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Meeting

2027 TMS Annual Meeting & Exhibition

Symposium

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

Presentation Title

Interpretable Machine Learning and Topological Descriptors for Structural Evolution, Interfaces, and Dopant Thermodynamics: A Halide Perovskites Case Study

Author(s)

Ayush Kumar Pandey, Abhishek Tewari

On-Site Speaker (Planned)

Ayush Kumar Pandey

Abstract Scope

In this study of halide perovskites, we apply atom-specific persistent homology (ASPH) to investigate structural evolution, phase interfaces, and dopant thermodynamics. While classical geometric metrics often fail to resolve complex local distortions, ASPH maps topological connectivity to capture underlying structural physics. We demonstrate that these topological invariants rigorously correlate with macroscopic volume during the CsPbI3 δ-to-γ transition, providing superior phase discrimination. Spatially profiling a δ-γ heterostructure isolates the exact phase boundary achieving superior performance compared to state of art descriptors such as SOAP while being interpretable and identifies localized structural precursors to nucleation within the metastable lattice. Furthermore, deploying this descriptor on Cd- and Zn-doped CsPbI3 yields an interpretable machine learning model that accurately predicts formation energies and identifies the fundamental drivers of dopant-induced stability. By quantifying topological rarity to explain model prediction errors, we validate ASPH as a physically grounded, reliable representation for data-driven materials discovery.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

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