Transformer-Based Crystal Structure Generation from OTC and Chemical Composition

Abstract

Multi-component oxides, composed of three or more elements, offer a vast combinatorial space of possible structures with tunable properties such as thermal stability, ion conductivity, and catalytic activity. Exploring this space using traditional trialand- error methods is time-consuming and expensive. This thesis investigates the use of a Transformer-based language model to generate Crystallographic Information Files (CIFs), which encode atomic positions, lattice parameters, and symmetry elements. The model is trained to learn relationships between structural features and material properties, allowing it to propose new CIFs representing potential novel crystal structures based on input descriptors like oxygen transfer capacity and composition. The results show that the Transformer model can capture complex structural patterns and generate valid CIF sequences, demonstrating its potential as a data-driven tool to accelerate the discovery and design of multi-component oxides.