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Abstract Among promising applications of metal‐halide perovskite, the most research progress is made for perovskite solar cells (PSCs). Data from myriads of research work enables leveraging machine learning (ML) to significantly expedite material and device optimization as well as potentially design novel configurations. This paper represents one of the first efforts in providing open‐source ML tools developed utilizing the Perovskite Database Project (PDP), the most comprehensive open‐source PSC database to date with over 43 000 entries from published literature. Three ML model architectures with short‐circuit current density (J_sc) as a target are trained exploiting the PDP. Using the XGBoost architecture, a root mean squared error (RMSE) of 3.58 , R²of 0.35 and a mean absolute percentage error (MAPE) of 9.49% are achieved. This performance is comparable to results reported in literature, and through further investigation can likely be improved. To overcome challenges with manual database creation, an open‐source data cleaning pipeline is created for PDP data. Through the creation of these tools, which have been published on GitHub, this research aims to make ML available to aid the design for PSC while showing the already promising performance achieved. The tools can be adapted for other applications, such as perovskite light‐emitting diodes (PeLEDs), if a sufficient database is available. 

Abstract The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.