jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lei Zhang | Gábor Cśanyi | E. van der Giessen | Francesco Maresca
Source: arXiv (Cornell University) (preprint, 2023-07-19)
Field: Materials Science — Materials Chemistry

Efficiency, Accuracy, and Transferability of Machine Learning Potentials: Application to Dislocations and Cracks in Iron Machine learning interatomic potentials (ML-IAPs) enable quantum-accurate, classical molecular dynamics simulations of large systems, beyond reach of density functional theory (DFT). Yet, their efficiency and ability to predict systems larger than DFT supercells are not fully explored, posing a question regarding transferability to large-scale simulations with defects (e.g. dislocations, cracks). Here, we apply a three-step validation approach to body-centered-cubic iron. First, accuracy and efficiency are assessed by optimizing ML-IAPs based on four state-of-the-art ML packages. The Pareto front of computational speed versus testing root-mean-square-error (RMSE) is computed. Second, benchmark properties relevant to plasticity and fracture are evaluated. Their average relative error Q with respect to DFT is found to correlate with RMSE. Third, transferability of ML-IAPs to dislocations and cracks is investigated by using per-atom model uncertainty quantification. The core structures and Peierls barriers of screw, M111 and three edge dislocations are compared with DFT. Traction-separation curve and critical stress intensity factor (K_Ic) are also predicted. Cleavage on the pre-existing crack plane is found to be the zero-temperature atomistic fracture mechanism of pure body-centered-cubic iron under mode-I loading, independent of ML package and training database. Quantitative predictions of dislocation glide paths and KIc can be sensitive to database, ML package, cutoff radius, and are limited by DFT accuracy. Our results highlight the importance of validating ML-IAPs by using indicators beyond RMSE. Moreover, significant computational speed-ups can be achieved by using the most efficient ML-IAP package, yet the assessment of the accuracy and transferability should be performed with care. Density functional theory ; Materials science ; Mean squared error ; Dislocation ; Atom (system on chip) ; Computer science ; Mathematics ; Computational chemistry ; Statistics ; Chemistry ; Composite material Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Md. Nazrul Islam | Md. Saikat Islam Khan | Md. Nahid Hasan | Mohammad Abu Yousuf
Source:  (article, 2023-12-21)
Field: Materials Science — Materials Chemistry

Computing Confinement Loss of Open-Channels Based PCF-SPR Sensor with ANN Approach Surface plasmon resonance (SPR) is a sensitive spectroscopic technique for measuring changes in the refractive index (RI) of a medium in contact with a sensor surface. Confinement loss computation is crucial for designing an SPR-based photonic crystal fiber (PCF) sensor as it directly impacts its performance and sensitivity. The study highlights the simple and fast-training feed-forward Artificial Neural Network (ANN) as a technique for calculating the confinement loss of an open-channels-based PCF-SPR sensor. Moreover, ANN demonstrates proficiency in generating precise predictions for the aforementioned optical properties within the conventional parameter range, encompassing wavelengths between 0.60 μm and 0.90 μm, as well as an analyte RI range spanning from 1.33 to 1.40. Additionally, this work demonstrates a higher speed in predicting output values for varying sensor structural parameters than direct numerical simulation techniques. This study signifies a noteworthy progression in the field through the utilization of ANN-driven optimization methods for integrated silicon photonics devices. This advancement leads to improved speed and accuracy in nredicting sensor outputs. Computer science ; Computer network ; Optoelectronics ; Materials science High voltage insulation and dielectric phenomena ; Sensor Technology and Measurement Systems ; Electrostatic Discharge in Electronics
Authors: B. Sai Teja Reddy | Shufali Ashraf Wani | S. K. Amizhtan | Chillu Naresh | R. Sarathi
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2023-11-17)
Field: Materials Science — Materials Chemistry

Understanding the Surface Discharge Activity With Nano Oil-Pressboard Insulation Under AC and Lightning Impulse Voltages The present study investigates the performance of nanofluid-impregnated pressboard material for surface discharges. Surface discharge is simulated as per IEC 60112 standard with nano TiO2 dispersed mineral oil-impregnated pressboard. During surface discharge, multiple activities, such as localized surface discharge inception followed by bright light occurrences, bubble formation, and bridging of electrodes, occur over an extended period of time. The surface discharge activity under ac and transient voltage profiles was monitored by adopting ultrahigh-frequency (UHF) and optical detection sensors. The pressboard material impregnated with nanoparticle dispersed fluid shows that 9.7% improvement is surface discharge inception voltage (SDIV) compared to pressboard impregnated with pure transformer oil. Furthermore, three machine learning (ML) algorithms, support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN) algorithms were used to evaluate the best-fit model for classifying the various stages of the surface discharge process. Classification accuracy is achieved to be 97.3% with the optimized parameters. Laser-induced breakdown spectroscopy (LIBS) identifies that the elemental composition at the discharge sites on the pressboard and the plasma temperature of carbon traces were evaluated. Plasma temperature for lightning impulse treated is lower due to the instantaneous breakdown occurring in the surface of the pressboard in comparison to the ac voltage profile. Pressboard ; Impulse (physics) ; Voltage ; Partial discharge ; Materials science ; Insulation system ; Electrical engineering ; Electrical treeing ; Composite material ; Transformer oil ; Lightning (connector) ; Nano- ; Transformer ; Engineering ; Physics ; Power (physics) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Aerosol Filtration and Electrostatic Precipitation
Authors: Seung Chae Yoon | Je Youl Kong | Jea Myoung Park | Kye Jeong Park | Joo Sik Hyun
Source: The International Journal of Advanced Manufacturing Technology (article, 2023-11-23)
Field: Materials Science — Metals and Alloys

