High-Throughput First-Principles Prediction of Interfacial Adhesion Energies in Metal-on-Metal Contacts

: Adhesion energy, a measure of the strength by which two surfaces bind together, ultimately dictates the mechanical behavior and failure of interfaces. As natural and artificial solid interfaces are ubiquitous, adhesion energy represents a key quantity in a variety of fields ranging from geology to nanotechnology. Because of intrinsic difficulties in the simulation of systems where two different lattices are matched, and despite their importance, no systematic, accurate first-principles determination of heterostructure adhesion energy is available. We have developed robust, automatic high-throughput workflow able to fill this gap by systematically searching for the optimal interface geometry and accurately determining adhesion energies. We apply it here for the first time to perform the screening of around a hundred metallic heterostructures relevant for technological applications. This allows us to populate a database of accurate values, which can be used as input parameters for macroscopic models. Moreover, it allows us to benchmark commonly used, empirical relations that link adhesion energies to the surface energies of its constituent and to improve their predictivity employing only quantities that are easily measurable or computable

Spontaneous Learning of Visual Structures in Domestic Chicks

Effective communication crucially depends on the ability to produce and recognize structured signals, as apparent in language and birdsong. Although it is not clear to what extent similar syntactic-like abilities can be identified in other animals, recently we reported that domestic chicks can learn abstract visual patterns and the statistical structure defined by a temporal sequence of visual shapes. However, little is known about chicks’ ability to process spatial/positional information from visual configurations. Here, we used filial imprinting as an unsupervised learning mechanism to study spontaneous encoding of the structure of a configuration of different shapes. After being exposed to a triplet of shapes (ABC or CAB), chicks could discriminate those triplets from a permutation of the same shapes in different order (CAB or ABC), revealing a sensitivity to the spatial arrangement of the elements. When tested with a fragment taken from the imprinting triplet that followed the familiar adjacency-relationships (AB or BC) vs. one in which the shapes maintained their position with respect to the stimulus edges (AC), chicks revealed a preference for the configuration with familiar edge elements, showing an edge bias previously found only with temporal sequences

High-throughput generation of potential energy surfaces for solid interfaces

A robust, modular, and ab initio high-throughput workflow is presented to automatically match and characterize solid–solid interfaces using density functional theory calculations with automatic error corrections. The potential energy surface of the interface is computed in a highly efficient manner, exploiting the high-symmetry points of the two mated surfaces. A database is automatically populated with results to ensure that already available data are not unnecessarily recomputed. Computational parameters and slab thicknesses are converged automatically to minimize computational cost while ensuring accurate results. The surfaces are matched according to user-specified maximal cross-section area and mismatches. Example results are presented as a proof of concept and to show the capabilities of our approach that will serve as the basis for many more interface studies