jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xihui Bian | Zitong Shi | Yingjie Shao | Yuanyuan Chu | Xiaoyao Tan
Source: Molecules (article, 2023-09-02)
Field: Chemistry — Analytical Chemistry

Variational Mode Decomposition for Raman Spectral Denoising As a fast and non-destructive spectroscopic analysis technique, Raman spectroscopy has been widely applied in chemistry. However, noise is usually unavoidable in Raman spectra. Hence, denoising is an important step before Raman spectral analysis. A novel spectral denoising method based on variational mode decomposition (VMD) was introduced to solve the above problem. The spectrum is decomposed into a series of modes (uk) by VMD. Then, the high-frequency noise modes are removed and the remaining modes are reconstructed to obtain the denoised spectrum. The proposed method was verified by two artificial noised signals and two Raman spectra of inorganic materials, i.e., MnCo ISAs/CN and Fe-NCNT. For comparison, empirical mode decomposition (EMD), Savitzky-Golay (SG) smoothing, and discrete wavelet transformation (DWT) are also investigated. At the same time, signal-to-noise ratio (SNR) was introduced as evaluation indicators to verify the performance of the proposed method. The results show that compared with EMD, VMD can significantly improve mode mixing and the endpoint effect. Moreover, the Raman spectrum by VMD denoising is more excellent than that of EMD, SG smoothing and DWT in terms of visualization and SNR. For the small sharp peaks, some information is lost after denoising by EMD, SG smoothing, DWT and VMD while VMD loses fewest information. Therefore, VMD may be an alternative method for Raman spectral denoising. Raman spectroscopy ; Noise reduction ; Smoothing ; Hilbert–Huang transform ; Noise (video) ; Wavelet ; Signal-to-noise ratio (imaging) ; Algorithm ; Analytical Chemistry (journal) ; Artificial intelligence ; Materials science ; Biological system ; Computer science ; Chemistry ; Optics ; Physics ; White noise ; Computer vision ; Chromatography ; Telecommunications ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Emma Mounra | Alhadji Malloum | Jean Jules Fifen | Jeanet Conradie
Source: Journal of Molecular Modeling (article, 2023-11-18)
Field: Materials Science — Materials Chemistry

Adsorption of some cationic dyes onto two models of graphene oxide We have optimized the complexes formed by the interaction of dyes with graphene oxides at the PW6B95-D3/def2-SVP level of theory. The SMD solvation model realizes the implicit solvation, and water is used as the solvent. Calculations are performed using the Gaussian 16 suite of program. QTAIM analysis is performed using the AIMAll program. Gibbs free energies as function of temperature are calculated using the TEMPO program. Graphene ; Adsorption ; Cationic polymerization ; Oxide ; Chemistry ; Chemical engineering ; Materials science ; Nanotechnology ; Organic chemistry ; Engineering Graphene research and applications ; Nanomaterials for catalytic reactions ; Graphene and Nanomaterials Applications
Authors: Kechen Song | Jiwen Yang | Guohui Wang
Source: Frontiers in Physics (article, 2023-10-20)
Field: Chemistry — Analytical Chemistry

A Swin transformer and MLP based method for identifying cherry ripeness and decay Cherries are a nutritionally beneficial and economically significant crop, with fruit ripeness and decay (rot or rupture) being critical indicators in the cherry sorting process. Therefore, accurately identifying the maturity and decay of cherries is crucial in cherry processing. With advancements in artificial intelligence technology, many studies have utilized photographs for non-destructive detection of fruit appearance quality. This paper proposes a cherry appearance quality identification method based on the Swin Transformer, which utilizes the Swin Transformer to extract cherry image feature information and then imports the feature information into classifiers such as multi-layer perceptron(MLP) and support vector machine(SVM) for classification. Through the comparison of multiple classifiers, the optimal classifier, namely, MLP, in combination with the Swin Transformer is obtained. Furthermore, performance comparisons are conducted with the original Swin-T method, traditional CNN models, and traditional CNN models combined with MLP. The results demonstrate the following: 1) The proposed method based on the Swin Transformer and MLP achieves an accuracy rate of 98.5%, which is 2.1% higher than the original Swin-T model and 1.0% higher than the best-performing combination of traditional CNN model and MLP. 2) The training time required for the Swin Transformer and MLP is only 78.43 s, significantly faster than other models. The experimental results indicate that the innovative approach of combining the Swin Transformer and MLP shows excellent performance in identifying cherry ripeness and decay. The successful application of this method provides a new solution for determining cherry appearance ripeness and decay. Therefore, this method plays a significant role in promoting the development of cherry sorting machines. Ripeness ; Support vector machine ; Transformer ; Artificial intelligence ; Pattern recognition (psychology) ; Perceptron ; Multilayer perceptron ; Computer science ; Machine learning ; Artificial neural network ; Engineering ; Voltage ; Horticulture Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Vahid Rahmani | Shah Nawaz | David Pennicard | Shabarish Pala Ramakantha Setty | H. Graafsma
Source: Journal of Applied Crystallography (article, 2023-01-06)
Field: Materials Science — Materials Chemistry