Analysis of characteristic prediction of aluminized boron steel after the hot stamping process using an image color-based neural network Hot stamping ; Materials science ; Coating ; Metallurgy ; Quenching (fluorescence) ; Layer (electronics) ; Stamping ; Boron ; Diffusion ; Composite material ; Embrittlement Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Corrosion Behavior and Inhibition
Authors: Jonas Winklmann | Dimitrios Tsevas | Martin Schulz
Source:  (article, 2023-09-17)
Field: Materials Science — Materials Chemistry

Realistic Neutral Atom Image Simulation Neutral atom quantum computers require accurate single atom detection for the preparation and readout of their qubits. This is usually done using fluorescence imaging. The occupancy of an atom site in these images is often somewhat ambiguous due to the stochastic nature of the imaging process. Further, the lack of ground truth makes it difficult to rate the accuracy of reconstruction algorithms. We introduce a bottom-up simulator that is capable of generating sample images of neutral atom experiments from a description of the actual state in the simulated system. Possible use cases include the creation of exemplary images for demonstration purposes, fast training iterations for deconvolution algorithms, and generation of labeled data for machine-learning-based atom detection approaches. The implementation is available through our GitHub as a C library or wrapped Python package. We show the modeled effects and implementation of the simulations at different stages of the imaging process. Not all real-world phenomena can be reproduced perfectly. The main discrepancies are that the simulator allows for only one characterization of optical aberrations across the whole image, supports only discrete atom locations, and does not model all effects of complementary metal-oxide-semiconductor (CMOS) cameras perfectly. Nevertheless, our experiments show that the generated images closely match real-world pictures to the point that they are practically indistinguishable and can be used as labeled data for training the next generation of detection algorithms. Computer science ; Qubit ; Deconvolution ; Python (programming language) ; Atom (system on chip) ; Computational science ; Algorithm ; Artificial intelligence ; Computer vision ; Quantum ; Physics ; Parallel computing Electronic and Structural Properties of Oxides ; Quantum Computing Algorithms and Architecture ; Quantum Information and Cryptography
Authors: Chi‐Ching Lee | Yu-Chieh Lin | Teng Yu Pan | Cheng Yang | Pei‐Hsuan Li | Sin You Chen | Jhih Jie Gao | Chi Yang | Lichieh Julie Chu | Po-Jung Huang | Yuan‐Ming Yeh | Ping Tang | Yu‐Sun Chang | Jau‐Song Yu | Yen-Chang Hsiao
Source: Analytical Chemistry (article, 2023-10-11)
Field: Chemistry — Spectroscopy

HeapMS: An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks The process of peak picking and quality assessment for multiple reaction monitoring (MRM) data demands significant human effort, especially for signals with low abundance and high interference. Although multiple peak-picking software packages are available, they often fail to detect peaks with low quality and do not report cases with low confidence. Furthermore, visual examination of all chromatograms is still necessary to identify uncertain or erroneous cases. This study introduces HeapMS, a web service that uses artificial intelligence to assist with peak picking and the quality assessment of MRM chromatograms. HeapMS applies a rule-based filter to remove chromatograms with low interference and high-confidence peak boundaries detected by Skyline. Additionally, it transforms two histograms (representing light and heavy peptides) into a single encoded heatmap and performs a two-step evaluation (quality detection and peak picking) using image convolutional neural networks. HeapMS offers three categories of peak picking: uncertain peak picking that requires manual inspection, deletion peak picking that requires removal or manual re-examination, and automatic peak picking. HeapMS acquires the chromatogram and peak-picking boundaries directly from Skyline output. The output results are imported back into Skyline for further manual inspection, facilitating integration with Skyline. HeapMS offers the benefit of detecting chromatograms that should be deleted or require human inspection. Based on defined categories, it can significantly reduce human workload and provide consistent results. Furthermore, by using heatmaps instead of histograms, HeapMS can adapt to future updates in image recognition models. The HeapMS is available at: https://github.com/ccllabe/HeapMS. Skyline ; Convolutional neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Histogram ; Pipeline (software) ; Process (computing) ; Interference (communication) ; Data mining ; Image (mathematics) ; Channel (broadcasting) Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Cell Image Analysis Techniques
Authors: Nooshin Noshiri | Michael A. Beck | Christopher P. Bidinosti | Christopher J. Henry
Source: arXiv (Cornell University) (review, 2023-06-15)
Field: Chemistry — Analytical Chemistry

