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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 Machine Learning Interatomic Potential for Titanium-Helium systems
Author(s) Narayan Pokhrel, Kamal Wagle, Reeju Pokharel
On-Site Speaker (Planned) Narayan Pokhrel
Abstract Scope Understanding the dynamic response of Titanium-Helium (Ti-He) systems under extreme loading conditions is essential for predicting the operational and performance limits of plasma-facing materials and structural components in fusion energy systems. Accurately modeling these systems requires quantum-mechanical bonding behavior under severe volumetric deformation while scaling the simulations to millions of atoms to capture large scale dynamic material response. In this work, we present an end-to-end framework based on the Graph Atomic Cluster Expansion (GRACE) machine learning interatomic potential. By using curated dataset from a large number of Density Functional Theory (DFT) calculations with deformations, interstitials, vacancies, He bubbles and finite-temperature configurations, we develop a machine learning potential that achieves near-DFT accuracy while enabling large-scale simulations at a fraction of the computational cost.
Proceedings Inclusion? Planned:
Keywords Titanium, Machine Learning, Computational Materials Science & Engineering

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