Symposia

This symposium explores recent advances in quantum mechanical approaches for understanding and predicting materials behavior at the electronic and atomic scales. Topics cover first-principles electronic structure approaches, advances in density functional theory, quantum many-body methods, and emerging quantum simulation techniques, together with related progress in multiscale modeling and quantum computing for materials research. A special honorary session will celebrate the scientific legacy of Jisoon Ihm and his pioneering contributions to computational materials physics.

This symposium will focus on how artificial intelligence is reshaping the discovery and design of materials across atomistic, microstructural, and process-informed representations. We welcome contributions on graph and foundation models for property prediction, generative and inverse design of compositions and structures, active learning and Bayesian optimization, and closed-loop strategies linking simulation, data, and experiment. Particular emphasis is placed on methods that connect first-principles calculations, multiscale modeling, and experimental data in a scientifically rigorous manner. Topics of interest include uncertainty quantification, interpretability, physics-informed learning, small-data and out-of-domain generalization, and AI for synthesis-aware or processing-aware materials design. Contributions should go beyond black-box prediction and demonstrate mechanistic relevance, quantitative validation, or experimentally actionable guidance. Applications may span structural, energy, electronic, catalytic, quantum, and soft materials. The symposium is intended for researchers developing the next generation of AI-enabled approaches for materials discovery, screening, optimization, and design within the broader MMM community.

Understanding the underlying mechanisms of additive manufacturing (AM) through computational materials engineering necessitates to materials design and process optimisation. Prediction of microstructure variation using microstructure modelling induced by the localised liquid/solid reaction can facilitate process design for heat source-materials interaction process. Better understand of property scatter induced by relevant microstructures can be used as a science-based tool for part scale simulation. The multiscale metallurgy-based simulations lead to development of data-driven modelling to unlock the materials–process–structure–property relationships. Key topics and methods

• Compositional design using atomistic simulations to understand materials property for AM;
• Modelling of AM processes, including, but not limited to, powder-bed fusion AM, directed energy deposition, materials extrusion, binder jetting using computational fluid dynamics and also powder dynamics methods or similar;
• AM-induced microstructure simulation using phase-field, cellular automata finite element calculation and/or statistical approaches coupled with metallurgical thermodynamics calculation;
• Multi-scale micro-/macroscopic computational mechanics using dislocation dynamics and/or crystal plasticity models;
• Reduced-order or data-driven modelling framework development for accelerating the close loop control and digital twin for AM processes;
• Machine learning and artificial intelligence approach to unlock the MPSP relations and design for new materials for AM.

The transition toward sustainable energy systems requires the accelerated discovery and optimization of functional materials for energy harvesting, conversion, and storage. However, the performance of these energy materials is governed by a complex interplay of electrochemical, mechanical, and thermal phenomena that span multiple length and time scales. This symposium aims to bring together researchers utilizing multiscale modeling and data-driven approaches to understand, predict, and design next-generation energy materials. We seek to bridge the gap between fundamental atomistic mechanisms (such as ion transport and charge transfer) and macroscopic device performance, while addressing challenges like material degradation and interfacial dynamics. Topics spanned by the Symposium include:

• First-principles (DFT) and atomistic simulations of energy materials (e.g., solid-state batteries, fuel cells, photovoltaics, thermoelectrics, and catalysts).
• Mesoscale, phase-field, and continuum modeling of microstructural evolution, mechanical degradation, and transport phenomena during operation.
• Integration of machine learning and AI-driven workflows for the accelerated screening, property prediction, and inverse design of energy materials.
• Multiscale approaches linking nanoscale interfacial chemistry to macroscopic electrochemical and mechanical responses.
• Validation of multiscale computational models with advanced experimental characterization techniques.

This symposium focuses on multiscale computational modeling and data-driven approaches for soft material systems, including polymers and their composites. Core topics include quantum and molecular simulations, coarse-grained modeling, mesoscale and hierarchical multiscale frameworks, and continuum and micromechanics-based approaches. Particular emphasis is placed on coarse-grained modeling as a key enabler for bridging molecular-level physics to mesoscale behavior and systematically exploring chemical and structural design spaces. The symposium also highlights emerging artificial intelligence and machine learning techniques for property prediction and inverse design. By integrating physics-based and data-driven methodologies, this session aims to advance predictive capabilities and uncover structure–property relationships in soft materials. It is intended for researchers in materials modeling, mechanics, and computational materials science, and provides a coherent platform for state-of-the-art developments at MMM12.