A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object. It can capture detailed information about the chemical and physical properties of agricultural crops. Due to its wide spectral range, compared with multispectral- or RGB-based imaging methods, HSI can be a more effective tool for monitoring crop health and productivity. With the advent of this imaging tool in agrotechnology, researchers can more accurately address issues related to the detection of diseased and defective crops in the agriculture industry. This allows to implement the most suitable and accurate farming solutions, such as irrigation and fertilization before crops enter a damaged and difficult-to-recover phase of growth in the field. While HSI provides valuable insights into the object under investigation, the limited number of HSI datasets for crop evaluation presently poses a bottleneck. Dealing with the curse of dimensionality presents another challenge due to the abundance of spectral and spatial information in each hyperspectral cube. State-of-the-art methods based on 1D- and 2D-CNNs struggle to efficiently extract spectral and spatial information. On the other hand, 3D-CNN-based models have shown significant promise in achieving better classification and detection results by leveraging spectral and spatial features simultaneously. Despite the apparent benefits of 3D-CNN-based models, their usage for classification purposes in this area of research has remained limited. This paper seeks to address this gap by reviewing 3D-CNN-based architectures and the typical deep learning pipeline, including preprocessing and visualization of results, for the classification of hyperspectral images of diseased and defective crops. Furthermore, we discuss open research areas and challenges when utilizing 3D-CNNs with HSI data. Hyperspectral imaging ; Computer science ; Convolutional neural network ; Multispectral image ; Artificial intelligence ; Bottleneck ; Precision agriculture ; Pattern recognition (psychology) ; Spatial analysis ; Field (mathematics) ; Object detection ; Preprocessor ; Remote sensing ; Machine learning ; Agriculture Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Anjali Khuntia | Jayeeta Mitra
Source: JSFA reports (article, 2023-09-26)
Field: Materials Science — Biomaterials

Ascorbic acid nanoencapsulation using polyelectrolyte complex formation and optimization using hybrid artificial neural network‐genetic algorithm Abstract Backgrounds The polyelectrolyte complex (PEC) formation using chitosan (Cs) and alginate (Alg) has been employed to carry out Vitamin C (VC) nanoencapsulation because of its instability in adverse conditions. This method includes VC nanoencapsulation using physical cross‐linking. The effect of the mixing order as well as the different mass ratios of both the polymer and the VC content, were investigated on the size, pdi, zeta potential, encapsulation efficiency (EE%), and yield%. Hence, considering different mass ratios of both polymer (4:1–1:4) and VC content (10%–30% w/w of Cs) as independent variables, PECs were formed using 0.1% (w/v) of Cs and Alg solution. Result The result showed that, for equal mass ratio (1:1) of Cs and Alg, the addition of Alg solution to Cs solution led to lower particle size, while higher particle size was observed with reverse order of addition. However, no significant effect of polymer mass ratios was observed on particle size and NPs stability, while EE varied with VC concentration. Artificial neural network (ANN) was applied to train the experimental parameters Cs: Alg ratios ( X 1 : X 2 ) and VC content ( X 3 ) for the output variables. Moreover, process optimization was carried out using multi‐objective genetic algorithm (MOGA) with the goal of lowering particle size and increasing EE while increasing nanoparticle yield. Conclusion The PEC method effectively encapsulated VC as obtained from higher EE. The Cs: Alg ratio of 4:2.1 and 18% VC content (w/w of Cs) was found optimum for nanoencapsulation. The final ANN‐GA model showed an acceptable agreement with experimental data. Particle size ; Polyelectrolyte ; Ascorbic acid ; Polymer ; Zeta potential ; Yield (engineering) ; Chitosan ; Materials science ; Particle swarm optimization ; Nanoparticle ; Chemical engineering ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Mathematics ; Nanotechnology ; Algorithm ; Organic chemistry ; Composite material ; Physical chemistry Electrospun Nanofibers in Biomedical Applications ; Polymer Nanocomposite Synthesis and Irradiation ; Nanocomposite Films for Food Packaging
Authors: Youfa Zhou | Ping Zhang | Jiaqi Li | Xurui Mao
Source: Nanotechnology (article, 2023-09-09)
Field: Materials Science — Materials Chemistry

