Chemical design of SiO2-based glasses with high elastic moduli and low weight is of great interest. However, it is difficult to find a universal expression to predict the elastic moduli according to the glass composition before synthesis since the elastic moduli are a complex function of interatomic bonds and their ordering at different length scales.Here we show that the densities and elastic moduli of SiO2-based glasses can be efficiently predicted by machine learning (ML) techniques across a complex compositional space with multiple (>10) types of additive oxides besides SiO2. Our machine learning approach relies on a training set generated by high-throughput molecular dynamic (MD) simulations, a set of elaborately constructed descriptors that bridges the empirical statistical modeling with the fundamental physics of interatomic bonding, and a statistical learning/predicting model developed by implementing least absolute shrinkage and selection operator with a gradient boost machine (GBM-LASSO). The predictions of the ML model are comprehensively compared and validated with a large amount of both simulation and experimental data. By just training with a dataset only composed of binary and ternary glass samples, our model shows very promising capabilities to predict the density and elastic moduli for k-nary SiO2based glasses beyond the training set. As an example of its potential applications, our GBM-LASSO model was used to perform a rapid and low-cost screening of many (~10 5 ) compositions of a multicomponent glass system to construct a compositionalproperty database that allows for a fruitful overview on the glass density and elastic properties.

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Group-III nitrides are established commercial semiconductors that were recently synthesized in 2D form. We apply first-principles calculations to determine the electronic properties of polar hydrogen-passivated monolayers and bilayers of BN, AlN, GaN, and InN. We find that hydrogen-passivated AlN, GaN, and InN monolayers are stable under standard conditions. Our results indicate that antialigned bilayer orientations, which cancel the polarization fields, yield wide band gaps by quantum confinement. However, the band gaps are narrower in bilayers with aligned polarizations, due to the strong quantum-confined Stark effect. Strongly bound interlayer excitons form in structures with aligned polarizations due to the spatial separation of electron and hole wave functions on different monolayers. Our results demonstrate that the stacking orientation acts as an additional degree of freedom to tune the band gap of polar 2D bilayers, as well as to control the direct or interlayer nature of excitons for optoelectronic and excitonic applications.

Hydrogen (H) adsorption strengths on oxygen-terminated (0001¯) surfaces of pure and doped wurtzite ZnO are investigated under varying H surface coverage conditions. Consistent with the prediction of the classical electron counting rules, a 12 monolayer (ML) of adsorbed H changes the electronic structure of pure ZnO (0001¯) surface from metallic to semiconductor state by saturating unpaired electrons of surface oxygen atoms. This closed-shell electron configuration of the ZnO (0001¯) surface significantly reduces the adsorption strengths of subsequent H atoms, making the dissociative adsorption of a H2 molecule endothermic. We apply a simple electron counting model to predict and tune the coverage-dependent H adsorption strengths on general polar semiconductor surfaces. This model is confirmed by our investigations of H adsorption on (0001¯) surfaces of ZnO with a series of dopant elements (Na, Mg, Al, Ti, Fe, Sn, etc.). It can also be applied to H adsorption on other similar polar semiconductors, such as ZnO (0001¯) containing O vacancies, wurtzite GaN (0001¯), and zincblende ZnS (1¯1¯1¯) surfaces.