Data reduction for X-ray serial crystallography using machine learning Serial crystallography experiments produce massive amounts of experimental data. Yet in spite of these large-scale data sets, only a small percentage of the data are useful for downstream analysis. Thus, it is essential to differentiate reliably between acceptable data (hits) and unacceptable data (misses). To this end, a novel pipeline is proposed to categorize the data, which extracts features from the images, summarizes these features with the 'bag of visual words' method and then classifies the images using machine learning. In addition, a novel study of various feature extractors and machine learning classifiers is presented, with the aim of finding the best feature extractor and machine learning classifier for serial crystallography data. The study reveals that the oriented FAST and rotated BRIEF (ORB) feature extractor with a multilayer perceptron classifier gives the best results. Finally, the ORB feature extractor with multilayer perceptron is evaluated on various data sets including both synthetic and experimental data, demonstrating superior performance compared with other feature extractors and classifiers. Reduction (mathematics) ; Data reduction ; Crystallography ; X-ray crystallography ; Computer science ; X-ray ; Materials science ; Diffraction ; Chemistry ; Physics ; Optics ; Data mining ; Mathematics ; Geometry X-ray Diffraction in Crystallography ; Enzyme Structure and Function ; Mineral Processing and Grinding
Authors: Zhou Chen | Victor G. Bucklew | Perry S. Edwards | Chenji Zhang | Jinkai Yang | Philip J. Ryan | David Hughes | Xinshun Qu | Zhiwen Liu
Source: Applied Spectroscopy (article, 2023-03-11)
Field: Chemistry — Analytical Chemistry

Portable Diffuse Reflectance Spectroscopy of Potato Leaves for Pre-Symptomatic Detection of Late Blight Disease We report on the use of leaf diffuse reflectance spectroscopy for plant disease detection. A smartphone-operated, compact diffused reflectance spectrophotometer is used for field collection of leaf diffuse reflectance spectra to enable pre-symptomatic detection of the progression of potato late blight disease post inoculation with oomycete pathogen <i>Phytophthora infestans</i>. Neural-network-based analysis predicts infection with >96% accuracy, only 24 h after inoculation with the pathogen, and nine days before visual late blight symptoms appear. Our study demonstrates the potential of using portable optical spectroscopy in tandem with machine learning analysis for early diagnosis of plant diseases. Blight ; Phytophthora infestans ; Oomycete ; Diffuse reflectance infrared fourier transform ; Reflectivity ; Spectroscopy ; Pathogen ; Biology ; Horticulture ; Optics ; Microbiology ; Physics ; Astronomy Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Plant Pathogens and Fungal Diseases
Authors: Yunxiang Wang | Ziyuan Yang | Pan Hu | Sushmit Hossain | Zerui Liu | Tse‐Hsien Ou | Jiacheng Ye | Wei Wu
Source: Nanomaterials (article, 2023-09-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

