jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dongyao Li | Mingsen Deng | hujun shen
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Simulating Infrared Spectra of O-H Stretching in Liquid H2o Based on a Machine Learning Potential Infrared ; Infrared spectroscopy ; Spectral line ; Chemistry ; Materials science ; Analytical Chemistry (journal) ; Physics ; Chromatography ; Optics ; Organic chemistry ; Quantum mechanics Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies
Authors: Zhen Jia Lim | Petar Žuvela | Šime Ukić | Mirjana Novak Stankov | Tomislav Bolanča | Mario Lovrić | Ming Wah Wong | Bogusław Buszewski
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Spectroscopy

Gradient Retention Time Modelling in Ion Chromatography Through Ensemble Machine Learning-Powered Quantitative Structure-Retention Relationships Retention time ; Ensemble learning ; Chromatography ; Computer science ; Chemistry ; Artificial intelligence Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Swayamprakash Patel | Ami Kalasariya | Jagruti Desai | Mehul Patel | Ashish Patel | Umang Shah | Nilay Solanki | Amit Thakkar
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Spectroscopy

Sol_Me: Unleashing the Power of Machine Learning for Enhanced Lipid-Based Formulation Design and Predicting Drug Solubility in Oil and Surfactant Solubility ; PubChem ; Bioavailability ; Drug ; Pulmonary surfactant ; Computer science ; Chemistry ; Biological system ; Chromatography ; Biochemical engineering ; Machine learning ; Organic chemistry ; Bioinformatics ; Engineering ; Pharmacology Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Drug Solubulity and Delivery Systems
Authors: Guanmao Zhang | Rui Qiu | Shaokai Du | Jie Liu | Hongyu Ji | Kaiyun Bi | Bochuang Xing | Guangchao Diao
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Network-Assisted Design of Gsst-Based Achromatic Metalens with Continuously Variablefocal Heights Achromatic lens ; Artificial neural network ; Computer science ; Optics ; Physics ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Optical Coatings and Gratings ; Photonic Crystals and Applications
Authors: Mikidadi Abubakar | Peter Wasswa | Esther Masumba | Patrick Obia Ongom | Geoffrey Mkamilo | Edward Kanju | Wilfred Abincha | Richard Edema | Karoline Leonard Sichalwe | Phinehas Tukamuhabwa | Siraj Ismail Kayondo | Ismail Rabbi | Heneriko Kulembeka
Source: Research Square (preprint, 2023-12-04)
Field: Chemistry — Analytical Chemistry

Use of Low Cost Near-Infrared Spectroscopy, to Predict Pasting Properties of High Quality Cassava Flour Calibration ; Context (archaeology) ; Derivative (finance) ; Near-infrared spectroscopy ; Second derivative ; Stacking ; Viscosity ; Computer science ; Mathematics ; Analytical Chemistry (journal) ; Materials science ; Chemistry ; Statistics ; Physics ; Chromatography ; Optics ; Geology Spectroscopy and Chemometric Analyses ; Cassava research and cyanide ; Food composition and properties
Authors: Emanuele Telari | Antonio Tinti | Manoj Settem | Luca Maragliano | Riccardo Ferrando | Alberto Giacomello
Source: arXiv (Cornell University) (preprint, 2023-06-22)
Field: Materials Science — Materials Chemistry

Charting nanocluster structures via convolutional neural networks A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large dataset of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and to distill a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold, in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences, such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables which has the potential to be used for exploration and enhanced sampling purposes. Nanoclusters ; Computer science ; Cluster analysis ; Differentiable function ; Structural motif ; Maxima and minima ; Representation (politics) ; Convolutional neural network ; Chart ; Set (abstract data type) ; Theoretical computer science ; Artificial intelligence ; Materials science ; Nanotechnology ; Physics ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Computational Drug Discovery Methods
Authors: Khaled Nasri | Lotfi Toubal
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Artificial Neural Network Approach for Assessing Impact Performance of Natural-Fiber Composites Exposed to Uv Radiation Composite material ; Artificial neural network ; Radiation ; Materials science ; Natural (archaeology) ; Natural fiber ; Fiber ; Computer science ; Artificial intelligence ; Physics ; Optics ; Biology Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Fiber-reinforced polymer composites
Authors: Uduak Uboh
Source: arXiv (Cornell University) (preprint, 2023-09-20)
Field: Materials Science — Polymers and Plastics

