High-throughput density functional calculations of solids are highly time-consuming. As an alternative, we propose a machine learning approach for the fast prediction of solid-state properties. To achieve this, local spin-density approximation calculations are used as a training set. We focus on predicting the value of the density of electronic states at the Fermi energy. We find that conventional representations of the input data, such as the Coulomb matrix, are not suitable for the training of learning machines in the case of periodic solids. We propose a novel crystal structure representation for which learning and competitive prediction accuracies become possible within an unrestricted class of $spd$ systems of arbitrary unit-cell size.

Designing materials with advanced functionalities is the main focus of contemporary solid-state physics and chemistry. Research efforts worldwide are funneled into a few high-end goals, one of the oldest, and most fascinating of which is the search for an ambient temperature superconductor (A-SC). The reason is clear: superconductivity at ambient conditions implies being able to handle, measure and access a single, coherent, macroscopic quantum mechanical state without the limitations associated with cryogenics and pressurization. This would not only open exciting avenues for fundamental research, but also pave the road for a wide range of technological applications, affecting strategic areas such as energy conservation and climate change. In this roadmap we have collected contributions from many of the main actors working on superconductivity, and asked them to share their personal viewpoint on the field. The hope is that this article will serve not only as an instantaneous picture of the status of research, but also as a true roadmap defining the main long-term theoretical and experimental challenges that lie ahead. Interestingly, although the current research in superconductor design is dominated by conventional (phonon-mediated) superconductors, there seems to be a widespread consensus that achieving A-SC may require different pairing mechanisms. In memoriam, to Neil Ashcroft, who inspired us all.

We present a first-principles study of the electron-phonon interaction and the prediction of the superconducting critical temperature in molecular metallic hydrogen. Our study is able to single out the features which drive the system towards superconductivity: mainly, a rich and complex Fermi surface and strongly coupled phonon modes driving the intra- or intermolecular charge transfer. We demonstrate that in this simple system, a very high superconducting critical temperature can be reached via electron-phonon and Coulomb electron-electron interactions.

Due to its low atomic mass, hydrogen is the most promising element to search for high-temperature phononic superconductors. However, metallic phases of hydrogen are only expected at extreme...

A new parameter-free approximation for the exchange-correlation kernel fxc of time-dependent density-functional theory is proposed. This kernel is expressed as an algorithm in which the exact Dyson equation for the response, as well as an approximate expression for fxc in terms of the dielectric function, are solved together self-consistently, leading to a simple parameter-free kernel. We apply this to the calculation of optical spectra for various small band gap (Ge, Si, GaAs, AlN, TiO2, SiC), large band gap (C, LiF, Ar, Ne), and magnetic (NiO) insulators. The calculated spectra are in very good agreement with the experiment for this diverse set of materials, highlighting the universal applicability of the new kernel.