Laboratory earthquake forecasting: A machine learning competition.

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google's ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.

Optimization of Transparent Organic Light-Emitting Diodes by Simulation-Based Design of Organic Capping Layers.

In this work, we use the transfer matrix method to optimize TPBi capping layers deposited on organic light emitting diodes with respect to light extraction and transmittance. The green transparent organic light emitting diodes comprise three organic semiconductors (CBP, Ir(ppy)3 and TPBi) forming an efficient simplified phosphorescent organic light emitting diode stack. A transparent cathode of 2 nm Cs2CO3, 2 nm Al and 16 nm Au is deposited by thermal evaporation. The diode stack as well as the capping layer are deposited by organic vapor phase deposition. The refractive indices and extinction coefficients of all materials in the transparent organic light emitting diodes (glass, indium tin oxide, organic semiconductors and cathode) are determined using spectroscopic ellipsometry combined with optical transmittance and reflectance measurements. With these spectrally resolved data, we calculate the transmittance of transparent organic light emitting diodes with TPBi capping layers of different thicknesses. The results were validated with high accuracy in the visible spectral range and beyond (360 nm-1000 nm) by a series of experiments. By choosing a TPBi capping layer of optimized thickness (here 50 nm), we fabricated transparent organic light emitting diodes with an optical transmittance which was strongly enhanced from 47% (reference without capping layer) to 65%, measured at 555 nm.