VIRMETAL

Multiscale modelling strategies for designing new materials The discovery of materials with novel or enhanced properties is central to technological progress, opening new possibilities across many areas of industry. The VIRMETAL project aims at accelerating this process by means of multiscale modelling strategies that will enable scientists to design, process and test advanced metallic alloys in silico before they are manufactured, as Professor Javier Llorca explains The development of

new materials plays a central role in technological innovation. There are many instances throughout history where the synthesis of a material with novel properties has led on to significant technical breakthroughs. This is the case, for instance, with the synthesis of multilayers with giant magnetoresistance, which led to a dramatic increase in the capacity of hard disk drives.

An alternative route to technical progress is through the progressive improvement of existing engineering materials for novel applications, as has been seen in superalloys and composites in the aerospace industry for example.

In practice, both of these routes act as a brake on technological progress, yet recent developments in modeling tools, along with advances in multiscale modeling strategies and continued increases in computational power, promise to open up new possibilities to accelerate the discovery and design of new materials for engineering applications.

A wide range of modeling tools are available nowadays to simulate the behavior of materials for particular length and time scales, including density functional theory, molecular mechanics, computational thermodynamics, finite elements, etc.

These techniques have already been used to design materials with improved properties or unexpected structures, such as new catalysts or Lithium (Li)-based materials for batteries. However, this is only possible because the critical structure or properties depend on phenomena which take place at particular time and length scales, which can be simulated using just a single one of the aforementioned techniques.

This is not always the case, and in fact it is unlikely to be observed in materials intended for structural applications. Balanced mechanical properties like stiffness, strength and toughness are dependent on many different processes which take place along nine or more orders of magnitude in length scales, from nanometers to meters. This dependence on different length scales is even more apparent in multifunctional (smart) materials.

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VIRMETAL Project This challenge forms the backdrop to the work of the VIRMETAL project, an ERCbacked initiative which aims to develop novel multiscale modeling strategies to carry out virtual design, virtual processing and virtual testing of advanced metallic alloys for engineering applications.

The ultimate goal in the project is to enable the design, testing and optimization of new metallic alloys in silico before they are actually manufactured in a laboratory, which would dramatically reduce the time necessary to discover and incorporate new materials in industrial applications.

Magnesium) and Mg-Al-Zn (MagnesiumAluminum-Zinc) systems, both of which hold considerable industrial interest.

Some exciting results have already been achieved. For instance, a multiscale modelling strategy has been developed to predict the homogeneous and heterogeneous nucleation of θ’ (Al 2Cu) precipitates in an Al-Cu alloy during high temperature aging. The model parameters that determine the different energy contributions (chemical free energy, interfacial energy, lattice parameters, elastic constants) were obtained from computational thermodynamics or first-principles density functional theory.

Researchers aim to demonstrate that multiscale modelling can be used to predict the microstructural development during solidification and thermomechanical processing, as well as to extend the virtual testing capabilities to include damage and fracture Nevertheless, not everything can or should be computed, and critical experiments constitute an integral part of the research program for the calibration and validation of the multiscale strategies.

Research is focused on two metallic alloys from the Al-Cu-Mg (Aluminum-Copper-

From this information, the evolution and equilibrium morphology of the θ’ precipitates is simulated in 3D using the phase-field model. The model was able to reproduce the evolution of the different orientation variants of plate-like shaped θ’ precipitates with orientation relationship

Figure 1. (a) Multiscale simulation of the nucleation and growth of θ’ (Al2Cu) precipitates on dislocations during high temperature aging of an Al-Cu alloy. (b) Transmission electron microscopy micrograph showing the formation of a staircase structure of θ’ precipitates on a dislocation.

(From H. Liu, B. Bellón, J. LLorca. Acta Materialia 132 (2017) 611-626.)