Using Artificial Intelligence for the Automation of Knitting Patterns Knitting patterns are a crucial component in the creation and design of knitted materials. Traditionally, these patterns were taught informally, but thanks to advancements in technology, anyone interested in knitting can use the patterns as a guide to start knitting. Perhaps because knitting is mostly a hobby, with the exception of industrial manufacturing utilising specialised knitting machines, the use of Al in knitting is less widespread than its application in other fields. However, it is important to determine whether knitted pattern classification using an automated system is viable. In order to recognise and classify knitting patterns. Using data augmentation and a transfer learning technique, this study proposes a deep learning model. The Inception ResNet-V2 is the main feature extraction and classification algorithm used in the model. Metrics like accuracy, logarithmic loss, F1-score, precision, and recall score were used to evaluate the model. The model evaluation's findings demonstrate high model accuracy, precision, recall, and F1 score. In addition, the AUC score for majority of the classes was in the range (0.7-0.9). A comparative analysis was done using other pretrained models and a ResNet-50 model with transfer learning and the proposed model evaluation results surpassed all others. The major limitation for this project is time, as with more time, there might have been better accuracy over a larger number of epochs. Artificial intelligence ; Computer science ; Automation ; Machine learning ; F1 score ; Precision and recall ; Recall ; Feature (linguistics) ; Data mining ; Pattern recognition (psychology) ; Engineering Textile materials and evaluations ; Fashion and Cultural Textiles
Authors: Lilach Choona | Jasmine S. Linshiz | Shaul Pres | Boris Levant | Noam Tal-Perry | Gaetano Santoro | S. Baudot | A. Opdebeeck | Jason M. Reifsnider | Senthil Vadakupudhu Palayam | Lorusso Gian | Jérôme Mitard | Shay Yogev
Source:  (article, 2023-04-18)
Field: Materials Science — Surfaces, Coatings and Films

Direct yield prediction from SEM images In line Electrical measurement (E-Test) are the most effective predictors for EOL yield control. As technology progress with scaling, the number. of process layers increases, allowing in-line electrical measurements only after several months since lot started process in-line. As a result, each E-Test monitor controls longer and more challenging process loop. Most of the in-line pattern control that impact electrical performance measured separately for each pattern polygon and material properties. In addition, Edge Placement Error (EPE) methodology, allows combination of multiple dimensions like CD, Overlay and LER measurements to better predict yield impact. Technology shrinkage, resulting that transistor electrical performance, defined by more geomaterial parameters as well as material compositions and defectivity. In this paper we demonstrate a direct prediction from high resolution Scanning Electron Microscope (SEM) images to the first inline electrical measurement (M1) using Deep Learning (DL) techniques. The DL model provide early prediction of electrical performance, describing accurately Within Wafer (WIW) variation weeks earlier than the actual electrical measurements. Multiple layers prediction may indicate suspected process loop that modulate majority of variation and save time to solution. It can be achieved since the DL model utilizes complementary information exist on the full e-Beam image like materials and defectivity. The following results will indicate that accumulating information collected from several layers will improve prediction sensitivity and lead to even more accurate prediction capabilities. We assume that the effectiveness of the proposed prediction method will increase with process complexity, since the modulation of the existing yield predictors is losing sensitivity as design rule shrinks. In addition, since fabrication phase gets longer, the time to actual electrical measurements increase, making an early, nondestructive, and accurate prediction for electrical performance more and more valuable. Computer science ; Process (computing) ; Reduction (mathematics) ; Line (geometry) ; Sensitivity (control systems) ; Wafer ; Process control ; Enhanced Data Rates for GSM Evolution ; Electronic engineering ; Materials science ; Artificial intelligence ; Mathematics ; Engineering ; Optoelectronics Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advancements in Photolithography Techniques
Authors: Jing Guo | Ning Yang
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Performance Potential of Transistors Based on Tellurium Nanowire Arrays: A Quantum Transport Study Nanowire ; Tellurium ; Transistor ; Optoelectronics ; Nanotechnology ; Materials science ; Quantum ; Physics ; Quantum mechanics ; Voltage ; Metallurgy Graphene research and applications ; Nanowire Synthesis and Applications ; Advancements in Semiconductor Devices and Circuit Design
Authors: Yana Dukhnenko
Source: Automobile Roads and Road Construction (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

