Computational materials science

Summary

Computational materials science and engineering uses modeling, simulation, theory, and informatics to understand materials. The main goals include discovering new materials, determining material behavior and mechanisms, explaining experiments, and exploring materials theories. It is analogous to computational chemistry and computational biology as an increasingly important subfield of materials science. Introduction Just as materials science spans all length scales, from electrons to components, so do its computational sub-disciplines. While many methods and variations have been and continue to be developed, seven main simulation techniques, or motifs, have emerged. These computer simulation methods use underlying models and approximations to understand material behavior in more complex scenarios than pure theory generally allows and with more detail and precision than is often possible from experiments. Each method can be used independently to predict m

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Atomistic modeling of the solid-liquid interface of metals and alloys

Edoardo Baldi

Solidification is a phase transformation of utmost importance in material science, for it largely controls materials' microstructure on which a wide range of mechanical properties depends. Almost every human artifact undergoes a transformation that leads to a solid phase, be it via well-established manufacturing processes as casting or forging, or more recent technological advances such as 3D printing. This thesis aims to study some fundamental aspects of solidification, focussing in particular on metals and alloys. Despite being a phenomenon investigated for a long time with both experiments and theoretical models, solidification still involves a very complex set of phenomena that requires a multi-scale approach to provide useful insights. For example, properties relating to the thermodynamics and kinetics of solid-liquid interfaces play a crucial role in micro-scale modeling of solidification, yet are particularly challenging to assess with experimental techniques. Our main instrument of investigation has been a set of well-established computer simulation techniques at the atomic scale, and we applied and extended some of these methods, with the primary goal of improving the reliability of some results related to the properties of solid-liquid interfaces and being able to study systems whose level of complexity comes closer to that of interest to some real manufacturing processes. The approach of atomistic simulations involves several technical and theoretical problems. A first issue is related to the size of the systems that it is necessary to simulate to obtain results of some relevance despite an inherent statistical error that is often substantial. Moreover, some theoretical subtleties, such as the arbitrariness in the definition of the interface, inevitably emerge from an approach at the atomic scale, and they lead to problems whose solution is anything but univocal; often, in fact, different formulations of these problems provide different results. The first contribution of this thesis focused on extending a computational method that serves to calculate a fundamental quantity known as interface free energy and, in particular, to decrease its computational cost by reducing the number of particles of the simulated systems. The second contribution addressed the study of crystal-melt interface properties of a particular metallic binary alloy. The idea behind this part of the work was to combine different techniques of atomistic simulations whose outcomes make it possible to obtain an exhaustive description of both the thermodynamics and the dynamics of the interfaces. We have based this approach on a precise definition of dividing surface, and we have derived all our results in a consistent way allowing us to eliminate some arbitrary choices that similar kinds of simulations usually entail. A part of the results obtained confirmed the reliability of our approach, showing satisfactory agreement with some other established results. However, challenges remain associated with the accuracy of the interatomic potential, the presence of significant finite-size effects, and the difficulty in converging to satisfactory statistical accuracy the thermodynamic and dynamical properties of solid-liquid interfaces.

EPFL2020

Age hardening induced by the formation of (semi)-coherent precipitate phases is crucial for the processing and final properties of the widely used Al-6000 alloys despite the early stages of precipitation are still far from being fully understood. This crucial step in the technology of Al-based alloys is studied by means of multi-scale simulations that include first-principles atomistic modeling, surrogate models based on statistical learning, as well as kinetic Monte Carlo and continuum elasticity models to bridge time and length scales. We begin with an analysis of the energetics of nanometric precipitates of the meta-stable beta'' phases (that play a crucial role in this system) identifying the bulk, elastic strain and interface energies that contribute to the stability of a nucleating cluster. Results show that needle-shape precipitates are unstable to growth even at the smallest size beta'' formula unit. This study made it possible to develop a semi-quantitative classical nucleation theory model, including also elastic strain energy, that captures the trends in precipitate energy versus size and composition. This validates the use of mesoscale models to assess stability and interactions of beta'' precipitates. Studies of smaller 3D clusters also show stability relative to the solid solution state, indicating that the early stages of precipitation may be diffusion-limited. Our results thus point toward the need for a systematic study of the energetics of aggregates in the Guinier-Preston zone regime, and the interactions between those aggregates and vacancies and/or trace elements to understand and fine-tune the behavior of Al-6000 alloys in the early stages of precipitation. To enable full atomistic-level simulations of the whole precipitation sequence of this important alloy system, two Neural Network (NN) potentials have been created by representing just 2-body interactions and including also the 3-body interactions. For the latter, we developed an automatic scheme to determine the most appropriate representation of the structural features of this ternary alloy. Training of the NN uses an extensive database of energies and forces computed using Density Functional Theory, including complex precipitate phases. The NN potentials accurately reproduce most of the properties of pure Al which are relevant to the mechanical behavior and formation energies of small solute clusters and precipitates that are required for modeling the precipitation and mechanical strengthening. This success not only enables future detailed studies of Al-Mg-Si but also highlights the ability of machine learning methods to generate useful potentials in complex alloy systems. Finally, we used this NN potential to implement a kinetic Monte Carlo scheme to study the formation of pre-precipitation clusters. While quantitative accuracy will probably require further refinement of its training set, to achieve a more complete description of the interactions between solute atoms and vacancies, we could already observe some of the key mechanisms the determine the ultra-fast formation of aggregates. This work lays the foundations for a thorough investigation of the behavior of Al-6000 alloys over time and size scales that are technologically relevant and demonstrates a combination of atomistic modeling techniques that could be adapted to a large number of similar metallic alloys.

EPFL2018

Impact of oxygen plasma on nitrided and annealed atomic layer deposited SiO2/high-k/metal gate for high-voltage input and output fin-shaped field effect transistor devices

Sandip De

In this study, the authors investigate the impact of radical oxygen plasma on nitrided and annealed atomic layer deposited (ALD) SiO2 as a thick gate oxide (1.65-3 V) with a high-k/metal gate transistor. Time-dependent-dielectric-breakdown voltage, secondary ion mass spectroscopy (SIMS), and x-ray photoelectron spectroscopy (XPS) studies were conducted, and the results are discussed for nitrided and annealed ALD SiO2 with and without radical oxygen plasma exposure. Atomistic material simulations were performed to understand the reactions between oxygen radicals and silicon oxynitride (SiON). Our key findings from SIMS and XPS show that radical oxygen plasma exposure led to a 34% nitrogen loss from the thick SiON gate oxide and damaged the gate oxide, resulting in a 1V reduction in the dielectric breakdown voltage. Atomistic material simulation results also show that atomic oxygen can react with Si-(ON)Si to form Si-O-Si bonds and mobilize NO into the interstitial space. Similarly, when O atoms and O-2 molecules are placed near N clusters, spontaneous diffusion of N-2 into the interstitial space occurs. Diffusion barrier calculations further indicate a barrier energy of less than 0.5 eV in SiO2 and SiON, which can lead to the out-diffusion of NO and N-2 from SiO2 and SiON. These compositional changes may result in increased leakage and a degraded breakdown voltage. The increased gate leakage and degraded breakdown voltage is attributed to the compositional changes in SiON. (C) 2017 American Vacuum Society.

A V S Amer Inst Physics2017

Atomistic modeling of the solid-liquid interface of metals and alloys

Edoardo Baldi

EPFL2020

EPFL2018