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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Prediction of Formation Energies of XYB₁₄ Boron-Rich Borides By Machine Learning

Author(s)

Rıza Uğur Akbulut, Irmak Sargin

On-Site Speaker (Planned)

Rıza Uğur Akbulut

Abstract Scope

Boron-rich borides with the XYB₁₄ formula are notable for their high hardness, low density, thermal stability, and Seebeck coefficient, making them promising for high-temperature thermoelectric applications. However, their production is difficult due to high melting points, complicating the discovery of new compositions. This study aims to accelerate the selection of stable compositions by predicting formation energies through machine learning. The dataset includes formation energies and effective charges from literature, along with boron-to-metal ratio, valence electrons, ionization energy, atomic radii, and shell energies of X and Y elements from materials databases. Effective charge, critical for icosahedral stability, is first predicted, then used to estimate formation energies. A supervised multivariable regression model with nested cross-validation achieved 7.5% MAPE in effective charge and 0.3 eV/atom MAE in formation energy. The model was used to screen 38,416 XYB₁₄ compositions, enabling rapid discovery of suitable X and Y elements and their corresponding stoichiometries.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning,

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