End-to-End Diverse Metasurface Design and Evaluation Using an Invertible Neural Network Employing deep learning models to design high-performance metasurfaces has garnered significant attention due to its potential benefits in terms of accuracy and efficiency. A deep learning-based metasurface design framework typically comprises a forward prediction path for predicting optical responses and a backward retrieval path for generating geometrical configurations. In the forward design path, a specific geometrical configuration corresponds to a unique optical response. However, in the inverse design path, a single performance metric can correspond to multiple potential designs. This one-to-many mapping poses a significant challenge for deep learning models and can potentially impede their performance. Although representing the inverse path as a probabilistic distribution is a widely adopted method for tackling this problem, accurately capturing the posterior distribution to encompass all potential solutions remains an ongoing challenge. Furthermore, in most pioneering works, the forward and backward paths are captured using separate models. However, the knowledge acquired from the forward path does not contribute to the training of the backward model. This separation of models adds complexity to the system and can hinder the overall efficiency and effectiveness of the design framework. Here, we utilized an invertible neural network (INN) to simultaneously model both the forward and inverse process. Unlike other frameworks, INN focuses on the forward process and implicitly captures a probabilistic model for the inverse process. Given a specific optical response, the INN enables the recovery of the complete posterior over the parameter space. This capability allows for the generation of novel designs that are not present in the training data. Through the integration of the INN with the angular spectrum method, we have developed an efficient and automated end-to-end metasurface design and evaluation framework. This novel approach eliminates the need for human intervention and significantly speeds up the design process. Utilizing this advanced framework, we have effectively designed high-efficiency metalenses and dual-polarization metasurface holograms. This approach extends beyond dielectric metasurface design, serving as a general method for modeling optical inverse design problems in diverse optical fields. Path (computing) ; Computer science ; Probabilistic logic ; Process (computing) ; Invertible matrix ; Inverse ; Artificial neural network ; Metric (unit) ; Deep learning ; Artificial intelligence ; Computer engineering ; Machine learning ; Mathematics ; Engineering Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Photonic Crystals and Applications
Authors: Yubin Fan | Jin Yao | Din Ping Tsai
Source: Light Science & Applications (article, 2023-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advance of large-area achromatic flat lenses A new framework of light coherence optimization is proposed to design non-ideal broadband achromatic lenses, enabling large-scale flat lenses' implementation and high performance. The strategy paves the way for practical planar optical devices and full-color imaging systems. Achromatic lens ; Broadband ; Planar ; Optics ; Computer science ; Coherence (philosophical gambling strategy) ; Physics ; Computer graphics (images) Metamaterials and Metasurfaces Applications ; Optical Coatings and Gratings ; Advanced optical system design
Authors: Yun Tao
Source: IEEE Access (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Quantitative Analysis of Near-Infrared Spectroscopy of Blended Fabrics Based on Convolutional Neural Network Quantitative analysis of blended fabrics plays a vital role in quality inspection and trade. As a method yielding non-destructive, pollution-free, and easy-to-obtain data, near-infrared spectroscopy is becoming an important supplement to the detection methods of blended fabrics. In other near-infrared spectroscopy analysis tasks, convolutional neural network is becoming a mainstream model. This study aims to further solve the problems of nonlinearity, sample imbalance, and inaccurate labels in the actual near-infrared spectral dataset of blended fabrics. We first proposed to introduce the convolutional network widely applied in similar tasks to establish a quantitative analysis model of the near-infrared spectroscopy of blended fabrics. Two model variants closely related to the spectral data characteristics are considered: a deeper model and a model with feature attention mechanism. For the sample imbalance problem, a label balancing method based on kernel density estimation is proposed, and for the label noise problem, a noise tolerance method based on absolute error truncation is proposed. Comparative experiments are conducted on eight common blended fabric datasets. The results show that the modeling effect of the convolutional neural network model is generally better than that of traditional machine learning methods, especially the network model that can extract more complex time series data features. Thus, the improvements proposed for the data problem can significantly improve the modeling effect in relevant scenarios. The proposed model and methods meet the needs for the non-destructive quantitative analysis of blended fabrics. Computer science ; Convolutional neural network ; Artificial intelligence ; Kernel (algebra) ; Near-infrared spectroscopy ; Sample (material) ; Noise (video) ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Optics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Water Quality Monitoring and Analysis
Authors: Manuel Cordova | Lyndon Emsley
Source: Journal of the American Chemical Society (article, 2023-07-13)
Field: Chemistry — Spectroscopy

Chemical Shift-Dependent Interaction Maps in Molecular Solids Structure determination of molecular solids through NMR crystallography relies on the generation of a comprehensive set of candidate crystal structures and on the comparison of chemical shifts computed for those candidates with experimental values. Exploring the polymorph landscape of molecular solids requires extensive computational power, which leads to a significant bottleneck in the generation of the set of candidate crystals by crystal structure prediction (CSP) protocols. Here, we use a database of crystal structures with associated chemical shifts to construct three-dimensional interaction maps in molecular crystals directly derived from a molecular structure and its associated set of experimentally measured chemical shifts. We show how the maps obtained can be used to identify structural constraints for accelerating CSP protocols and to evaluate the likelihood of candidate crystal structures without requiring DFT-level chemical shift computations. Chemistry ; Crystal structure prediction ; Bottleneck ; Crystal (programming language) ; Crystal structure ; Chemical shift ; Molecule ; Computation ; Molecular solid ; Chemical physics ; Computational chemistry ; Crystallography ; Physical chemistry ; Algorithm ; Computer science ; Organic chemistry Advanced NMR Techniques and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Solid-state spectroscopy and crystallography
Authors: Zhelin Cui | Xinran Li | Tao Li | Mingyang Li
Source: Forests (article, 2023-06-23)
Field: Chemistry — Organic Chemistry

