Modelling and Simulation

In an age where virtual realities shape tangible outcomes, modeling and simulation methods are more crucial than ever. Our symposium offers a deep dive into the role these methods play in both academic research and industrial applications. Engage in intricate discussions on the nexus between processing, microstructure evolution, thermodynamics, and material properties. With exhaustive coverage of everything from metals, ceramics, and glasses to polymers and composites, we promise a holistic exploration of the material world through the lens of simulation.

The symposium provides a platform for discussing the thermodynamics and kinetics of materials. The focus is on the constitution of materials and the modelling of phase transformations, aiming to provide fundamental understanding of microstructure formation and offer new information for materials design and applications in the perspective of material scientists, metallurgists, physicists and engineers.

Specifically encouraged are computational contributions on CALPHAD-type kinetic and thermodynamic databases (incl. 3rd generation) using classical expert evaluation, first-principle, Monte-Carlo and data-driven methods that allow the derivation of stable, metastable, kinetic and defect phase diagrams as well as contributions on modelling approaches with sharp and diffuse interface at the micro- and meso-scale considering solution phase thermodynamics, near-equilibrium and non-equilibrium states at the interface (rapid transformations).

Microstructural investigation of phase equilibria and pattern formation employing, e.g., X-ray diffraction, high-resolution microscopy as well as experimental results using dilatometry and calorimetry are highly welcome.

Materials of interest are metals and ceramics, respectively. However, applications related to composites, soft matter and biological materials are welcome to explore synergetic approaches. Theory and applications of thermodynamics and kinetics to describe “materials behavior in engineering systems”, e.g., for energy storage and conversion or for circular economy are also in the scope of this symposium.

The development of modern materials requires the incorporation of advanced materials modelling and simulation techniques. Computer-aided design of innovative materials through combined material simulations, operating on different scales and involving multiple physical phenomena, enables accelerated design of materials that are specialized for individual processes and applications. In particular, numerical methods based on the phase-field method have become indispensable and extremely versatile tools in materials science, microstructure mechanics and physics. The method typically operates at the mesoscopic length scale and provides important information about morphological changes in materials by mapping interfacial motions of physically separated regions. Diffuse interface parameterization can be used to model the temporal and spatial evolution of an arbitrarily complex multi-phase, multi-component and polycrystalline microstructure in materials without the need for additional assumptions about shape or mutual distribution. An outstanding feature of the phase-field method is the ability to consider different physical driving forces for interfacial motion due to diffusive, electrochemical, thermomechanical, etc. processes. In addition, large-scale numerical simulations can be performed by numerically solving the coupled multiphysics differential equations on high performance clusters. Because of this versatility, phase-field methods, used in a wide range of fields in materials science and physics, are constantly under development. To establish cross-community standards for phase-field modelling, this MSE2026 symposium aims to address following main objectives:
 

• Highlight current issues, emerging applications, and outstanding perspectives in phase field modelling
• Identify methodological commonalities between the different phase-field communities
• Discuss analytical challenges of phase-field modelling in a transparent way
• Establish benchmarks for the verification of models and implementations

The twin challenges of sustainability and digital transformation are redefining the landscape of materials modeling and design. As traditional experimental methods struggle to keep pace with the vast range of chemical components and process parameters, data-driven methodologies offer a compelling alternative. By merging physics-based insights with advanced statistical, machine, and deep learning techniques, these approaches provide a powerful framework to decode the complex links between composition, processing, structure, and properties (CPSP).

This symposium, “Data-driven exploration of CPSP linkages and materials design,” will spotlight innovative strategies that harness the full potential of integrated experimental and simulation data. Emphasis will be placed on hybrid models that combine conventional Integrated Computational Materials Engineering (ICME) with cutting-edge data analytics, enabling rapid prediction of material properties, process optimization, and tailored chemical compositions under real-world constraints. Moreover, data-driven exploration of CPSP linkages offers unique opportunities to bridge mechanics and materials science, fostering a holistic understanding of material behavior and structural performance.

Key Topics of Interest

• High-Throughput Data Preprocessing: Utilizing advanced statistical, deep, and active learning techniques to efficiently clean, structure, and analyze large-scale experimental and simulation datasets.
• Hybrid Modeling and Data Fusion: Merging diverse data sources to improve model accuracy while reducing experimental demands.
• Uncertainty Quantification: Systematically addressing inherent data variability to enhance the reliability of model predictions.
• Accelerated Process Simulation: Applying state-of-the-art computational methods to forecast microstructural evolution and processing outcomes.
• Advanced Microstructure Analysis: Leveraging 2D/3D imaging techniques and computer vision, including deep learning, for comprehensive microstructural characterization.
• Dimensionality Reduction and Feature Extraction: Developing robust strategies to establish and explore CPSP linkages.
• Adaptive and Multi-Objective Optimization: Leveraging sequential adaptive learning loops and multi-objective optimization techniques for efficient alloy design.
• High-Performance Data Management: Innovating research data systems to support extensive materials investigations.

By uniting expertise from academia and industry, the symposium seeks to bridge traditional materials science with modern data analytics — paving the way for next-generation materials engineered for a sustainable and digitally advanced future.

Grain boundaries and other interfaces exert a significant influence on the mechanical, physical, and chemical properties of polycrystalline materials. For better understanding of the complex structures and phenomena occurring at interfaces in materials during their manufacturing and service, it is essential to combine atomistic simulations with meso-scale and continuum modelling, as well as to employ machine learning approaches for uncovering the composition-structure property relationships.

The purpose of this symposium is to explore and discuss state-of-the-art applications of theoretical and computational tools that enable reliable simulations of grain boundaries and other interfaces in realistically complex materials and their properties. Multi-scale and gap-bridging approaches that seek to bring together theoretical and experimental results are of particular importance. Also of significance is the application of physics-informed machine learning (ML) techniques as well as CALPHAD-integrated studies that can capture the underlying physics and make high-fidelity predictions on interface energetics and other quantities of interest. The focus areas of computational studies include the broad microstructure evolution phenomena as in segregation phenomena, precipitate-matrix interactions, intermetallic compounds and generalized stacking fault energies, embrittlement phenomena, grain growth and recrystallization.

We warmly invite all contributions on the structure, dynamics, and thermodynamics of interfaces, as well as on their role in the property formation and evolution in metals, alloys, ceramics, multi-layered and composite materials. Symposium contributions on the implementation of ML schemes and automated or high-throughput approaches to address fundamental questions in interface properties are also welcomed.

The development of advanced materials for energy and environmental applications is vital to achieving a sustainable future. While renewable energy technologies continue to evolve, there is also a growing need for materials that address environmental issues such as pollution, water purification, and waste management.

Traditional trial-and-error methods in materials development are often slow and costly. To accelerate discovery, researchers now combine simulations—such as DFT, molecular dynamics, and finite element analysis—with machine learning (ML). ML enables high-throughput screening, property prediction, and inverse design, offering a powerful complement to conventional modeling.
In environmental research, ML has been applied to optimize materials like biochar for pollutant removal and heavy metal remediation, demonstrating the potential of data-driven methods in real-world applications.

This symposium welcomes contributions on the use of simulations and ML in designing and studying materials for energy and environmental purposes, with a focus on theory–experiment integration and cross-disciplinary approaches.

Topics include:

• Modeling methods for energy and environmental materials
• High-throughput computational screening
• Machine learning for property prediction and materials design
• Mechanisms of energy conversion and pollutant remediation
• Integration of experimental and computational approaches