Functional glasses play a critical role in current and developing technologies. These materials have traditionally been designed empirically through trial-and-error experimentation. However, here we report recent advancements in the design of new glass compositions starting at the atomic level, which have become possible through an unprecedented level of understanding of glass physics and chemistry. For example, new damage-resistant glasses have been developed using models that predict both manufacturing-related attributes (e.g., viscosity, liquidus temperature, and refractory compatibility), as well as the relevant end-use properties of the glass (e.g., elastic moduli, compressive stress, and damage resistance). We demonstrate how this approach can be used to accelerate the design of new industrial glasses for use in various applications. Through a combination of models at different scales, from atomistic through empirical modeling, it is now possible to decode the “glassy genome” and efficiently design optimized glass compositions for production at an industrial scale.

The synthesis and characterization of a fused thiophene-diketopyrrolopyrrole based semiconducting polymer PTDPPTFT4 is presented. A number of synthetic challenges have been overcome in the development of a practical scalable synthesis. Characterization by Gel Permeation Chromatography (GPC) over a range of temperatures has revealed the tendency of this polymer to aggregate even at elevated temperatures and confirmed that the molecular weight values obtained are for nonaggregated material. This polymer meets a number of important requirements for potential industrial applications, such as scalable synthesis, solubility in industrially suitable solvents, and material stability and processability into stable high performance thin film transistor devices. Computational modeling has been used to help explain the structure property relationships contributing to the high performance. Grazing incidence X-ray of the thin films showed out of plane lamellar packing and in plane π−π stacking, both good indicators of a preferentially oriented thin film, desirable for high charge carrier mobility. Hole mobilities in excess of 2 cm 2 /V•s, on/off ratio of >10 6 , and threshold voltage <2 V have been achieved.

No abstract

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties.

No abstract