Publication date: 15th December 2025
The hot carrier (HC) cooling at an ultrafast timescale represents a critical limitation in achieving high-efficiency photon-to-electron conversion. The comprehensive materials screening seeks to identify candidates that inherently suppress hot carrier relaxation, thereby extending carrier lifetimes. Here, we combine time-dependent density functional theory and nonadiabatic molecular dynamics to investigate the hot electron (HE) dynamics in experimentally synthesized Cs2AgInX6 (X = Br and Cl). These halide double perovskites exhibit significantly extended HE lifetimes compared to conventional metal halide perovskites, primarily due to the presence of discrete electronic states at the conduction band edge and weakened. The well-trained machine learning models, including artificial neural networks (ANN) and mutual information (MI), efficiently capture the complex relationships among composition, structural dynamics, and excited-state properties while drastically reducing computational expense. The ANN models that are trained using only 10% of data enable accurate interpolation of dynamic electronic properties such as energy gaps near the conduction band edge and nonadiabatic coupling. The high accuracies of predicted properties ensure the ability of these ANN models to capture transient changes in electronic structure over extended timescales. The ANN-generated interpolated data enhance the reliability of MI analyses that uncover subtle, time-resolved correlations between structural dynamics and HE relaxation. These insights pin down the strategic design approaches to realize stable materials for HE-based optoelectronic applications.[1]
N.S. acknowledges IIT Delhi for the Junior Research Fellowship. D.G. acknowledges the CSIR-Human Resource Development Group (HRDG) for ExtraMural Research-II Grant 01/3136/23/EMR-II and the IIT Delhi HPC facility for computational resources. The research presented in this article was supported by the Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project numbers 20190656PRD4 and 20240590ECR. This work was performed in part at the Center for Integrated Nanotechnology (CINT) at LANL, a U.S. DOE and Office of Basic Energy Sciences user facility. This research used resources provided by the LANL Institutional Computing Program. LANL is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy (Contract No. 89233218CNA000001).
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