ANALYSIS OF EXISTING METHODS OF EVALUATING THE TECHNICAL CONDITION OF TRANSPORT STRUCTURES UNDER SPECIAL CONDITIONS The technical condition of transport facilities is a critical factor for ensuring the safety, efficiency and reliability of the transport infrastructure. Operating conditions can significantly affect the technical condition of buildings, especially in conditions characterized by high humidity, extreme temperatures or chemical pollution. This is especially true for structures that are exposed to natural factors such as sea water, aggressive environments or severe frosts. The article analyzes various methods of assessing the technical condition, which are used to detect defects and search for signs of damage to transport facilities. Traditional methods such as visual inspection and non-destructive evaluation are considered, as well as newer approaches including the use of remote sensing technologies, sensor monitoring and machine learning data analysis. The main purpose of the article is to identify the advantages and disadvantages of various assessment methods, as well as to determine the most effective approaches to assessing the technical condition of transport facilities in conditions that correspond to the specifics of the transport infrastructure. The obtained results can be useful for specialists in the field of transport engineering, who are engaged in the operation of transport infrastructure and road safety. Taking into account the technical condition of the facilities, it is possible to make informed decisions regarding repair work, planning regular maintenance and establishing preventive measures aimed at maintaining the safety and reliability of transport facilities. Reliability (semiconductor) ; Risk analysis (engineering) ; Transport engineering ; Work (physics) ; Transport infrastructure ; Field (mathematics) ; Computer science ; Construction engineering ; Reliability engineering ; Engineering ; Business Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: Kevin Ita | Joyce Prinze
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Machine Learning for Skin Permeability Prediction: Random Forest and XGBoost Regression Random forest ; Regression ; Artificial intelligence ; Machine learning ; Computer science ; Regression analysis ; Statistics ; Mathematics Textile materials and evaluations ; Skin Protection and Aging
Authors: Matheus Máximo‐Canadas | Itamar Borges
Source: Research Square (preprint, 2023-11-09)
Field: Chemistry — Physical and Theoretical Chemistry

Absorption Spectra of p-Nitroaniline Derivatives: Charge Transfer Effects and the Role of Substituents Abstract Context Push-pull compounds are model systems and have numerous applications. By changing their substituents, properties are modified and new molecules for different applications can be designed. The work investigates the gas phase electronic absorption spectra of fifteen derivatives of p –nitroaniline ( p NA) using theoretical methods. This molecule has applications in pharmaceuticals, azo dyes, corrosion inhibitors, and optoelectronics. Both electron-donor and electron-withdrawing groups were investigated. Overall, electron-donors redshift the absorption spectrum and reduce the HOMO-LUMO energy gap. Conversely, electron-withdrawing groups blueshift the spectrum and increase the energy gap. A comprehensive charge transfer (CT) analysis of the main transition of all systems was also carried out. We found that donors slightly enhance the CT character of the unsubstituted p NA, whereas acceptors decrease it, leading to increased local excitations within the aromatic ring. The overall CT variation is not large, except for p NA–SO 2 H, which considerably decreases the total CT value. It was found that the strong electron donors p NA–OH, p NA–OCH 3 , and p NA–NH 2 , which have small HOMO-LUMO energy gaps, have potential for optoelectronic applications. Methods We investigated theoretically employing the second-order algebraic diagrammatic construction (ADC(2)) ab initio wave function the gas phase electronic absorption spectra of fifteen derivatives of p –nitroaniline ( p NA). The substituents considered in this investigation included both electron-donor (C 6 H 5 , CCH, CH 3 , NH 2 , OCH 3 , and OH,) and electron-withdrawing (Br, CCl 3 , CF 3 , Cl, CN, COOH, F, NO 2 , and SO 2 H) substituents. Employing machine-learning-derived Hammett's constants σ m , σ m 0 , σ R , and σ I , correlations between substituents and electronic properties were obtained. HOMO/LUMO ; Polar effect ; Chemistry ; Blueshift ; Band gap ; Absorption spectroscopy ; Context (archaeology) ; Molecule ; Absorption (acoustics) ; Fluorene ; Ab initio ; Photochemistry ; Computational chemistry ; Materials science ; Organic chemistry ; Polymer ; Physics Photochemistry and Electron Transfer Studies ; Nonlinear Optical Materials Research ; Free Radicals and Antioxidants
Authors: Lorenzo Fongaro | Cecilia Marie Futsæther | Oliver Tomić | Isak B. Lande | Knut Kvaal | Maria Wallenius | K. Ulrich Mayer
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Development of a New Approach for Rapid Identification and Classification of Uranium Ore Concentrate Powders Using Textural and Spectroscopy Signatures Uranium ore ; Uranium ; Spectroscopy ; Identification (biology) ; Mineralogy ; Materials science ; Geochemistry ; Metallurgy ; Geology ; Physics ; Biology Radioactive element chemistry and processing
Authors: Jianbin Fu | Renjie Liu | Yuewen Sun | Tianceng Zeng | Haoyu Liu | Shuo Xu | Peng Cong
Source: The Proceedings of the International Conference on Nuclear Engineering (ICONE) (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

