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The efficiency of silicon solar cells is affected by the light absorption and recombination losses of photoexcited charge carries. One possible way to improve the efficiency is through the deposition of transition metal nanoparticles on Si surfaces. Here, we first carry out density functional theory (DFT) calculations to obtain electronic structures for Agn (n = 1–7) monolayered clusters adsorbed on Si(111)/H surfaces. Results are presented in the form of the density of states, band gaps, and light absorption, which allow for the investigation of the interaction of Ag clusters with Si. Different behaviors can be expected depending on the size of the deposited Ag clusters. Overall, the deposition of Ag clusters leads to smaller band gaps, red-shifts, and large increases in light absorption compared to the pristine Si slab. We then study the relaxation dynamics of electron–hole pairs for slabs based on nonadiabatic couplings using the reduced density matrix approach within the Redfield formalism. Nonradiative relaxation rates are noticeably different for various structures and transitions. One observes higher relaxation rates for surfaces with adsorbates than for the pristine Si surface due to charge transfer events involving Ag orbitals. We also compute emission spectra from excited-state relaxation dynamics. The band gap emission is dark for the pristine Si due to the indirect nature of its band gap. The addition of larger Ag clusters breaks the symmetry of Si slabs, enabling indirect gap transitions. These slabs thus exhibit bright band gap emission. The introduction of adsorbates is advantageous for applications in photovoltaics and photocatalysis.
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This content will become publicly available on July 16, 2027
First-Principles Calculations and Machine Learning for Nonradiative Relaxation of Charge Carriers at Nanostructured Ag n Si(111) Monolayered Surfaces
The relaxation of hot carriers in Si materials is dominated by nonradiative processes. The relaxation dynamics of Agn (n = 0–7) monolayered clusters adsorbed on Si(111) slabs has been computed based on nonadiabatic couplings (NACs) using the Redfield formalism of density matrix theory for dissipative rates, with an electronic basis set generated by density functionals. Here, we present machine learning (ML) models to predict nonradiative relaxation rate constants of charge carriers (ke/h). We construct a data set in which each sample corresponds to ke/h associated with a specific initial photoexcited condition of a given system. The ML feature vector consists of Redfield tensor elements, carrier identity, and atomic structure identity. The ML target label is ke/h obtained from the Redfield formalism. Four ML regressors are evaluated: linear Ridge regression, random forest (RF) regressor, XGBoost, and feed-forward multilayer perceptron (MLP). Model performance is evaluated using stratified 5-fold cross-validation. All models achieve strong predictive performance, with test set R2 values exceeding 0.96. Ridge regression and XGBoost exhibit the highest test set R2 values of 0.99. The small performance gap between Ridge regression and the more complex nonlinear models suggests that much of the structure–property relationship is approximately linear in the transformed feature space. This study shows that complex excited-state relaxation processes can be learned from Redfield tensor elements using relatively simple models.
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- Award ID(s):
- 1944921
- PAR ID:
- 10699634
- Publisher / Repository:
- American Chemical Society
- Date Published:
- Journal Name:
- The Journal of Physical Chemistry Letters
- Volume:
- 17
- Issue:
- 28
- ISSN:
- 1948-7185
- Page Range / eLocation ID:
- 8032 to 8037
- Subject(s) / Keyword(s):
- Carrier Dynamics Elements Machine Learning Molecular Dynamics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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