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We report a combined experimental and theoretical study of the spatial distributions and sizes of conductive filaments in nano-scale electrochemical metallization (ECM) cells. Each cell comprises a silver nanocube as active electrode, a titanium dioxide (TiO 2 ) or aluminum oxide (Al 2 O 3 ) layer as dielectric, and a highly-doped silicon substrate as passive counter electrode. Following electroforming of the ECM cell and subsequent mechanical delamination of the silver nanocubes, current maps at previous particle locations reveal an intriguing metal distribution in the TiO 2 , with preferential accumulation close to the original locations of the nanocube edges. We assign this behavior to electric field enhancements close to the cube edge positions. In contrast, filaments in Al 2 O 3 layers show a comparatively homogenous distribution, which may be assigned to its lower dielectric permittivity. By increasing the oxide thickness, the total area of conductive spots in the current maps increases monotonically for both materials. Kinetic Monte-Carlo simulations of ion migration dynamics in TiO 2 confirm the experimental observations, describing both the preferred locations and oxide thickness-dependent metal loadings associated with filament formation. Overall, our findings are highly valuable for the design of future electrochemical metallization cells, especially in the sub-100 nm regime, where optimal filament control is of major importance for achieving lowest device-to-device variability.

As the number of Internet of Things devices is rapidly increasing, there is an urgent need for sustainable and efficient energy sources and management practices in ambient environments. In response, we developed a high-efficiency ambient photovoltaic based on sustainable non-toxic materials and present a full implementation of a long short-term memory (LSTM) based energy management using on-device prediction on IoT sensors solely powered by ambient light harvesters. The power is supplied by dye-sensitised photovoltaic cells based on a copper(ii/i) electrolyte with an unprecedented power conversion efficiency at 38% and 1.0 V open-circuit voltage at 1000 lux (fluorescent lamp). The on-device LSTM predicts changing deployment environments and adapts the devices' computational load accordingly to perpetually operate the energy-harvesting circuit and avoid power losses or brownouts. Merging ambient light harvesting with artificial intelligence presents the possibility of developing fully autonomous, self-powered sensor devices that can be utilized across industries, health care, home environments, and smart cities., Competing Interests: There are no competing interests to declare., (This journal is © The Royal Society of Chemistry.)

Novel optoelectronic materials have the potential to revolutionize the ongoing green transition by both providing more efficient photovoltaic (PV) devices and lowering energy consumption of devices like LEDs and sensors. The lead candidate materials for these applications are both organic semiconductors and more recently perovskites. This Perspective illustrates how novel machine learning techniques can help explore these materials, from speeding up ab initio calculations toward experimental guidance. Furthermore, based on existing work, perspectives around machine-learned molecular dynamics potentials, physically informed neural networks, and generative methods are outlined.

The field of photovoltaics gives the opportunity to make our buildings ''smart'' and our portable devices "independent", provided effective energy sources can be developed for use in ambient indoor conditions. To address this important issue, ambient light photovoltaic cells were developed to power autonomous Internet of Things (IoT) devices, capable of machine learning, allowing the on-device implementation of artificial intelligence. Through a novel co-sensitization strategy, we tailored dye-sensitized photovoltaic cells based on a copper(ii/i) electrolyte for the generation of power under ambient lighting with an unprecedented conversion efficiency (34%, 103 μW cm -2 at 1000 lux; 32.7%, 50 μW cm -2 at 500 lux and 31.4%, 19 μW cm -2 at 200 lux from a fluorescent lamp). A small array of DSCs with a joint active area of 16 cm 2 was then used to power machine learning on wireless nodes. The collection of 0.947 mJ or 2.72 × 10 15 photons is needed to compute one inference of a pre-trained artificial neural network for MNIST image classification in the employed set up. The inference accuracy of the network exceeded 90% for standard test images and 80% using camera-acquired printed MNIST-digits. Quantization of the neural network significantly reduced memory requirements with a less than 0.1% loss in accuracy compared to a full-precision network, making machine learning inferences on low-power microcontrollers possible. 152 J or 4.41 × 10 20 photons required for training and verification of an artificial neural network were harvested with 64 cm 2 photovoltaic area in less than 24 hours under 1000 lux illumination. Ambient light harvesters provide a new generation of self-powered and "smart" IoT devices powered through an energy source that is largely untapped., Competing Interests: Authors declare no competing interests., (This journal is © The Royal Society of Chemistry.)