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Optimizing Perovskites for Solar Cells with AI and DFT

Researchers combined density functional theory (DFT) with machine learning (ML) to screen 41,400 metal halide perovskites (MHPs), identifying 10 promising candidates with improved stability and optoelectronic properties. Highlighting CsGe0.3125Sn0.6875I3 and CsGe0.0625Pb0.3125Sn0.625Br3, this study offers a new framework for optimizing perovskites for solar cells.

19 May 2024

InsectSound1000: A Rich Dataset for Acoustic Insect Monitoring

Researchers have introduced InsectSound1000, a dataset featuring over 169,000 labeled sound samples from 12 insect species. This dataset, recorded in an anechoic box with high precision, is primed for training deep-learning models to enhance pest and ecological monitoring systems.

19 May 2024

Enhanced CAD Integration Using Deep Learning

Researchers developed advanced deep learning (DL)-based automatic feature recognition (AFR) methods that significantly enhance computer-aided design (CAD), process planning (CAPP), and manufacturing (CAM) integration. Their approach, using the multidimensional attributed face-edge graph (maFEG) and Sheet-metalNet, a graph neural network, improves recognition accuracy and adapts to evolving datasets, addressing limitations of traditional and voxelized representations.

19 May 2024

Deep Learning Enhances Robot Obstacle Avoidance in Power Plants

Researchers developed a deep learning and particle swarm optimization (PSO) based system to enhance obstacle recognition and avoidance for inspection robots in power plants. This system, featuring a convolutional recurrent neural network (CRNN) for obstacle recognition and an artificial potential field method (APFM) based PSO algorithm for path planning, significantly improves accuracy and efficiency.

19 May 2024

Advanced Kiwifruit Soft Rot Detection with Deep Learning

Researchers presented a novel dual-branch selective attention capsule network (DBSACaps) for detecting kiwifruit soft rot using hyperspectral images. This approach, detailed in Nature, separates spectral and spatial feature extraction, then fuses them with an attention mechanism, achieving a remarkable 97.08% accuracy.

17 May 2024

Holographic System Blends Reality and Digital Content

Researchers present a groundbreaking holographic system in Nature, merging metasurface gratings, compact waveguides, and AI-driven holography algorithms to create vibrant 3D AR experiences. Their prototype, integrating a metasurface waveguide and phase-only SLM, achieves unmatched visual quality and represents a significant leap in wearable AR device development.

17 May 2024

Taste the Future: Novel E-Tongue Revolutionizes Liquid Sample Detection

Researchers introduce a novel electronic tongue (E-tongue), the multichannel triboelectric bioinspired E-tongue (TBIET), engineered with advanced triboelectric components on a single glass slide chip. Through comprehensive classification studies across medical, environmental, and beverage samples, the TBIET demonstrates exceptional taste classification accuracy, promising significant advancements in on-site liquid sample detection and analysis.

17 May 2024

Crack the Code: Advanced Method Enhances Train Rivet Inspection

This study proposes an innovative method for detecting cracks in train rivets using fluorescent magnetic particle detection (FMPFD) and instance segmentation, achieving high accuracy and recall. By enhancing the YOLOv5 algorithm and developing a single coil non-contact magnetization device, the researchers achieved significant improvements in crack detection.

17 May 2024

Mapping ML in Seismic Engineering

A recent scientometric review highlighted the transformative impact of machine learning (ML) in seismic engineering, showcasing advancements in material performance prediction and seismic resistance. The study, published in the journal Buildings, analyzed 3189 papers using the Scopus database, identifying key research trends and fostering collaboration within the field.

16 May 2024

AI Boosts Manufacturing Particle Detection

A novel framework combining deep learning and preprocessing algorithms significantly improved particle detection in manufacturing, addressing challenges posed by heterogeneous backgrounds. The framework, validated through extensive experimentation, enhanced in-situ process monitoring, offering robust, real-time solutions for diverse industrial applications.

