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Abstract Network glass fracture occurs as a sequence of elementary events occurring at weak sites in the glass structure. Fracture is a highly complex process that occurs suddenly and without obvious structural or thermodynamic signs prior to the event’s occurrence. We show that a stress threshold value quantified by local mechanical probing highly correlates with nanoscale crack nucleation in a two-dimensional network glass. Subsequently, a neural network-based predictor, the local intelligent stress threshold indicator (LISTI), links the local stress threshold with the undeformed local structural topology. LISTI yields a reliable heatmap indicating soft spots that strongly correlate with the localized initiation and development of the fracture process. Finally, we show that LISTI can be used to find local zones prone to rearrangement in real-measured two-dimensional silica glass structures. 

Replica exchange transition interface sampling simulations in Mg–Al alloys with high vacancy concentrations indicate that the presence of a solute reduces thermodynamic barriers to the clustering of vacancies and the formation of voids. The emergence of local minima in the free energy along the reaction coordinate suggests that void formation may become a multi-step process in the presence of a solute. In this scenario, vacancies agglomerate with solute before they coalesce into a stable void with well-defined internal surfaces. The emergence of vacancy–solute clusters as intermediate states would imply that classical nucleation theory is unlikely to adequately describe void formation in alloys at high vacancy concentrations, a likely precursor for alloy strengthening through nanoscale precipitation. 

Performance in math, particularly algebra, is a major barrier to student success and participation in STEM among under-represented minoritized students, particularly Black U.S. high school students. This research applies social cognitive career theory (SCCT) to measure the impacts of an afterschool algebra for engineering program on math self-efficacy and interest in STEM among high school students in a large urban district. To study the program’s effects, a mixed methods research design was used where schools were assigned to either treatment or control conditions. Students in treatment schools accessed algebra for engineering modules, STEM professional role model videos, and field trips, while students in control schools accessed role model videos and field trips only. Surveys measuring math self-efficacy and STEM interest, outcome expectations, and choice goals were completed by participants in both conditions at the beginning and end of two separate program years, 2021–2022 and 2022–2023. Across both years, quantitative results suggest some positive effects of participation, particularly for STEM choice goals, but benefits depend upon student participation levels. Qualitative data offer student voice around prior experiences in math and science and the development of postsecondary plans in STEM. In combination, the results suggest that for students who do not initially identify as STEM career-bound, afterschool programming may not necessarily promote preparation for STEM careers due to an accumulation of weak math and science school experiences and other socio-environmental influences.