Speaker
Description
Perovskite materials are central to various technologies, particularly in photovoltaic applications. However, computational studies of perovskites using ab initio methods are limited by computational cost, especially when simulating large systems or long time scales. Molecular dynamics (MD) simulations offer a viable alternative, yet classical interatomic potentials often fall short in accurately modeling the complex interactions in these materials under different thermodynamic conditions [1].
In this work, we use the recently introduced Local Atomic Tensor Trainable Expansion (LATTE) descriptor [2] to construct a machine-learning interatomic potential for CsPbI₃ perovskite. The LATTE-based model achieves a lower loss compared to the Atomic Cluster Expansion (ACE) on the same dataset, highlighting its superior accuracy and generalization capabilities. These results suggest that LATTE provides a powerful approach for simulating perovskites, potentially enabling more predictive modeling of their behavior in realistic conditions.
[1] H. Zhang, H.C. Thong, L. Bastogne, C. Gui, X. He, P. Ghosez, Phys. Rev. B 110, 054109 (2024)
[2] F. Pellegrini, S. de Gironcoli, E. Küçükbenli, arXiv:2405.08137 (2024).
| Broad physics domain | Condensed Matter Physics / Materials Science |
|---|---|
| AI/ML technique(s) to be presented | Machine Learning Interatomic Potentials (MLIPs), Local Atomic Tensor Descriptors, Supervised Regression |
