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The conditions for rupture of a material commonly vary from sample to sample. Of great importance to applications are the conditions for rare-event rupture, but their measurements require many sam- ples and consume much time. Here, the conditions for rare-event rupture are measured by developing a high-throughput experiment. For each run of the experiment, 1,000 samples are printed under the same nominal conditions and pulled simultaneously to the same stretch. Identifying the rupture of individual samples is automated by processing the video of the experiment. Under monotonic load, the rupture stretch for each sample is recorded. Under cyclic load, the number of cycles to rupture for each sample is also recorded. Rare-event rupture is studied by using the Weibull distribution and the peak-over-threshold method. This work reaffirms that predict- ing rare events requires large datasets. The high-throughput exper- iments enable the prediction of rare events with high accuracy and confidence.