Effect of temperature, pressure, crystal defect types, and densities on the mechanical behavior of tungsten under tensile deformation: A molecular dynamics simulation study

Alivaliollahi, A.; Alahyarizadeh, Gh.; Minuchehr, A.

doi:10.1016/j.nme.2023.101555

Cited by 4 publications

(1 citation statement)

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“…We have extended our examination to encompass additional mechanical properties that can be derived from the elastic constants, the elastic constants, as described in refs 45 , 46 , and elucidated in the methods section. Comprehensive mechanical behavior regarding these derived properties can be found in the supplementary Information.…”

Section: Resultsmentioning

confidence: 99%

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

Peivaste,

Ramezani,

Alahyarizadeh

et al. 2024

Sci Rep

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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 level, the study demonstrates the potential of trained artificial neural networks (tANNs) as surrogate models. These tANNs capture complex patterns from built datasets, enabling fast and accurate predictions of material properties. The article highlights the application of 3D convolutional neural networks (CNNs) to incorporate atomistic details and defects in predictions, a significant advancement compared to current 2D image-based, or descriptor-based methods. Through a dataset of atomistic structures and MD simulations, the trained 3D CNN achieves impressive accuracy, predicting material properties with a root-mean-square error below 0.65 GPa for the prediction of elastic constants and a speed-up of approximately 185 to 2100 times compared to traditional MD simulations. This breakthrough promises to expedite materials design processes and facilitate scale-bridging in materials science, offering a new perspective on addressing computational demands in atomistic simulations.

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Section: Resultsmentioning

confidence: 99%