Properties of structural materials depend on their microstructure, involving point defects, defect clusters, dislocations, grain boundaries, and phase inclusions. Microstructures predicted by multiscale models are then compared to real-space observations, using various high-resolution diffraction and electron microscopy techniques. The interpretation of microscopic observations is complex and indirect, whereas applications require algorithms for generating 3D microstructures across scales, reinforced by uncertainty quantification. This symposium aims to promote the development of multiscale atomistic and mesoscale models for generating microstructures of metals and alloys as well as for reconstructing microstructures from experimental data. We would like to define scientific criteria for analysing microstructural data, their comparability, as well as trends in microstructural evolution in a variety of operating environments. Topics spanned by the Symposium include

• Fundamentals of simulation and experimental observation, visualisation, reconstruction and interpretation of defect and dislocation microstructures in metals and alloys
• Simulation versus reality in the interpretation of electron microscope images and other experimental representations of complex microstructures
• Representation of microstructural data for fast multiscale HPC simulations
• Algorithms and applications of AI-generated synthetic microstructures
• Uncertainty Quantification of simulated and reconstructed microstructures, and microstructural evolution models

Multi-principal element alloys (MPEAs) have emerged as a prominent class of structural materials owing to their exceptional mechanical properties and outstanding compositional tunability. However, the vast compositional space and complex interatomic interactions lead to highly heterogeneous microstructures and intricate plastic deformation mechanisms across multiple length and time scales. To achieve a fundamental, predictive understanding of their mechanical behavior, multiscale modeling frameworks that integrate atomistic, mesoscale, and continuum descriptions are indispensable. This symposium aims to gather researchers from diverse backgrounds to bridge modeling and experimental scales, elucidating the underlying physical mechanisms of plasticity in MPEAs. Contributions covering recent advances in multiscale modeling, defect dynamics, chemical short-range order, microstructural evolution, and extreme-environment behavior (including nuclear/radiation conditions) are highly encouraged. Topics also include modeling-guided materials design, experiment-computation integration, and machine-learning-assisted performance prediction for MPEAs.

The "Hydrogen Economy" faces multiscale material challenges, from embrittlement in infrastructure to chemo-mechanical coupling in green metallurgy. This symposium gathers with the global community to discuss high-fidelity simulations of H-defect interactions alongside experimental characterization of H-storage and resistant alloys. We strongly welcome contributions spanning theory, simulation, and experiments, to bridge materials microstructures and performances with predictive models. By integrating multiscale modeling with multiphysics frameworks and techno-economic assessments, we aim to connect sub-atomic behaviors with macroscopic industrial performance. Key topics cover fracture mechanics, diffusion kinetics, and H-based metal extraction, targeting researchers in materials design, characterization, and sustainable manufacturing.

This symposium focuses on the multiscale design of sustainable structural materials that achieve high mechanical performance with minimal reliance on alloying. Targeting compositionally simple yet microstructurally complex metals and alloys, it explores how intrinsic defect configurations, such as dislocation and grain-boundary or interface networks, govern mechanical behavior. Core topics include defect‑based property tailoring, dislocation dynamics, dislocation-interface interactions, and microstructural evolution during processing. Emphasis is placed on contributions employing continuum/discrete dislocation dynamics, atomistic simulations, phase‑field methods, crystal plasticity, and microstructure evolution modeling, along with complementary experimental characterization and processing to advance predictive, defect‑informed design of sustainable materials.

Multiscale simulations have traditionally followed a bottom-up approach, using data from finer scales to inform the construction of models at a coarser scale. While MMM is a testament to the success of these approaches, the rise of AI/ML-enhanced modelling techniques now promise quantitative accuracy across scales or automated information extraction from modalities such as text/images. To fully realize this promise we need new techniques to incorporate coarse scale or multimodal data in lower-scale simulations and provide informative error bounds for multiscale, sampling-intensive predictions. This symposium brings together leading experts from physics, chemistry and machine learning to present novel approaches for bidirectional information transfer across scales. We welcome submissions from researchers working on data-intensive multiscale problems in both simulation and experimental domains.
Key topics:

1. End-to-end differentiable simulations, top-down learning
2. Multi-scale propagation of uncertainty
3. Surrogate models for sampling intensive calculations
4. AI/ML schemes incorporating multimodal data (e.g. literature, images, timeseries)
5. Design of multiscale features
6. Agentic / autonomous workflow integration

A range of X-ray, neutron, and electron scattering methods are routinely employed to investigate microstructures across multiple scales, from atomic to mesoscopic and up to the bulk level. These methods have long been utilized for the purpose of developing and validating models of engineering materials using process-structure-property-performance (PSPP) relationships. As the capabilities of scattering techniques have advanced in parallel with increased computational power, the boundaries separating experiment, analysis, and modeling have begun to blur. This symposium will explore the paradigm through recent advances in:

• The experimental support of multiscale modeling using scattering techniques
• The interpretation of scattering experiments using multiscale modeling
• The integration of models and scattering experiments (with an eye toward real-time, model-driven experiments that probe PSPP relationships)

In this ever-developing world, the hunger for energy is increasing. Most of the energy in today’s world is spent on the continuous production of drinking water, heating, cooling applications and power generation. Among all these processes, thermal energy is either the source or rejected. Understanding the transfer, conversion and storage of heat and mass, is therefore critical for optimizing these corresponding facilities to have a shorter operation duration, a higher conversion efficiency, and a larger storage capacity. In this session, we will comprehensively discuss the recent advances in fundamentals and technologies (including both modeling and experiments) for the transfer, conversion and storage of heat and mass.