Inhibitory artificial synapses based on photoelectric co-modulation of graphene/WSe<sub>2</sub> van der Waals heterojunctions Optical artificial synapses possess several advantages, including high bandwidth, strong interference immunity, and ultra-fast signal transmission, overcoming the limitations of electrically stimulated synapses. Among various functional materials, 2D materials exhibit exceptional optical and electrical properties. By utilizing van der Waals heterostructures formed by these materials through rational design, synaptic devices can mimic the information perception ability of biological systems. This lays the foundation for low-energy artificial vision systems and neuromorphic computing. This study introduces an inhibitory artificial synapse based on photoelectric co-modulation of graphene/WSe<sub>2</sub>van der Waals heterojunctions. By synergistically applying gate voltage and light pulses, we simulate memory and logic functions observed in the brain's visual cortex. We achieve the construction of inhibitory synapses, enabling properties such as postsynaptic current response, short-term and long-term plasticity, and paired-pulse facilitation. Additionally, we accomplish the inverse recovery of device conductivity through separate gate voltage stimulation. Through bidirectional modulation of the artificial synaptic conductance, we construct an artificial hardware neural network that achieves 92.5% accuracy in recognizing handwritten digital images from the MNIST dataset. The network also has good recognition accuracy for handwritten digital images with different standard deviation Gaussian noise applied and other datasets. Furthermore, we successfully mimic the neural behavior of aversive learning for alcohol withdrawal in alcoholic patients using the device properties. The promising capabilities of artificial synapses constructed through electrical and optical synergistic modulation make them suitable for wearable electronics and artificial vision systems. Materials science ; Graphene ; Heterojunction ; van der Waals force ; Photoelectric effect ; Modulation (music) ; Nanotechnology ; Optoelectronics ; Condensed matter physics ; Chemical physics ; Quantum mechanics ; Molecule ; Physics 2D Materials and Applications ; Advanced Memory and Neural Computing ; Graphene research and applications
Authors: Li Li | Yangyao Peng | Dongjie Zhang | Qi Zeng | Xueli Chen
Source:  (article, 2023-01-26)
Field: Chemistry — Analytical Chemistry

Raman spectra-based structural classification analysis of flavone and isoflavone Flavonoids are natural compounds with diverse structures. This type of nature product is considered to possess a wide range of health beneficial effects. Different skeleton structures and substituent groups lead to different Raman spectral features. In this work, we developed three Raman spectrum analysis methods based on artificial intelligence to classify 18 flavonoids. Firstly, applying principal component analysis (PCA) as dimension reduction method, we compress the 1300cm<sup>-1</sup> -1600cm<sup>-1</sup> spectral band into several important variables. The results obtained by the preprocessing methods were combined with K-Nearest Neighbor algorithm (KNN), support vector machine (SVM) for classification. Secondly, the combination of relevant features was taken by advanced machine learning method of random forest (RF). In terms of the accuracy of the results, all the methods achieved acceptable classification accuracy, which was almost over 84% on the test set. The experimental results demonstrated that the Raman spectroscopy study based on corresponding unique vibration mode exhibited application prospects in chemical structure classification and pharmacological activity prediction. Support vector machine ; Principal component analysis ; Raman spectroscopy ; Artificial intelligence ; Random forest ; Pattern recognition (psychology) ; k-nearest neighbors algorithm ; Dimensionality reduction ; Preprocessor ; Computer science ; Biological system ; Mathematics ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Flavonoids in Medical Research ; Phytochemicals and Antioxidant Activities
Authors: I Gede Totok Suryawan | Made Sudarma | I Ketut Gede Darma Putra | Oka Sudana
Source:  (article, 2023-12-06)
Field: Materials Science — Polymers and Plastics

Classifying Ikat Gringsing Woven Cloth Motifs Using Convolutional Neural Network Indonesia boasts a unique traditional woven fabric called ikat gringsing, created using the double ikat technique and natural materials from Tenganan Village, Pegringsingan, Bali. The motifs are inspired by the surrounding nature, featuring animals and plants commonly found in the area. The intricate and diverse designs of gringsing woven cloth have attracted many tourists, and this cloth has the potential to boost Bali's and Indonesia's tourism sector. However, it can be challenging for tourists to find detailed information about gringsing woven fabrics, and some may not even know the names of the motifs that catch their eye. To address this issue, a deep learning model utilizing CNN has been developed to introduce ikat gringsing woven fabric motifs. With an accuracy rate of 82%, the model successfully recognized 402 fabric motifs out of a total of 470 datasets. Utilizing the dropout technique to prevent overfitting resulted in consistent accuracy values throughout the training and testing process. The model's validation results in the training process averaged at 86%, while the testing process yielded an accuracy rate of 82% out of the 10 folds. Convolutional neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Artificial neural network Textile materials and evaluations