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Photoplasticity, the light-induced alteration of mechanical properties in semiconductors, is crucial for the development of advanced optoelectronic devices and the understanding of semiconductor mechanics. Despite progress in understanding this phenomenon, atomic-scale mechanisms, particularly under photoexcitation, remain complex and are partially understood. Here, we introduce a new computational framework combining constrained Density Functional Theory (CDFT) with machine learning potential (MLP) to explore Peierls stress and dislocation dynamics in zinc sulfide (ZnS) under both ground and excited states. Our results reveal that photoexcitation significantly increases Peierls stress by reducing strain concentration at the dislocation core, contributing to the transition from ductility to brittleness under light exposure. Importantly, this enhancement occurs without substantial changes in the dislocation core structure. These insights provide an understanding of the atomic-scale mechanisms behind photoplasticity in ZnS, demonstrating that integrating CDFT with MLP is a highly accurate and efficient approach to study complex material behaviors under photoexcitation. 

Abstract Zirconium carbide (ZrC), a high‐performance refractory ceramic, exhibits complex defect dynamics that critically influence its behavior in extreme environments. In this work, we employ density functional theory (DFT) simulations to determine the formation energies and migration barriers of four defect types—isolated carbon vacancies, divacancies, Frenkel pairs, and Schottky pairs—across various charge states. The calculated formation energies reveal that isolated carbon vacancies are the most energetically favorable (1.13 eV), followed by Frenkel pairs (3.29 eV), while divacancies (6.86 eV) and Schottky pairs (8.29 eV) require higher formation energies, indicating their lower intrinsic concentrations. Isolated carbon vacancies exhibit the highest migration barrier (4.11 eV) in ZrC, with a modest increase to 4.13 eV upon adding one electron to 64‐atom supercell and a decrease to 4.06 eV with two electrons/64‐atom supercell—reflecting charge redistribution that stabilizes the local environment and weakens nearby Zr–C bonds. In contrast, Frenkel and Schottky pairs show barrier increases with electron doping and decreases with holes (ranging from 3.26 to 3.44 eV and 3.37 to 3.73 eV, respectively), while divacancies display increases (carbon vacancies: 2.69 to 2.93 eV; zirconium vacancies: 3.60 to 3.69 eV) upon electron addition. These results reveal the defect‐specific impact of charge carriers on mobility in ZrC, offering key insights for optimizing its performance in extreme environments. 

Sulfide-based solid electrolytes (SEs) are emerging as compelling materials for all-solid-state batteries (ASSBs), primarily due to their high ionic conductivities and robust mechanical stability. In particular, glassy SEs (GSEs) comprising mixed Si and P glassformers show promise, thanks to their efficient synthesis process and their intrinsic ability to prevent lithium dendrite growth. However, to date the complexity of their glassy structures hinders a complete understanding of the relationships between their structures and properties. Here, new machine learning force field (ML- FF) specifically designed for lithium sulfide-based GSEs has been developed. This ML-FF has been used to investigate the structural characteristics, mechanical properties, and lithium ionic conductivities in binary lithium thiosilicate and lithium thiophosphate GSEs, as well as their ternary mixed glassformer (MGF) lithium thiosilicophosphate GSEs. Molecular dynamic (MD) simulations using the ML-FF were conducted to explore the glass structures in varying compositions, including binary Li2S-SiS2 and Li2S-P2S5, as well as ternary Li2S-SiS2-P2S5. The simulations with the ML-FF yielded consistent results in terms of density, elastic modulus, radial distribution functions, and neutron structure factors, compared to DFT and experimental work. A key focus of this study was to investigate the local environments of Si and P molecular clusters. We discovered that most Si atoms in the Li2S-SiS2 GSE are situated in an edge-sharing environment, while the Li2S-P2S5 glass contained a minor proportion of edge-sharing P2S62- environments. In the ternary 60Li2S-32SiS2-8P2S5 glass, the ML-FF predicted similar P environments as observed in the binary Li2S-P2S5 glass. Additionally, it indicated the coexistence of corner and edge-sharing between PS4 and SiS4 tetrahedra in this ternary composition. Concerning lithium ionic conductivity at 300K, all studied glass compositions exhibited similar magnitudes and followed the Arrhenius relationship. The 50Li2S-50SiS2 glass displayed the lowest conductivity at 2.1 mS/cm, while the 75Li2S-25P2S5 composition exhibited the highest at 3.6 2 mS/cm. The ternary glass showed a conductivity of 2.57 mS/cm, sitting between the two. Interestingly, the predicted conductivities were about an order of magnitude higher than experimental values for the binary glasses but aligning more closely with that of the ternary glass. Moreover, an in-depth analysis of lithium-ion diffusion over the MD trajectory in the ternary glass demonstrated a significant correlation between diffusion pathways and the rotational dynamics of nearby SiS4 or PS4 tetrahedra. The ML-FF developed in this study shows immense potential as a versatile tool for exploring a broad spectrum of solid-state and mixed-former sulfide-based electrolytes.