Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning

A fundamental challenge in the design of LEDs is to maximise electro-luminescence efficiency at high current densities. We simulate GaN-based LED structures that delay the onset of efficiency droop by spreading carrier concentrations evenly across the active region. Statistical analysis and machine learning effectively guide the selection of the next LED structure to be examined based upon its expected efficiency as well as model uncertainty. This active learning strategy rapidly constructs a model that predicts Poisson-Schrödinger simulations of devices, and that simultaneously produces structures with higher simulated efficiencies.B.R.-L., K.B. and T.L. acknowledge funding support from the Los Alamos National Laboratory (LANL) Laboratory Directed Research and Development (LDRD) DR (#20140013DR) on Materials Informatics. B.R.-L. and C.J.H. acknowledge funding support from the EPSRC Programme Grant “Lighting the Future” (#EP/I012591/1)

Analysis of defect-related inhomogeneous electroluminescence in InGaN/GaN QW LEDs

The inhomogeneous electroluminescence (EL) of InGaN/GaN quantum well light emitting diode structures was investigated in this study. Electroluminescence hyperspectral images showed that inhomogeneities in the form of bright spots exhibited spectrally blue-shifted and broadened emission. Scanning electron microscopy combined with cathodoluminescence (SEM-CL) was used to identify hexagonal pits at the centre of approximately 20% of these features. Scanning transmission electron microscopy imaging with energy dispersive X-ray spectroscopy (STEM-EDX) indicated there may be p-doped AlGaN within the active region caused by the presence of the pit. Weak beam dark-field TEM (WBDF-TEM) revealed the presence of bundles of dislocations associated with the pit, suggesting the surface features which cause the inhomogeneous EL may occur at coalescence boundaries, supported by trends in the number of features observed across the wafer.The European Research Council has provided financial support under the European Community’s Seventh Framework Programme/ ERC grant agreement no. 279361 (MACONS).This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.spmi.2016.03.03

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

Rapid source characterization of the Maule earthquake using Prompt Elasto‐Gravity Signals

Abstract The recently identified Prompt Elasto‐Gravity Signals (PEGS), generated by large earthquakes, propagate at the speed of light and are sensitive to the earthquake magnitude and focal mechanism. These characteristics make PEGS potentially very advantageous for tsunami early warning, which relies on fast and accurate estimation of the magnitude of large offshore earthquakes. PEGS‐based early warning does not suffer from the problem of magnitude estimation saturation, that P‐wave based early warning algorithms have, and could be faster than Global Navigation Satellite Systems (GNSS)‐based systems while not requiring a priori assumptions on slip distribution. We use a deep learning model called PEGSNet to evaluate the possibility to estimate in real time the evolution of the magnitude of big earthquakes in the tsunamigenic zone of Chile. The model is a Convolutional Neural Network (CNN) trained on a database of synthetic PEGS – simulated for an exhaustive set of possible earthquakes distributed along the Chilean subduction zone – augmented with empirical noise. The approach is multi‐station and leverages the information recorded by the seismic network to estimate as fast as possible the magnitude and location of an ongoing earthquake. Our results indicate that PEGSNet could have estimated that the magnitude of the 2010 M w 8.8 Maule earthquake was above 8.7, 90 seconds after origin time. Our offline simulations using real data and noise recordings further support the instantaneous tracking of the source time function of the earthquake and show that deploying seismic stations in optimal locations could improve the performance of the algorithm

Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano

International audienceVolcanic tremor is key to our understanding of active magmatic systems, but due to its complexity, there is still a debate concerning its origins and how it can be used to characterize eruptive dynamics. In this study we leverage machine learning techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La Réunion island) to describe specific patterns of seismic signals recorded during eruptions. These results unveil what we interpret as signals associated with various eruptive dynamics of the volcano, including the effusion of a large volume of lava during the August-October 2015 eruption as well as the closing of the eruptive vent during the September-November 2018 eruption. The machine learning workflow we describe can easily be applied to other active volcanoes, potentially leading to an enhanced understanding of the temporal and spatial evolution of volcanic eruptions. Plain Language Summary A good understanding of volcanic activity is key to managing volcanic hazards resulting from eruptive activity. Volcanic tremor is a continuous seismic signal often seen during eruptions associated with the flow of magma through the volcano and is thus an extremely useful tool in characterizing the progression and phases of eruptions. In this study we study this signal at the Piton de la Fournaise volcano, on La Réunion island. Using machine learning algorithms, we investigate characteristics of this signal emitted by the volcano during eruptions to reveal the fundamental frequency at which it occurs, as well as changes in eruptive state that occur during some eruptions in our data set. This workflow may be applied to other volcanos to further our understanding of eruptive dynamics

Similarity of fast and slow earthquakes illuminated by machine learning

Tectonic faults fail in a spectrum of modes, ranging from earthquakes to slow slip events. The physics of fast earthquakes are well described by stick–slip friction and elastodynamic rupture; however, slow earthquakes are poorly understood. Key questions remain about how ruptures propagate quasi-dynamically, whether they obey different scaling laws from ordinary earthquakes and whether a single fault can host multiple slip modes. We report on laboratory earthquakes and show that both slow and fast slip modes are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Using machine learning, we find that acoustic emissions generated during shear of quartz fault gouge under normal stress of 1–10 MPa predict the timing and duration of laboratory earthquakes. Laboratory slow earthquakes reach peak slip velocities of the order of 1 × 10−4 m s−1 and do not radiate high-frequency elastic energy, consistent with tectonic slow slip. Acoustic signals generated in the early stages of impending fast laboratory earthquakes are systematically larger than those for slow slip events. Here, we show that a broad range of stick–slip and creep–slip modes of failure can be predicted and share common mechanisms, which suggests that catastrophic earthquake failure may be preceded by an organized, potentially forecastable, set of processes