16 May 2024

Machine Learning Tames Chaos on Edge Devices

This study highlights the use of a next-generation reservoir computing (NG-RC) algorithm to control chaotic dynamics with high sensitivity and efficiency. Implemented on an FPGA, the NG-RC-based controller outperformed conventional chaos control techniques by stabilizing various unstable states and trajectories with minimal power consumption, suggesting promising applications in diverse fields like autonomous systems and biological control.

16 May 2024

ChemCrow: AI-Driven Chemistry Breakthrough

In a Nature Machine Intelligence paper, researchers unveiled ChemCrow, an advanced LLM chemistry agent that autonomously tackles complex tasks in organic synthesis and materials design. By integrating GPT-4 with 18 expert tools, ChemCrow excels in chemical reasoning, planning syntheses, and guiding drug discovery, outperforming traditional LLMs and showcasing its potential to transform scientific research.

16 May 2024

Silent Speech Interface Using Graphene-Based Textile Strain Sensors and AI

Researchers introduced a groundbreaking silent speech interface (SSI) leveraging few-layer graphene (FLG) strain sensing technology and AI-based self-adaptation. Embedded into a biocompatible smart choker, the sensor achieved high accuracy and computational efficiency, revolutionizing communication in challenging environments.

15 May 2024

DPA-1: A Deep Potential Model for Molecular Simulation

Researchers introduced DPA-1, a deep potential model with a gated attention mechanism, for representing atomic system conformation and chemical spaces. DPA-1 demonstrated superior performance in learning potential energy surfaces (PES) compared to existing benchmarks, offering efficiency and interpretability.

15 May 2024

Machine Learning-based Predictive Modeling for Traffic Congestion

Researchers investigated the predictability of vehicle travel time and traffic status on Al-Madina Al-Monawara St in Amman, Jordan, using machine learning algorithms. Results showed high accuracy in predicting travel time and traffic status six hours ahead, with AdaBoost demonstrating exceptional performance. The study suggests integrating predictive models into navigation apps and leveraging recent traffic data for effective congestion identification and traffic management in urban areas.

15 May 2024

JARVIS-Leaderboard: Advancing Materials Design Benchmarking

The article introduces JARVIS-Leaderboard, an open-source platform facilitating materials design benchmarking across various categories like AI, electronic structure, force-field, quantum computation, and experiments. Integrated with NIST-JARVIS infrastructure, it offers a dynamic framework for comparing methods and datasets, fostering reproducibility and collaboration in materials science research.

15 May 2024

Better Structural Monitoring Using LSTM Neural Networks

Researchers utilized long-short-term memory (LSTM) neural networks to address sensor maintenance issues in structural monitoring systems, particularly during grid structure jacking construction. Their LSTM-based approach effectively recovered missing stress data by analyzing data autocorrelation and spatial correlations, showcasing superior accuracy compared to traditional methods.

14 May 2024

Smart Traffic Surveillance: Faster R-CNN for Vehicle Segmentation

Researchers introduced a novel deep learning approach based on faster R-CNN for segmenting vehicles in traffic videos, addressing challenges like occlusions and varying traffic densities. Through adaptive background modeling and topological active nets, the method achieved superior segmentation accuracy, showcasing its potential to enhance real-world traffic surveillance and management systems.

14 May 2024

SiTHv2: A Model for Global Evapotranspiration and Soil Moisture Dynamics

Researchers introduced SiTHv2, a terrestrial hydrosphere model, in Scientific Data, offering high-resolution daily estimates of evapotranspiration (ET) components and soil moisture dynamics. Utilizing satellite observations and reanalysis data, SiTHv2 was validated against in situ measurements, demonstrating accuracy and reliability for water cycle dynamics, drought assessment, and ecosystem health studies.

14 May 2024

Novel Metrics for Assessing Prediction Difficulty in Neural Networks

Researchers introduced innovative metrics to gauge the prediction difficulty of individual cases within datasets using neural networks (NN). These metrics, designed for fully connected feedforward NNs, offer a unique approach to understanding data complexity by considering factors like model complexity, double modeling, and predictive uncertainty.

14 May 2024