Interatomic potential | EPFL Graph Search

Interatomic potentials are mathematical functions to calculate the potential energy of a system of atoms with given positions in space. Interatomic potentials are widely used as the physical basis of molecular mechanics and molecular dynamics simulations in computational chemistry, computational physics and computational materials science to explain and predict materials properties. Examples of quantitative properties and qualitative phenomena that are explored with interatomic potentials include lattice parameters, surface energies, interfacial energies, adsorption, cohesion, thermal expansion, and elastic and plastic material behavior, as well as chemical reactions. Functional form Interatomic potentials can be written as a series expansion of functional terms that depend on the position of one, two, three, etc. atoms at a time. Then the total potential of the system \textstyle V_\mathrm{} can be written as :: V_\mathrm{} = \sum_{i

An Atomistic Simulation Method for Oxygen Impurities in Aluminium Based on Variable Charge Molecular Dynamics

Andreas Elsener

Impurities are known to have a significant impact on materials properties. In particular, the presence of impurities can change mechanical properties and stabilize the microstructure by reducing grain growth and recrystallization processes. In the past atomistic simulations, in particular molecular dynamics, have contributed a lot to the understanding of deformation mechanisms in nanocrystalline metals. Especially, insights into details of dislocation/GB interactions have been gained. Additionally, simulations on coupled grain boundary migration in bicrystalline samples have played an important role in understanding stress assisted grain growth in nanocrystalline metals. However, all these simulations have been performed on monatomic systems. This means that the role of impurities has not yet been considered in these simulations. Some of the important difference between experiments and simulations could be removed by introducing dilute O distributions to computational Al samples. For this purpose, a simulation method is required which can deal with the oxidation of Al atoms around the O impurities and which can simulate the ionic and metallic nature of bonding simultaneously. Here, a method is suggested which fulfills the requirements by modifying the variable charge method of Streitz and Mintmire [Phys. Rev. B 50, 11996 (1994)]. A local chemical potential approach which optimizes the charge on only those atoms expected to be ionic is developed. Additionally the EAM potential for the non-electrostatic interaction given in the publication of Streitz and Mintmire is substituted by empirical potentials which treat the Al-Al interactions with a well-known Al potential. The Al-O and O-O interactions in these EAM potentials are fitted for the simulation of dilute O impurities in Al and additionally for the simulation of corundum in case of a second potential. The aforementioned developments are applied to study the effect of O impurities on the deformation of nc Al. A fully three-dimensional nc sample containing 15 grains of 12 nm grain-size is deformed with a dilute O content in the triple junction lines. The impact of O impurities on dislocation propagation is also considered in a sample which consists of a single grain extracted from a large molecular dynamics simulation. This simulation geometry contains a perfect dislocation, which is pinned at the surrounding grain boundary network. Therefore it allows the study of pinning strength and after unpinning the propagation behavior of the dislocation under various O impurity distributions. Additionally, the role of O impurities on coupled GB migration is investigated by studying two bicrystalline samples with different O distributions in both of them. In the discussion, the emphasis is on the simulation method. Especially, the significance of the samples, simulation conditions and the performance of the method in the different applications are discussed. Additionally, the different developments (local chemical potential approach and EAM potentials) are critically analyzed and improvements for future simulations of impurities are suggested.

EPFL2010

Neural Network Potentials for Age Hardening Aluminum Alloys

Daniel John Gilles Marchand

High-strength metal alloys achieve their performance via careful control of precipitates and solutes.The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic scale phenomena, particularly during early-stage nucleation and growth. Atomistic modeling using interatomic potentials is a desirable tool for understanding the detailed phenomena involved in precipitation and strengthening, which requires length and time scales far larger than those accessible by first-principles methods. Current interatomic potentials for alloys are not, however, sufficiently accurate for such studies.In this work we will consider new interatomic potentials for the 2xxx (Al-Cu-Mg), 6xxx (Al-Mg-Si), and 7xxx (Al-Zn-Mg-Cu) age-hardenable aluminum alloys. First a family of neural-network potentials (NNPs) for the Al-Cu system are presented as a simple example of a machine-learning potential that can achieve near-first-principles accuracy for many different metallurgically-important aspects of 2xxx alloys. High fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, generalized stacking fault energies and surfaces for slip in matrix and precipitates, antisite defect energies, and other quantities, are shown. The NNPs also captures the subtle entropically-induced transition between

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at temperatures around 600K. Many comparisons are made with the state-of-the-art Angular-Dependent Potential for Al-Cu, demonstrating the significant quantitative benefit of a machine-learning approach. A preliminary kinetic Monte Carlo study shows the NNP to predict the emergence of GP zones in Al-4at%Cu at T=300K in agreement with experiments.Then, a family of NNPs for the Al-Mg-Si system is developed to enable quantitative studies of 6xxx alloys. The NNP is trained on metallurgically-important quantities computed by first principles density function theory (DFT) leading to high fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, Al stacking fault energies, antisite defect energies, and other quantities. The Generalized Stacking Fault Energy surfaces (GSFE) for the three prevalent

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precipitate compositions in peak-aged Al-6xxx are then computed with the NNP, and are validated by DFT computations at key points. A preliminary examination of early-stage clustering kinetics and energetics in Al-6xxx is then made, showing the formation of low-energy Mg-Si structures and the trapping of vacancies in these clusters.Finally we demonstrate a family of NNPs to produce what we believe to be the first Al-Cu-Mg (2xxx) and Al-Cu-Mg-Zn (7xxx) potentials of sufficient accuracy to be used for atomistic simulations. These NNP studies show that the NNP has significant transferability to defects and properties outside the structures used to train the NNP, but also shows some errors highlighting that the use of any interatomic potential requires careful validation in application to specific metallurgical problems of interest.

EPFL2022

Neural Network Potentials for Age Hardening Aluminum Alloys

Daniel John Gilles Marchand

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EPFL2022