Interpretable Representations in Materials AI
The FAIRmat Seminar series continues with a talk by Prof. Abhishek K. Singh, Chair at the Materials Research Centre, Indian Institute of Science.
Abstract:
Materials representation across multiple length scales is essential for enabling AI models to solve structure-to-property prediction problems in complex systems such as superalloys. Properties like Vickers hardness are primarily governed by microstructural features, while formation energy correlates to atomic arrangements, making it crucial to capture relevant information from different structural hierarchies. Accurate and interpretable representations across these scales allow machine learning approaches to accelerate materials discovery and design.
At the microstructural level, three frameworks are used to represent image-based information: (1) statistical representations using 2-point spatial correlations to capture phase distribution patterns, (2) geometry-driven image processing techniques that extract morphological descriptors such as area, perimeter, and shape of precipitates, and (3) deep learning models like convolutional neural networks that automatically learn hierarchical features directly from raw SEM images. These image-derived features are combined with metadata such as composition and processing history to predict mechanical properties such as Vickers hardness.
At the atomic level, graph-based representations like the CLEAR (Chemistry and Local Environment Adaptive Representation) descriptor-based model represent crystal structures by combining elemental properties with interatomic distances through Voronoi-based neighbours. By applying pooling operations, these graph features are transformed into fixed-size vectors that enable predictive modelling of formation energy and phase stability.
1. Acta Mater., 196, 295-303 (2020)
2. J. Mater. Sci. 55, 15845 (2020)
3. Acta Mater., 276, 120-122 (2024)
4. Comput. Mater. Sci (2025), 113854
Join us for this engaging discussion in person at the Center for the Science of Materials Berlin or online.
No registration is required for in-person attendance. To receive the Zoom link for online participation, please complete the registration form.