Improvement and Assessment of Convolutional Neural Network for Tree Species Identification Based on Bark Characteristics Efficient tree species identification is of great importance in forest inventory and management. As the textural properties of tree barks vary less notably as a result of seasonal change than other tree organs, they are more suitable for the identification of tree species using deep learning models. In this study, we adopted the ConvNeXt convolutional neural network to identify 33 tree species using the BarkNetV2 dataset, compared the classification accuracy values of different tree species, and performed visual analysis of the network’s visual features. The results show the following trends: (1) the pre-trained network weights exhibit up to 97.61% classification accuracy for the test set, indicating that the network has high accuracy; (2) the classification accuracy values of more than half of the tree species can reach 98%, while the confidence level of correct identification (probability ratio of true labels) of tree species images is relatively high; and (3) there is a strong correlation between the network’s visual attractiveness and the tree bark’s biological characteristics, which share similarities with humans’ organization of tree species. The method suggested in this study has the potential to increase the efficiency of tree species identification in forest resources surveys and is of considerable value in forest management. Tree (set theory) ; Identification (biology) ; Convolutional neural network ; Computer science ; Artificial intelligence ; Bark (sound) ; Set (abstract data type) ; Pattern recognition (psychology) ; Machine learning ; Ecology ; Mathematics ; Biology Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Remote Sensing in Agriculture
Authors: Kai Xu | Jiali Guo | Grazia Raciti | A. R. Goñi | M. I. Alonso | Xavier Borrisé | Ilaria Zardo | Mariano Campoy‐Quiles | J. S. Reparaz
Source: International Journal of Heat and Mass Transfer (article, 2023-06-22)
Field: Materials Science — Materials Chemistry

In-plane thermal diffusivity determination using beam-offset frequency-domain thermoreflectance with a one-dimensional optical heat source We present an innovative contactless method suitable to study in-plane thermal transport based on beam-offset frequency-domain thermoreflectance using a one-dimensional heat source with uniform power distribution. Using a one-dimensional heat source provides a number of advantages as compared to point-like heat sources, as typically used in time- and frequency-domain thermoreflectance experiments, just to name a few: (i) it leads to a slower spatial decay of the temperature field in the direction perpendicular to the line-shaped heat source, allowing to probe the temperature field at larger distances from the heater, hence, enhancing the sensitivity to in-plane thermal transport; (ii) the frequency range of interest is typically <100 kHz. This rather low frequency range is convenient regarding the cost of the required excitation laser system but, most importantly, it allows the study of materials without the presence of a metallic transducer with almost no influence of the finite optical penetration depth of the pump and probe beams on the thermal phase lag, which arises from the large thermal penetration depth imposed by the used frequency range. We also show that for the case of a harmonic thermal excitation source, the phase lag between the thermal excitation and thermal response of the sample exhibits a linear dependence with their spatial offset, where the slope is proportional to the inverse of the thermal diffusivity of the material. We demonstrate the applicability of this method to the cases of: (i) suspended thin films of Si and PDPP4T, (ii) Bi bulk samples, and (iii) Si, glass, and highly-oriented pyrollitic graphite (HOPG) bulk samples with a thin metallic transducer. Finally, we also show that it is possible to study in-plane heat transport on substrates with rather low thermal diffusivity, e.g., glass, even using a metallic transducer. We achieve this by an original approach based on patterning the transducer using focused ion beam, with the key purpose of limiting in-plane heat transport through the thin metallic transducer. Thermal diffusivity ; Materials science ; Optics ; Thermal ; Excitation ; Penetration depth ; Perpendicular ; Thermal conductivity ; Laser flash analysis ; Physics ; Geometry Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Thermography and Photoacoustic Techniques
Authors: Hyunjun Ma | Jin‐Soo Kim | Jong‐Ho Choe | Q‐Han Park
Source: Nanophotonics (article, 2023-05-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep‐learning‐assisted reconfigurable metasurface antenna for real‐time holographic beam steering We propose a metasurface antenna capable of real-time holographic beam steering. An array of reconfigurable dipoles can generate on-demand far-field patterns of radiation through the specific encoding of meta-atomic states i.e., the configuration of each dipole. Suitable states for the generation of the desired patterns can be identified using iteration, but this is very slow and needs to be done for each far-field pattern. Here, we present a deep-learning-based method for the control of a metasurface antenna with point dipole elements that vary in their state using dipole polarizability. Instead of iteration, we adopt a deep learning algorithm that combines an autoencoder with an electromagnetic scattering equation to determine the states required for a target far-field pattern in real-time. The scattering equation from Born approximation is used as the decoder in training the neural network, and analytic Green's function calculation is used to check the validity of Born approximation. Our learning-based algorithm requires a computing time of within 200 μs to determine the meta-atomic states, thus enabling the real-time operation of a holographic antenna. Autoencoder ; Antenna (radio) ; Holography ; Radiation pattern ; Computer science ; Reconfigurable antenna ; Deep learning ; Artificial neural network ; Dipole antenna ; Scattering ; Optics ; Field (mathematics) ; Dipole ; Physics ; Artificial intelligence ; Mathematics ; Telecommunications ; Quantum mechanics ; Antenna efficiency Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies