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Abstract Defects, such as halide interstitials, act as charge recombination centers, induce degradation of halide perovskites, and create major obstacles to applications of these materials. Alkali metal dopants greatly improve perovskite performance. Using ab initio nonadiabatic molecular dynamics, it is demonstrated that alkalis bring favorable effects. The formation energy of halide interstitials increases by up to a factor of four in the presence of alkali dopants, and therefore, defect concentration decreases. When defects are present, alkali metals strongly bind to them. Halide interstitials introduce mid‐gap states that rapidly trap charge carriers. Alkalis eliminate the trap states, helping to maintain high current density. Further to charge trapping, the interstitials accelerate charge recombination. By passivating the interstitials, alkalis make carrier lifetimes up to seven times longer than in defect‐free perovskites and up to thirty times longer than in defective perovskites.
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This content will become publicly available on September 3, 2025
Breaking the size limitation of nonadiabatic molecular dynamics in condensed matter systems with local descriptor machine learning
Nonadiabatic molecular dynamics (NA-MD) is a powerful tool to model far-from-equilibrium processes, such as photochemical reactions and charge transport. NA-MD application to condensed phase has drawn tremendous attention recently for development of next-generation energy and optoelectronic materials. Studies of condensed matter allow one to employ efficient computational tools, such as density functional theory (DFT) and classical path approximation (CPA). Still, system size and simulation timescale are strongly limited by costly ab initio calculations of electronic energies, forces, and NA couplings. We resolve the limitations by developing a fully machine learning (ML) approach in which all the above properties are obtained using neural networks based on local descriptors. The ML models correlate the target properties for NA-MD, implemented with DFT and CPA, directly to the system structure. Trained on small systems, the neural networks are applied to large systems and long timescales, extending NA-MD capabilities by orders of magnitude. We demonstrate the approach with dependence of charge trapping and recombination on defect concentration in MoS2. Defects provide the main mechanism of charge losses, resulting in performance degradation. Charge trapping slows with decreasing defect concentration; however, recombination exhibits complex dependence, conditional on whether it occurs between free or trapped charges, and relative concentrations of carriers and defects. Delocalized shallow traps can become localized with increasing temperature, changing trapping and recombination behavior. Completely based on ML, the approach bridges the gap between theoretical models and realistic experimental conditions and enables NA-MD on thousand-atom systems and many nanoseconds.
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- Award ID(s):
- 2154367
- PAR ID:
- 10583509
- Publisher / Repository:
- PNAS
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 121
- Issue:
- 36
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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