior at the atomic leve...

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Bibliographic Details
Main Authors: Iman Peivaste, Saba Ramezani, Ghasem Alahyarizadeh, Reza Ghaderi, Ahmed Makradi, Salim Belouettar
Format: Article
Language:English
Published: Nature Portfolio 2024-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-50893-9

Internet

https://doi.org/10.1038/s41598-023-50893-9

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