The fundamental challenge for designing transparent conductors used in photovoltaics, displays and solid-state lighting is the ideal combination of high optical transparency and high electrical conductivity. Satisfying these competing demands is commonly achieved by increasing carrier concentration in a wide-bandgap semiconductor with low effective carrier mass through heavy doping, as in the case of tin-doped indium oxide (ITO). Here, an alternative design strategy for identifying high-conductivity, high-transparency metals is proposed, which relies on strong electron-electron interactions resulting in an enhancement in the carrier effective mass. This approach is experimentally verified using the correlated metals SrVO3 and CaVO3, which, despite their high carrier concentration (>2.2 × 10(22) cm(-3)), have low screened plasma energies (<1.33 eV), and demonstrate excellent performance when benchmarked against ITO. A method is outlined to rapidly identify other candidates among correlated metals, and strategies are proposed to further enhance their performance, thereby opening up new avenues to develop transparent conductors.

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Transition metal oxides offer functional properties beyond conventional semiconductors. Bridging the gap between the fundamental research frontier in oxide electronics and their realization in commercial devices demands a wafer-scale growth approach for high-quality transition metal oxide thin films. Such a method requires excellent control over the transition metal valence state to avoid performance deterioration, which has been proved challenging. Here we present a scalable growth approach that enables a precise valence state control. By creating an oxygen activity gradient across the wafer, a continuous valence state library is established to directly identify the optimal growth condition. Single-crystalline VO2 thin films have been grown on wafer scale, exhibiting more than four orders of magnitude change in resistivity across the metal-to-insulator transition. It is demonstrated that ‘electronic grade' transition metal oxide films can be realized on a large scale using a combinatorial growth approach, which can be extended to other multivalent oxide systems.

Reconfigurable devices offer the ability to program electronic circuits on demand. In this work, we demonstrated on-demand creation of artificial neurons, synapses, and memory capacitors in post-fabricated perovskite NdNiO 3 devices that can be simply reconfigured for a specific purpose by single-shot electric pulses. The sensitivity of electronic properties of perovskite nickelates to the local distribution of hydrogen ions enabled these results. With experimental data from our memory capacitors, simulation results of a reservoir computing framework showed excellent performance for tasks such as digit recognition and classification of electrocardiogram heartbeat activity. Using our reconfigurable artificial neurons and synapses, simulated dynamic networks outperformed static networks for incremental learning scenarios. The ability to fashion the building blocks of brain-inspired computers on demand opens up new directions in adaptive networks.