Complex concentrated alloys and related multi-principal element alloys, spanning chemically disordered to ordered states, have attracted increasing interest due to their exhibited extraordinary mechanical performance and radiation tolerance. Yet, the local chemical complexity, chemical short-range ordering, and processing-induced heterogeneities strongly modulate defect formation, migration, and interaction, making defect kinetics and microstructural evolution difficult to predict under coupled extreme environments. This symposium will highlight emerging computational or experimental approaches that enable quantitative, mechanism-based descriptions of defects and their evolution during deformation or irradiation. Particular emphasis will be placed on how chemical order and non-equilibrium microstructural heterogeneity govern deformation mechanisms, irradiation response, and property degradation or enhancement.

• Chemical order/disorder and defect-controlled phase evolution: precipitation, ordering transitions, segregation, and radiation-induced/assisted precipitation in CCAs and metals
• Coupled extremes and non-equilibrium defect dynamics: irradiation, stress, high temperature, extreme strain rate; twinning, faulting, and dislocation-mediated plasticity
• Multiscale and data-driven modeling: DFT/first-principles, atomistic simulations, kinetic Monte Carlo (kMC), rate theory/cluster dynamics, crystal plasticity, and machine learning for defect energetics and kinetics
• Advanced characterization and in situ methods: in situ TEM/ion irradiation, 4D-STEM and strain mapping, APT, synchrotron/neutron techniques, and defect spectroscopy
• Structure-property links under irradiation and deformation: strengthening mechanisms, defect accumulation and recovery, swelling/creep, embrittlement, fatigue, crack initiation

The goal of this symposium and associated Special Collection in the Journal of Materials Science: Materials Theory is to bring together the crystalline interface research community, provide a vessel for advancing understanding, and to stimulate discussion around current and future directions for the field. The symposium and Special Collection welcome contributions on any topic related to theory and modeling of crystalline interfaces, including but not limited to:

• Atomistic, phase field, and continuum modeling of crystalline interfaces
• Experimental comparisons with predictions from theory and modeling
• Grain boundary phases/complexions, their properties, and transitions
• Thermodynamics of interfaces
• Interfacial migration mechanisms and kinetics
• Line defects in interfaces
• Atomic structure of interfaces and its relation to theory/modeling

This symposium will focus on studies of defect segregation in crystalline materials to understand the role of solute atoms on the mechanical properties of materials. Segregation of a secondary species (including alloying elements or interstitials such as oxygen, hydrogen or helium) occurs across a variety of length scales leading to heterogeneities of elements and properties. Segregation to surfaces, grain boundaries, dislocations, and stacking faults can greatly affect the mechanical, chemical, thermal and electrical performance of materials, both positively or negatively depending on the application. A detailed understanding of the relationships between solute and solvent atoms that lead to segregation can enable design of novel materials with tailored response including, for example, alloys that are strong and lightweight, or resistant to corrosion or hydrogen/helium bubble formation. This symposium welcomes contributions from all length scales, including quantum density functional theory, classical atomistic simulations, mesoscale simulations (e.g., kinetic Monte Carlo, dislocation dynamics, phase field) and finite element modeling.

This symposium focuses on the critical role of mesoscale modeling in capturing the complex microstructural evolution that dictates macroscopic material properties. We invite contributions that advance the state-of-the-art in computational and theoretical approaches, specifically spanning from Monte Carlo (MC) and Kinetic Monte Carlo (KMC) methods to Phase-field (PF) modeling. A particular emphasis is placed on the integration of first-principles or atomistic-level calculations, multi-physics coupling, and machine learning-assisted acceleration to enhance the predictive power of mesoscale simulations. Core topics include grain growth, recrystallization, solidification, and other related kinetic phenomena as they manifest during diverse material processing stages in systems such as structural alloys, ceramics, and electronic materials. By highlighting novel algorithms and high-fidelity models, this session aims to address the fundamental challenges in linking manufacturing parameters with microstructural outcomes. The symposium is intended for researchers developing the next generation of modeling approaches who seek to leverage multiscale techniques to accelerate material innovation.