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About this Abstract
Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title AI-driven theoretical frameworks to determine structure of complex materials and defects from experiments
Author(s) Venkata Surya Chaitanya Kolluru, Maria Chan
On-Site Speaker (Planned) Venkata Surya Chaitanya Kolluru
Abstract Scope Determination of accurate structure from various experimental characterization data is crucial for theoretical modeling and understanding these materials. We create multi-objective optimization framework leveraging the machine learning interatomic potentials (MLIPs) to determine the accurate atomistic representation of complex nanoscale materials. In this talk, I will discuss the application of AI-driven frameworks combining experimental data such as XRD and STEM images with structure generation protocols applied towards solid-state electrolytes and interfaces in superconducting qubits. This framework is agnostic to the type of characterization data and geometry of the materials. Further, the determined structures are grounded in realistic experimental observations while being thermodynamically stable or metastable, thereby providing a reliable modality to explore complex materials and accelerate materials design for various applications.
Proceedings Inclusion? Planned:
Keywords Energy Conversion and Storage, Thin Films and Interfaces, Modeling and Simulation

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