Ab initio modelling of the early stages of precipitation in Al-6000 alloys

Giofrè, Daniele; Junge, Till; Ceriotti, Michele

doi:10.1016/j.actamat.2017.08.017

Cited by 15 publications

(10 citation statements)

References 36 publications

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“…The reference data set we used as a starting point is composed of the 10551 structures used by Kobayashi et al [51], supplemented by 609 structures of β -phase precipitates and interfaces that have been generated in a previous DFT study of the alloy [52]. Given that many of the resulting 11160 structures are taken from short MD runs and are highly correlated, we selected 2000 structures with FPS, that have been used both for the selection of SF and the training/testing procedure.…”

Section: B a Potential For Aluminum Alloysmentioning

confidence: 99%

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

Imbalzano

Anelli

Giofrè

et al. 2018

The Journal of Chemical Physics

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320

294

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Machine learning of atomic-scale properties is revolutionizing molecular modeling, making it possible to evaluate inter-atomic potentials with first-principles accuracy, at a fraction of the costs. The accuracy, speed, and reliability of machine learning potentials, however, depend strongly on the way atomic configurations are represented, i.e., the choice of descriptors used as input for the machine learning method. The raw Cartesian coordinates are typically transformed in "fingerprints," or "symmetry functions," that are designed to encode, in addition to the structure, important properties of the potential energy surface like its invariances with respect to rotation, translation, and permutation of like atoms. Here we discuss automatic protocols to select a number of fingerprints out of a large pool of candidates, based on the correlations that are intrinsic to the training data. This procedure can greatly simplify the construction of neural network potentials that strike the best balance between accuracy and computational efficiency and has the potential to accelerate by orders of magnitude the evaluation of Gaussian approximation potentials based on the smooth overlap of atomic positions kernel. We present applications to the construction of neural network potentials for water and for an Al-Mg-Si alloy and to the prediction of the formation energies of small organic molecules using Gaussian process regression.

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Section: B a Potential For Aluminum Alloysmentioning

confidence: 99%

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

Imbalzano

Anelli

Giofrè

et al. 2018

The Journal of Chemical Physics

Self Cite

320

294

View full text Add to dashboard Cite

show abstract

“…Density functional theory (DFT) has been shown to provide reliable energetics for aluminum and its alloys . We have used self-consistent DFT as implemented in the Quantum ESPRESSO package [4] . Quantum ESPRESSO is open-Source Package for Research in Electronic Structure, Simulation, and Optimization, is an integrated software suite for first-principle simulations, using density-functional theory (DFT), a plane waves (PW) basis set and pseudopotentials (PP) Computational Details.…”

Section: Methodsmentioning

confidence: 99%

“…Of special interest are Al-Mg-Si alloys, being the alloys of choice for medium-strength architectural and transportation applications, most prominently in the automotive industry [2,3] .The purpose of adding the two main alloying elements Mg and Si in small amounts (≈ 1%) is to form hardening precipitates, which typically contribute with a fivefold strength increase from pure aluminium. Understanding the process of precipitation and how it affects mechanical properties have been important subjects of research for hundred years [4] . Only in the last decades we begun to understand the structure and formation of the different phases during heat treatment.…”

Section: Introductionmentioning

confidence: 99%

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Study of First Principles of Mg-Si Binary Systems as Precipitates from T6 Heat Treatment of AL-MG-SI ALLOYS

Haryadi

Rudianto

2020

Indonesian J Appl Phys

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<p>One of the purposes adding the two main alloying elements Si and Mg in small quantities (≈ 1%) on Aluminum is to form strengthening precipitates in aluminium alloy, which typically contribute with a fivefold strength increase from pure aluminum. In the last decades, the investigation has begun to understand the structure and formation of the different phases during heat treatment. An important precipitate in Al-Mg-Si Alloy is β" and β. when the Precipitate β is created after β" is that the strength suddenly drops and the alloy is said to be overaged. By means of a first-principles supercell approach and density functional theory calculations, this research studied precipitate energy and interface energy of precipitate β" and β. Results show that precipitate β have precipitate energy higher then β" but have interface energy lowers then β". And each low energy interface was found possesses interface with the Al matrix is fully incoherent. The final result of calculation and simulation values for the interfacial energies, precipitate energies and strain energies that can be used in future predictions of the characteristic precipitate.</p>

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“…The popular matrix used in AMC is Al 6061. In the commercial, Al 6061 is an aluminum alloy that can be improved in mechanical properties after heat treatment (Giofré et al, 2017). The Al 6061 is commonly chosen as construction, automotive, aerospace, and marine application due to their characteristics such as intermediate strength, good corrosion resistance, and can be improved in mechanical properties (Gireesh et al, 2018).…”

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confidence: 99%

Technical Investigation of Sea Sand Reinforcement for Novel Al6061- Sea Sand Composites: Identification of Performance and Mechanical Properties

Akbar¹,

Surojo²,

Ariawan³

et al. 2020

PTQ

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Lightweight materials with low-cost production have been developed in recent years. The addition of ceramics and oxide particles such as Al2O3, SiC, and SiO2 has been improving in the mechanical properties of aluminum matrix composites (AMCs). As a solution, the use of natural reinforcing material continues to investigate. This paper investigates sea sand as an alternative reinforcement to AMCs. The Al 6061 was used as the matrix and sea sand as reinforcement. The manufacturing of the composite was conducted by stir casting route with variation 0, 2, 4, 6 %wt of the reinforcement. The composite was tested in hardness and tensile strength, the hardness test was obtained according to ASTM E-10, and the tensile test was conducted according to JIS Z2201 standard. Composite density decreases linearly with the addition of the reinforcement from 2 %wt to 6%wt, and the same phenomenon is obtained in porosity, the porosity increases with the addition of sea sand particles from 2%wt to 6%wt. The decline in density due to the lower density of sea sand particles compared to the aluminum matrix. Higher the sea sand particle that disperses into the matrix resulted in a lower density of the composite. Increasing porosity caused a higher fraction of reinforcement resulting in wider surface contact between matrix and reinforcement that promote the porosity. The hardness and ultimate tensile strength of AMCs increase with increasing of sea sand particles content. The presence of an oxide compound on the sea sand increased the mechanical properties of the composite. Increasing in mechanical properties indicate the sea sand particle dispersed into the matrix and sea sand can be used as engineering purpose material.

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Ab initio modelling of the early stages of precipitation in Al-6000 alloys

Cited by 15 publications

References 36 publications

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

Study of First Principles of Mg-Si Binary Systems as Precipitates from T6 Heat Treatment of AL-MG-SI ALLOYS

Technical Investigation of Sea Sand Reinforcement for Novel Al6061- Sea Sand Composites: Identification of Performance and Mechanical Properties

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