PASCAL is an automated platform for spin coating and annealing thin films onto small (>2x2 cm) substrates, aimed af increasing experimental throughput in the pursuit of designing better perovskite solar cells. Perovskite solar cells are infinitely tunable, as they can be formed with combinations of nearly half of the periodic table, presenting the excitement of limitless possibilities and the curse of dimensionality. With PASCAL, we aim to increase our experimental search rate by orders of magnitude, enabling a more systematic and exhaustive approach toward exploring the vast compositional space of interest for solar cell design.
pascal_timelapse.mp4
- Computed material properties -> manifold embedding / dimensionality reduction into "behavior space"
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update compositions of interest using in-line during experiment
- bayesian approach with operator input (XRD, UV-Vis, etc)
- find covariances between composition/fabrication conditions
- point towards broader design philosophies in perovskite solar cells
- python=3.7.9=h26836e1_0
See our fenningresearchgroup.com/https://github.com/fenning-research-group/PASCAL_TrainView https://github.com/fenning-research-group/PASCAL_TrainView
and https://github.com/dnzckn/BO-PV repo's for additional capabilities