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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Materials
Presentation Title Graph Mining in Materials Science for the Prediction of Material Properties
Author(s) Mehrdad Jalali, Christof Wöll
On-Site Speaker (Planned) Mehrdad Jalali
Abstract Scope There has been a rapid growth in the number and application of new materials. Today, increased ‎computational power and the established use of automated machine learning approaches make the ‎data science tools available, which provide an overview of the chemical space, support the choice of ‎appropriate materials, and predict specific properties of materials for the desired application. Among ‎the different data science tools, graph theory approaches, where data generated from numerous real-world applications are represented as a graph (network) of connected objects, have been widely used in ‎various scientific fields. In this work, we describe the application of a particular graph theory approach ‎known as graph mining to predict material properties based on a constructed graph in the ‎metal-organic framework (MOFs) and high-entropy alloys (HEAs) materials. The results show a ‎significant improvement in predicting properties in both fields.‎
Proceedings Inclusion? Planned:
Keywords Machine Learning, Characterization, High-Entropy Alloys

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Computation Discovery of Materials for Solid-state Batteries
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Data- and Physics-driven Approaches to Discovering the Governing Equations for Complex Phenomena in Heterogeneous Materials
Design and Development of High Strength High Conductivity Alloys using ICMD® Approach
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Electronic and Structural Properties of Ab-initio Predicted BxAl1-xN Alloy Structures
Elucidating the Mechanisms for Fast Diffusion in Doped LLZO
Exploiting First-principles Based Interpretation of X-ray Absorption Spectra of Ni, Cr, Fe Elements in Molten-salt System
Graph Mining in Materials Science for the Prediction of Material Properties
M-16: Building an ImageNet for Materials Grain Boundaries
M-17: Generative Adversarial Networks and Diffusion Models in Material Discovery
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