Applying Active Learning to the Screening of Molecular Oxygen Evolution Catalysts

The oxygen evolution reaction (OER) can enable green hydrogen production; however, the state-of-the-art catalysts for this reaction are composed of prohibitively expensive materials. In addition, cheap catalysts have associated overpotentials that render the reaction inefficient. This impels the search to discover novel catalysts for this reaction computationally. In this communication, we present machine learning algorithms to enhance the hypothetical screening of molecular OER catalysts. By predicting calculated binding energies using Gaussian process regression (GPR) models and applying active learning schemes, we provide evidence that our algorithm can improve computational efficiency by guiding simulations towards candidates with promising OER descriptor values. Furthermore, we derive an acquisition function that, when maximized, can identify catalysts that can exhibit theoretical overpotentials that circumvent the constraints imposed by linear scaling relations by attempting to enforce a specific mechanism. Finally, we provide a brief perspective on the appropriate sets of molecules to consider when screening complexes that could be stable and active for this reaction.

Extending the Applicability of the Meyer–Peter and Müller Bed Load Transport Formula

The present paper deals with the applicability of the Meyer–Peter and Müller (MPM) bed load transport formula. The performance of the formula is examined on data collected in a particular location of Nestos River in Thrace, Greece, in comparison to a proposed Εnhanced MPM (EMPM) formula and to two typical machine learning methods, namely Random Forests (RF) and Gaussian Processes Regression (GPR). The EMPM contains new adjustment parameters allowing calibration. The EMPM clearly outperforms MPM and, also, it turns out to be quite competitive in comparison to the machine learning schemes. Calibrations are repeated with suitably smoothed measurement data and, in this case, EMPM outperforms MPM, RF and GPR. Data smoothing for the present problem is discussed in view of a special nearest neighbor smoothing process, which is introduced in combination with nonlinear regression.

BRNN-LSTM for Initial Access in Millimeter Wave Communications

The use of beamforming technology in standalone (SA) millimeter wave communications results in directional transmission and reception modes at the mobile station (MS) and base station (BS). This results in initial beam access challenges, since the MS and BS are now compelled to perform spatial search to determine the best beam directions that return highest signal levels. The high number of signal measurements here prolongs access times and latencies, as well as increasing power and energy consumption. Hence this paper proposes a first study on leveraging deep learning schemes to simplify the beam access procedure in standalone mmWave networks. The proposed scheme combines bidirectional recurrent neural network (BRNN) and long short-term memory (LSTM) to achieve fast initial access times. Namely, the scheme predicts the best beam index for use in the next time step once a MS accesses the network, e.g., transition from sleep to active (or idle) modes. The scheme eliminates the need for beam scanning, thereby achieving ultra-low access times and energy efficiencies as compared to existing methods.

END-TO-END PHYSICS-INFORMED REPRESENTATION LEARNING FOR SATELLITE OCEAN REMOTE SENSING DATA: APPLICATIONS TO SATELLITE ALTIMETRY AND SEA SURFACE CURRENTS

This paper addresses physics-informed deep learning schemes for satellite ocean remote sensing data. Such observation datasets are characterized by the irregular space-time sampling of the ocean surface due to sensors’ characteristics and satellite orbits. With a focus on satellite altimetry, we show that end-to-end learning schemes based on variational formulations provide new means to explore and exploit such observation datasets. Through Observing System Simulation Experiments (OSSE) using numerical ocean simulations and real nadir and wide-swath altimeter sampling patterns, we demonstrate their relevance w.r.t. state-of-the-art and operational methods for space-time interpolation and short-term forecasting issues. We also stress and discuss how they could contribute to the design and calibration of ocean observing systems.

Application of Three Deep Learning Schemes Into Oceanic Eddy Detection

Recent years have witnessed the increase in applications of artificial intelligence (AI) into the detection of oceanic features. Oceanic eddies, ubiquitous in the global ocean, are important in the transport of materials and energy. A series of eddy detection schemes based on oceanic dynamics have been developed while the AI-based eddy identification scheme starts to be reported in literature. In the present study, to find out applicable AI-based schemes in eddy detection, three AI-based algorithms are employed in eddy detection, including the pyramid scene parsing network (PSPNet) algorithm, the DeepLabV3+ algorithm and the bilateral segmentation network (BiSeNet) algorithm. To justify the AI-based eddy detection schemes, the results are compared with one dynamic-based eddy detection method. It is found that more eddies are identified using the three AI-based methods. The three methods’ results are compared in terms of the numbers, sizes and lifetimes of detected eddies. In terms of eddy numbers, the PSPNet algorithm identifies the largest number of ocean eddies among the three AI-based methods. In terms of eddy sizes, the BiSeNet can find more large-scale eddies than the two other methods, because the Spatial Path is introduced into the algorithm to avoid destroying the eddy edge information. Regarding eddy lifetimes, the DeepLabV3+ cannot track longer lifetimes of ocean eddies.

Training of a Spiking Neural Network on spintronics based analog hardware for handwritten digit recognition

We trained <b>Spiking neural network </b>(SNN) using <b>spike time dependent plasticity (STDP)</b>-enabled learning under two different learning schemes in <b>MNIST data set</b>(hand written digit recognition). We showed how the post-neurons need to be far more in number than the output classes for larger data sets in the case of SNN for reasonably high accuracy number. We have also reported the net energy consumed for learning in the spintronic devices and associated transistor-based circuits that enable synaptic functionality for this SNN.

Training of a Spiking Neural Network on spintronics based analog hardware for handwritten digit recognition

We trained <b>Spiking neural network </b>(SNN) using <b>spike time dependent plasticity (STDP)</b>-enabled learning under two different learning schemes in <b>MNIST data set</b>(hand written digit recognition). We showed how the post-neurons need to be far more in number than the output classes for larger data sets in the case of SNN for reasonably high accuracy number. We have also reported the net energy consumed for learning in the spintronic devices and associated transistor-based circuits that enable synaptic functionality for this SNN.