Ref: David Holec, CC BY-SA 4.0

Multiscale Materials Modelling

At the heart of understanding material behavior lies the examination of phenomena occurring at diverse length and time scales. Historically, individual disciplines—ranging from quantum mechanics to continuum mechanics—have specialized in exploring specific scales, from atomic and molecular up to macroscopic levels.

Multiscale Materials Modelling endeavors to bridge these scales, employing a hierarchical approach to couple models from the finest atomic scales to broader macroscopic scales. At the atomic level, electronic structures dictate material properties, and understanding the positions of atoms and their underlying potentials is crucial. As we transition to larger scales, atomistic models are often too computationally expensive, necessitating the use of alternate representations which can capture the essence of the material behavior without delving into atomic detail.

This upscaling is a meticulous process, requiring the development of transition models that maintain the continuity of local physical variables, such as displacement or temperature. Such continuity ensures that the physics at one scale transition seamlessly into the next.

Additionally, a central challenge in multiscale modelling is uncertainty quantification. As we derive coarse-scale properties from finer-scale models, there's inherent uncertainty involved. Quantifying this uncertainty is pivotal, as the fidelity of the macroscopic model depends on the accuracy and reliability of its foundational microscopic models.

Modeling Microscale Defects in Aluminum: A Comprehensive Static and Dynamic Approach

Objective: The core aim of our research endeavor is to devise an in-depth model tailored for the understanding and representation of microscale defects inherent in aluminum structures. By skillfully integrating both static and dynamic coupling techniques, we set out to create a tool of unmatched efficiency and accuracy.

Methodology: To address the challenges posed by sub-micron scale defects, we employed the quasicontinuum method. This method permitted an intricate representation and analysis of the region immediately surrounding the defect. For areas farther away from these defects, the time-tested principles of continuum mechanics were invoked to provide an encompassing perspective on the material behavior.

Further detailing our methodology:

Key Findings:


Deep Material Network: An Innovative Approach to Composite RVE Representation

Objective: The primary aim of our research was to comprehensively study the Deep Material Network technique—a pioneering approach that synergizes principles from physics and machine learning to efficiently characterize a composite Representative Volume Element (RVE).

Methodology: Utilizing the foundational concepts of the Deep Material Network, we embarked on an exploration to discern its capabilities. In particular, our focus was on its adeptness at capturing the morphology of composite materials. During the course of our investigation, we introduced modifications to its standard implementation.


Implications: The implications of our research are profound, particularly for sectors reliant on composite material analysis. With the ability to provide accurate results in significantly reduced timeframes, the enhanced Deep Material Network can revolutionize efficiency metrics in composite RVE analysis and potentially pave the way for further advancements in the integration of physics and machine learning.

Selected Publications:

Anay Mohan Shembekar, S. Gopalakrishnan,  Atomistic and continuum length scale coupling in materials using quasicontinuum method,  Materials Today: Proceedings (2023)  Link to article