Two-dimensional superconductivity at a Mott-Insulator/Band-Insulator interface: LaTiO3/SrTiO3

Transition metal oxides display a great variety of quantum electronic behaviours where correlations often play an important role. The achievement of high quality epitaxial interfaces involving such materials gives a unique opportunity to engineer artificial structures where new electronic orders take place. One of the most striking result in this area is the recent observation of a two-dimensional electron gas at the interface between a strongly correlated Mott insulator LaTiO3 and a band insulator SrTiO3. The mechanism responsible for such a behaviour is still under debate. In particular, the influence of the nature of the insulator has to be clarified. Here we show that despite the expected electronic correlations, LaTiO3/SrTiO3 heterostructures undergo a superconducting transition at a critical temperature Tc=300 mK. We have found that the superconducting electron gas is confined over a typical thickness of 12 nm. We discuss the electronic properties of this system and review the possible scenarios

Accelerating the discovery of materials for clean energy in the era of smart automation

The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace