Humans primarily interact with information technology through glass touch screens, and the world would indeed be unrecognizable without glass. However, the low toughness of oxide glasses continues to be their Achilles heel, limiting both future applications and the possibility to make thinner, more environmentally friendly glasses. Here, we show that with proper control of plasticity mechanisms, record-high values of fracture toughness for transparent bulk oxide glasses can be achieved. Through proper combination of gas-mediated permanent densification and rational composition design, we increase the glasses’ propensity for plastic deformation. Specifically, we demonstrate a fracture toughness of an aluminoborate glass (1.4 MPa m0.5) that is twice as high as that of commercial glasses for mobile devices. Atomistic simulations reveal that the densification of the adaptive aluminoborate network increases coordination number changes and bond swapping, ultimately enhancing plasticity and toughness upon fracture. Our findings thus provide general insights into the intrinsic toughening mechanisms of oxide glasses.

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We present a new method based on particle swarm optimization (PSO) for parameterization of interatomic potentials. Using glassy silica as a case study, we parameterize two interatomic potentials based on structural features obtained from ab initio simulations and experimental neutron diffraction data.

There is currently a lack of research concerning whether Emotional Classification (EC) research on a language is applicable to other languages. If this is the case then we can greatly reduce the amount of research needed for different languages. Therefore, we propose a framework to answer the following null hypothesis: The change in classification accuracy for Emotional Classification caused by changing a single preprocessor or classifier is independent of the target language within a significance level of p = 0.05. We test this hypothesis using an English and a Danish data set, and the classification algorithms: Support-Vector Machine, Naive Bayes, and Random Forest. From our statistical test, we got a p-value of 0.12852 and could therefore not reject our hypothesis. Thus, our hypothesis could still be true. More research is therefore needed within the field of crosslanguage EC in order to benefit EC for different languages.

Glasses such as lithium thiophosphates (Li2S-P2S5) show promise as solid electrolytes for batteries, but a poor understanding of how the disordered structure affects lithium transport properties limits the development of glassy electrolytes. To address this, we here simulate glassy Li2S-P2S5 electrolytes with varying fractions of polyatomic anion clusters, i.e., P2S6 4–, P2S7 4–, and PS4 3–, using classical molecular dynamics. Based on the determined variation in ionic conductivity, we use a classification-based machine-learning metric termed “softness”a structural fingerprint that is correlated to the atomic rearrangement probabilityto unveil the structural origin of lithium-ion mobility. The softness distribution of lithium ions is highly spatially correlated: that is, the “soft” (high mobility) lithium ions are predominantly found around PS4 3– units, while the “hard” (low mobility) ions are found around P2S6 4– units. We also show that soft lithium-ion migration requires a smaller energy barrier to be overcome relative to that observed for hard lithium-ion migration.