Machine Learning Modeling of Temperature-Dependent Optoelectronic Properties of Anharmonic Solid Solutions

Abstract

Leveraging strong optoelectronic responses to external stimuli, such as temperature and electric fields, is central to the development of advanced photonic technologies, including adaptive photodetectors and reconfigurable photovoltaic devices. However, only a limited number of semiconducting materials, typically characterized by strong electron-phonon coupling, are known to exhibit such pronounced responsiveness, and their equilibrium optoelectronic properties are often not optimally suited for targeted applications. Chemical engineering strategies, such as doping and solid-solution mixing, are therefore widely employed to fine-tune the electronic and optical properties of semiconductors. Predicting the impact of such modifications, however, remains highly challenging due to the intrinsic complexity of chemically disordered and anharmonic systems, as well as the computational limitations of conventional first-principles approaches. In this work, we introduce a new computational framework that combines ab initio electronic-structure methods with machine-learning techniques to achieve first-principles precision in the prediction of optoelectronic properties of anharmonic solid solutions at finite temperature. We apply this approach to silver chalco-halide solid solutions, an emergent class of optoelectronic materials that have been experimentally shown to exhibit large band-gap tunability and strong responses to thermal excitations. Our results provide quantitative insight into the interplay between chemical disorder, lattice dynamics, and electronic structure in these materials. More broadly, this study establishes a general strategy for the accurate modeling of optoelectronic functionality in chemically disordered semiconductors.

Figures (8)

• Figure 1: Band-gap response to external stimuli in CAP. (a) Band-gap closing as a result of structural perturbations condensing in the equilibrium cubic CAP structure. Types of structural perturbations that may render band-gap closing in CAP, (b) general thermal fluctuations, and (c) a polar distortion stabilised by an electric field. (d) Cubic anti-perovskite structure of reference with space group $Pm\overline{3}m$. (e) CAP solid solutions with chemical formula Ag$_3$SBr$_x$I$_{1-x}$.
• Figure 2: Computational workflow for predicting temperature-dependent band gaps in solid solutions. (a) MLIP fine-tuning performed on a DFT-PBEsol dataset of energies, forces and stresses. (b) Band-gap prediction workflow: the graph representation of a given atomic configuration is first obtained and subsequently processed by a GNN predictive model trained on a DFT-HSEsol dataset. (c) General workflow for accurate band-gap prediction in solid solutions, integrating thermal corrections.
• Figure 3: GNN training for accurate band-gap prediction. (a) Workflow for training the GNN model on the DFT-HSEsol dataset. (b) GNN model architecture. (c,d) Band-gap distribution in the DFT-PBEsol and DFT-HSEsol training datasets. Predicted band gap vs. DFT calculated band gap for the training (e) and test (f) DFT-PBEsol datasets. Predicted band gap vs. DFT calculated band gap for the training (h) and test (i) DFT-HSEsol datasets. Band gap error distribution for GNN band-gap prediction on the DFT-PBEsol (g) and DFT-HSEsol (j) datasets.
• Figure 4: MLIP fine-tuning for CAP solid solutions. DFT vs. MLIP prediction of (a) energies, (b) atomic forces, and (c) mechanical stress using the pre-trained MACE model Batatia2022mace, and (d-f) the fine-tuned MLIP.
• Figure 5: Finetuned MLIP tested on phonon dispersions. (a) Phonon dispersions calculated for Ag$_3$SBr using DFT, a pre-trained MACE model Batatia2022mace, and the fine-tuned MLIP. MLIP predictions vs. DFT calculations for (b) a pre-trained MACE model, and (c) the fine-tuned MLIP. (d-f) Equivalent phonon results obtained for a Ag$_3$SBr$_{0.5}$I$_{0.5}$ solid solution.