RESEARCH ON INTELLIGENT DETECTION OF DEFECTS IN HTR-PM GRAPHITE AND CARBON COMPONENTS BASED ON DEEP LEARNING Graphite and carbon components are important materials for the core and reflector of a high temperature gas-cooled reactor-pebble bed module (HTR-PM). Expansion to the critical point will cause the components to rupture and fall off, which will pose a huge threat to the structural safety of the HTR-PM and its safe and stable operation. Therefore, before the reactor is built, the defect detection of graphite and carbon components is particularly important, which is not only the basic guarantee for the long-term safe operation of the nuclear reactor but also an important support for predicting the fatigue state of irradiated materials based on the pre-service data of the reactor to evaluate the life of the reactor. Considering the large volume of reactor components, long production cycle, and small internal defect volume, the defect detection accuracy and efficiency are required to be high, and traditional non-destructive testing methods such as ultrasound, eddy current, and X-ray are no longer applicable. Multi-slice spiral CT technology has the advantages of high detection accuracy and fast detection speed and is suitable for 3D digital imaging of graphite and carbon components. However, the amount of reconstructed data from spiral CT images is huge, and the manual image review based on visual inspection has shortcomings such as false detection and missed detection. Therefore, it is particularly important to intelligently detect defects. In this paper, the detection images of graphite and carbon component samples by multi-slice spiral CT system are used as the dataset, artificial defect annotation is carried out, and the deep learning algorithm based on YOLOX is used to realize the intelligent detection of defects of large-size graphite and carbon components in HTR-PM. The results show that the YOLOX algorithm achieves good performance in defect detection of graphite and carbon components, and can achieve 96.4% accuracy and 64.85 fps value, with high detection accuracy and detection efficiency. Graphite ; Nuclear engineering ; Materials science ; Spiral (railway) ; Nuclear reactor ; Carbon fibers ; Computer science ; Volume (thermodynamics) ; Process engineering ; Environmental science ; Mechanical engineering ; Engineering ; Composite material ; Physics Graphite, nuclear technology, radiation studies ; Advanced X-ray and CT Imaging
Authors: Dominique Beaini | Shenyang Huang | Joao Alex Cunha | Zhiyi Li | Gabriela Moisescu-Pareja | Oleksandr Dymov | Samuel Maddrell-Mander | Callum McLean | Ali Parviz | Luis T. Díaz Müller | Jama Hussein Mohamud | Frederik Wenkel | Michael Craig | Michał Koziarski | Jiarui Lu | Zhaocheng Zhu | Cristian Gabellini | Guillaume Rabusseau | Reihaneh Rabbany | Jian Tang | Christopher G. Morris | Mirco Ravanelli | Guy Wolf | Prudencio Tossou | Hadrien Mary | Błażej Banaszewski | Chad Martin | Dominic Masters
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets (Ultra Large Dataset) Recently, pre-trained foundation models have shown significant advancements in multiple fields. However, the lack of datasets with labeled features and codebases has hindered the development of a supervised foundation model for molecular tasks. Here, we have carefully curated seven datasets specifically tailored for node- and graph-level prediction tasks to facilitate supervised learning on molecules. Moreover, to support the development of multi-task learning on our proposed datasets, we created the Graphium graph machine learning library. Our dataset collection encompasses two distinct categories. Firstly, the TOYMIX category modifies three small existing datasets with additional data for multi-task learning. Secondly, the LARGEMIX category includes four large-scale datasets with 344M graph-level data points and 409M node-level data points from ∼5M unique molecules. Finally, the ultra-large dataset contains 2,210M graph-level data points and 2,031M node-level data points coming from 86M molecules. Hence our datasets represent an order of magnitude increase in data volume compared to other 2D-GNN datasets. In addition, recognizing that molecule-related tasks often span multiple levels, we have designed our library to explicitly support multi-tasking, offering a diverse range of multi-level representations, i.e., representations at the graph, node, edge, and node-pair level. We equipped the library with an extensive collection of models and features to cover different levels of molecule analysis. By combining our curated datasets with this versatile library, we aim to accelerate the development of molecule foundation models. Datasets and code are available at https://github.com/datamol-io/graphium. Task (project management) ; Scale (ratio) ; Computer science ; Machine learning ; Artificial intelligence ; Data science ; Geography ; Engineering ; Systems engineering ; Cartography Machine Learning in Materials Science