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(001)θ’//(001)α and [100]θ’//[100] α during homogeneous nucleation as well as the heterogeneous nucleation on dislocations, leading to the formation of precipitate arrays (Fig. 1). Heterogeneous nucleation on pre-existing dislocations was triggered by the interaction energy between the dislocation stress field and the stress-free transformation strain associated to the nucleation of the θ’ precipitates. Moreover, the mechanisms controlling the evolution of the morphology and the equilibrium aspect ratio of the precipitates were ascertained.

All the predictions of the multiscale model were in good agreement with experimental data, demonstrating the capability of the bottom-up multiscale approach to predict the structure of the material from first principles data.

The next step once the precipitate structure has been obtained is to predict the hardening induced by their presence. This can be achieved by means of dislocation dynamics simulations in which a dislocation has to propagate through a forest of precipitates. The lattice parameters, elastic constants and stress-free transformation strains of the precipitates were obtained by ab initio calculations, while molecular dynamics simulations were used to determine the dislocation mobility.

Thus, the multiscale simulations to predict the mechanical properties of the alloy are, again, based on information obtained from simulations at lower length scales.

The information obtained from the dislocation dynamics simulations can be used to develop a dislocation-based crystal plasticity model that can be used to simulate the behaviour of polycrystals. These models can take into account the storage of dislocations at grain boundaries and so can be used to predict the strengthening of

polycrystals as a function of the grain size (known as the Hall-Petch effect)[Fig. 2a].

The experiments and simulations again show close agreement, and the mechanism responsible for the grain size strengthening – the accumulation of dislocations at the grain boundaries – is clearly revealed in the contour plot of the dislocation density in Figure 2b.

This work is ongoing, with researchers aiming to demonstrate that multiscale modelling can be used to predict the development of a microstructure during solidification and thermo-mechanical processing, as well as to extend the virtual testing capabilities to include damage and fracture, both of which are major concerns to industry.

Expected outcomes The wider goal is to demonstrate that the structure and properties of two standard engineering alloys can be obtained from first principles by bridging a cascade of modeling tools at the different length scales. Once this has been proven, further research will lead to continued growth in the number of multiscale simulation tools, as well as the extension of their capabilities.

This holds important implications for both academia and industry, enabling the virtual design, processing and testing of new materials before they are manufactured, saving money and helping researchers to identify potential applications of a new material at a very early stage in development.

Once the benefits of these simulation tools become more apparent, it is expected that they will be widely applied across many important areas of European industry, including in the aerospace, automotive, rail transport, energy generation and engineering sectors.

VIRMETAL Virtual Design, Virtual Processing and Virtual Testing of Metallic Materials Project Objectives

The VIRMETAL project aims to develop multiscale modelling strategies to carry out virtual design, virtual processing and virtual testing of advanced metallic alloys for engineering applications, so that new materials can be designed, tested and optimised, before they are actually manufactured in the laboratory.

The focus of the project is on materials engineering, namely understanding how the structure of the material develops during processing, the relationship between this structure and the properties, and how to select materials for a given application.

Project Funding

The VIRMETAL project is funded under H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Advanced Grant.

Contact Details

Project Coordinator, Professor Javier Llorca Calle Eric Kandel 2 Tecnogetafe, 28906 Getafe, Madrid, Spain T: +34 91 549 34 22 E: javier.llorca@imdea.org W: http://materials.imdea.org/proyecto/ virmetal/

Professor Javier Llorca

Professor Javier Llorca is Scientific Director at IMDEA Materials Institute and Professor at the Technical University of Madrid. His main research interests lie in establishing the relationship between processing, microstructure and mechanical behaviour of materials at different length scales by means of the development of modelling tools and multiscale simulation strategies.

Figure 2. (a) Experimental results and multiscale modelling predictions of the flow stress of Cu as a function of the inverse of the grain size, 1/dg (mm-1) for different values of the applied strain, ε = 0.5% and 5%. (b) Contour plot of the dislocation density (m-2) showing the storage of dislocations around the grain boundaries during deformation in a Cu polycrystals with an average grain size of 10 µm. (From S. Hauoala, J. Segurado, J. LLorca. Acta Materialia 148 (2018) 72-85.)

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