Automated Toolkits and Machine-Learning Acceleration for Defect Simulations
First-principles simulations of atomic and electronic structure in solids offer a powerful route to predict and understand material properties. (1) This is particularly relevant in the case of point defects which dramatically affect material properties yet present many challenges for experimental characterisation.
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Tuesday, October 20th, 2026 — 8:00 AM PDT / 11:00 AM EDT / 5:00 PM CEST / 8:30 PM IST / 11:00 PM CST / 12:00 AM JST
Recent years have seen significant advances in both computational methodologies (2–4) and associated toolkits (5–9) for modelling defect behaviour. I will discuss our collaborative efforts in this area; including the continued development of the open-source doped defect simulation package5, approaches for exploring defect energy surfaces (including MLIP accelerations) (10,11) and remaining challenges in this area. (12)
Presented by Asst. Prof. Seán Kavanagh from the University of Cambridge