2D Material Armors Showing Superior Impact Strength of Few Layers
Stefano Signetti, Simone Taioli & Nicola M. Pugno
ACS Applied Materials & Interfaces, DOI: 10.1021/acsami.7b12030
We study the ballistic properties of two-dimensional (2D) materials upon the hypervelocity impacts of C60 fullerene molecules combining ab initio density functional tight binding and finite element simulations. The critical penetration energy of monolayer membranes is determined using graphene and the 2D allotrope of boron nitride as case studies. Furthermore, the energy absorption scaling laws with a variable number of layers and interlayer spacing are investigated, for homogeneous or hybrid configurations (alternated stacking of graphene and boron nitride). At the nanolevel, a synergistic interaction between the layers emerges, not observed at the micro- and macro-scale for graphene armors. This size-scale transition in the impact behavior toward higher dimensional scales is rationalized in terms of scaling of the damaged volume and material strength. An optimal number of layers, between 5 and 10, emerges demonstrating that few-layered 2D material armors possess impact strength even higher than their monolayer counterparts. These results provide fundamental understanding for the design of ultralightweight multilayer armors using enhanced 2D material-based nanocomposites.
Articolo citato in:
Armature di grafene super resistenti e ultraleggere
Pubblicato il 06.12.2017
Virtual screening of inorganic materials synthesis parameters with deep learning
Edward Kim, Kevin Huang, Stefanie Jegelka & Elsa Olivetti
npj Computational Materials, DOI: 10.1038/s41524-017-0055-6
Virtual materials screening approaches have proliferated in the past decade, driven by rapid advances in first-principles computational techniques, and machine-learning algorithms. By comparison, computationally driven materials synthesis screening is still in its infancy, and is mired by the challenges of data sparsity and data scarcity: Synthesis routes exist in a sparse, high-dimensional parameter space that is difficult to optimize over directly, and, for some materials of interest, only scarce volumes of literature-reported syntheses are available. In this article, we present a framework for suggesting quantitative synthesis parameters and potential driving factors for synthesis outcomes. We use a variational autoencoder to compress sparse synthesis representations into a lower dimensional space, which is found to improve the performance of machine-learning tasks. To realize this screening framework even in cases where there are few literature data, we devise a novel data augmentation methodology that incorporates literature synthesis data from related materials systems. We apply this variational autoencoder framework to generate potential SrTiO3 synthesis parameter sets, propose driving factors for brookite TiO2 formation, and identify correlations between alkali-ion intercalation and MnO2 polymorph selection.
Articolo citato in:
Sintetizzare nuovi materiali con le reti neurali
Pubblicato il 26.12.2017