Virtual Screening for Perovskites Discovery (Proposals Track)

Andrea Karlova (UCL); Cameron C.L. Underwood (University of Surrey); Ravi Silva (University of Surrey)

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Abstract

Re-inventing the global energy harvesting system from fossil fuels to renewables is essential for reducing greenhouse gas emissions in line with current climate targets. Perovskite photovoltaics (PVs), the class of materials with relatively unexplored material configurations, play key role in solar energy generation due to their low manufacturing cost and exceptional optoelectronic properties. Without the efficient utilisation of machine learning, the discovery and manufacturing process could take a dangerously long time. We present a high-throughput computational design that leverages machine learning algorithms at various steps in order to assess the suitability of the organic molecules for the perovskite crystals.