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We describe the triboluminescence response of undoped (BaAl2Si2O8, h–BAS) and Eu-doped (h–BAS:Eu) barium hexacelsian powders and show that the triboluminescence behavior is dependent on the formation of barium vacancies. X-ray photoelectron spectroscopy of the h–BAS:Eu powders confirms the presence of Eu3+ and Eu2+ in the compound, leading to the formation of significant vacancy point defects in excess of those found in h–BAS as a result of the charge imbalance caused by the substitution of Eu3+ in Ba2+ sites. From electron paramagnetic resonance measurements and density functional theory (DFT) calculations, we demonstrate that the vacancy defects correspond to singly ionized barium vacancies. DFT-calculated thermodynamic transitions and electronic structure calculations reveal deep energy levels within the compound’s energy band gap, with a strong emission at 3.33 eV correlated to an electron exchange between the conduction band minimum and a barium vacancy center. Time-resolved triboluminescence spectra show that the increased concentration of barium vacancies in h–BAS:Eu enhances the signal by about 75% compared to the signal from h–BAS. These results play an important role in the understanding of fundamental mechanisms behind the triboluminescence response of ceramic materials as well as the role of different types of defects in this process. 

The rapid development and large body of literature on machine learning potentials (MLPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLPs. This review paper covers a broad range of topics related to MLPs, including (i) central aspects of how and why MLPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLPs, (iv) a practical guide for estimating and understanding the execution speed of MLPs, including guidance for users based on hardware availability, type of MLP used, and prospective simulation size and time, (v) a manual for what MLP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLPs over the next 3-10+ years. 

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3–10+ years. 

Abstract Refractory multi-principal element alloys (RMPEAs) are promising materials for high-temperature structural applications. Here, we investigate the role of short-range ordering (SRO) on dislocation glide in the MoNbTi and TaNbTi RMPEAs using a multi-scale modeling approach. Monte carlo/molecular dynamics simulations with a moment tensor potential show that MoNbTi exhibits a much greater degree of SRO than TaNbTi and the local composition has a direct effect on the unstable stacking fault energies (USFEs). From mesoscale phase-field dislocation dynamics simulations, we find that increasing SRO leads to higher mean USFEs and stress required for dislocation glide. The gliding dislocations experience significant hardening due to pinning and depinning caused by random compositional fluctuations, with higher SRO decreasing the degree of USFE dispersion and hence, amount of hardening. Finally, we show how the morphology of an expanding dislocation loop is affected by the applied stress. 

Developing chemically and thermally stable, highly efficient green-emitting inorganic phosphors is a significant challenge in solid-state lighting. One accessible pathway for achieving green emission is by forming a solid solution with superior blue-emitting materials. In this work, we demonstrate that the cyan-emission ( λ em = 481 nm) of the BaScO 2 F:Eu 2+ perovskite can be red-shifted by forming a solid solution following (Ba 1− x Sr x ) 0.98 Eu 0.02 ScO 2 F ( x = 0, 0.075, 0.15, 0.25, 0.33, 0.40). Although green emission is achieved ( λ em = 516 nm) as desired, the thermal quenching (TQ) resistance is reduced, and the photoluminescence quantum yield (PLQY) drops by 65%. Computation reveals the source of these changes. Surprisingly, a basic density functional theory analysis shows the gradual Sr Ba substitution has negligible effects on the band gap ( E g ) energy, suggesting the activation energy barrier for the thermal ionization quenching remains unchanged, while the nearly constant Debye temperature indicates no loss of average structural rigidity to explain the decrease in the PLQY. Instead, temperature-dependent ab initio molecular dynamics (AIMD) simulations show that gradual changes of the Eu 2+ ion's local coordination environment rigidity are responsible for the drop in the observed TQ and PLQY. These results express the need to computationally analyze the local rare-earth environment as a function of temperature to understand the fundamental origin of optical properties in new inorganic phosphors.