jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zahra Alaei Roozbahani | Mohsen Labbafi | Ali Aghakhani | Saeed Izadi | Jahan B. Ghasemi
Source: Journal of Food Measurement & Characterization (article, 2023-09-26)
Field: Chemistry — Analytical Chemistry

Identification of the authenticity and geographical origin of Iranian lime juice by targeted method and machine learning Lime ; Sugar ; Citric acid ; Sucrose ; Food science ; Artificial intelligence ; Mathematics ; Chemistry ; Machine learning ; Computer science ; Materials science ; Metallurgy Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Advanced Chemical Sensor Technologies
Authors: Aditi Mahajan | Inderdeep Singh | Navneet Arora
Source: Materials Letters (article, 2023-06-10)
Field: Materials Science — Polymers and Plastics

Data-driven analysis and prediction of tensile behavior of coir-based composites Ultimate tensile strength ; Materials science ; Coir ; Composite material ; Fiber ; Young's modulus ; Husk ; Modulus Natural Fiber Reinforced Composites ; Electrospun Nanofibers in Biomedical Applications ; Textile materials and evaluations
Authors: Fatih Sevim | Ahmet Atalay
Source: ACS Omega (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Approach for Modeling of Effect of Ultrasound on the Dissolution of Magnesia in Aqueous Carbon Dioxide This article is about dissolving magnesia in aqueous carbon dioxide by applying ultra sound. Particle size, reaction temperature, and solid/liquid ratio were chosen as the experimental parameters. As a result of the experimental study, the ultrasound energy conversion fraction (USECF) was obtained. Using experimental data, a model has been created for artificial neural networks and USECF. Created and modeled, the particle size, time, reaction temperature, solid/liquid ratio, and amplitude rate were determined as input variables. USECF was determined as the output variable of the model. In this study, six different ANN models were created by using two different learning algorithms and three different transfer functions. The results of these models were compared with the experimental results. It has been determined that the model established with the Levenberg Marquart learning algorithm and the TANSIG transfer function gives the best result of the ANN model compared to the other models. The ANN model established with the Gradient Descent learning algorithm and the LOGSIG transfer function were determined to be the second model that gave the best results. The regression <i>R</i> value for the model performance indicator training data was determined as 0.99 after validation, and the regression <i>R</i> value for the test data was determined as 0.99. Artificial neural network ; Dissolution ; Transfer function ; Carbon dioxide ; Gradient descent ; Particle size ; Biological system ; Materials science ; Mathematics ; Computer science ; Chemistry ; Artificial intelligence ; Engineering ; Physical chemistry Ultrasound and Cavitation Phenomena ; Energy and Environmental Sustainability ; Industrial Gas Emission Control
Authors: V. V. Kurbatkina | E. I. Pаtsera | Н. А. Кочетов | Е. А. Левашов
Source: Ceramics International (article, 2023-07-26)
Field: Materials Science — Ceramics and Composites

Combustion synthesis of ultra-high-temperature solid solutions (ZrxNb1-x)B2. Part 1: The mechanisms of combustion and structure formation Combustion ; Materials science ; Zirconium ; Niobium ; Boron ; Suboxide ; Thermodynamics ; Self-propagating high-temperature synthesis ; Phase (matter) ; Metallurgy ; Microstructure ; Physical chemistry ; Oxide ; Chemistry ; Organic chemistry Advanced ceramic materials synthesis ; Intermetallics and Advanced Alloy Properties ; Advanced materials and composites
Authors: Sabrya E. van Heijst | Maarten Bolhuis | Abel Brokkelkamp | Jeroen J. M. Sangers | Sonia Conesa‐Boj
Source: Advanced Functional Materials (article, 2023-11-05)
Field: Materials Science — Materials Chemistry

Heterostrain‐Driven Bandgap Increase in Twisted WS<sub>2</sub>: A Nanoscale Study Abstract Twisted 2D materials present an enticing platform for exploring diverse electronic properties owning to the tunability of their bandgap energy. However, the intricate relationship between local heterostrain fields, thickness, and bandgap energy remains insufficiently understood, particularly at the nanoscale. Here, it presents a comprehensive nanoscale study elucidating the remarkable sensitivity of the bandgap energy to both thickness and heterostrain fields within twisted WS 2 nanostructures. This approach integrates electron energy‐loss spectroscopy (EELS) enhanced by machine learning with 4D scanning transmission electron microscopy (STEM). Through this synergistic methodology, enhancements up to 20% in the bandgap energy is unveiled depending on the specimen thickness. This phenomenon is traced back to sizable deformation angles present within individual layers, which can be directly linked to distinct variations in local heterostrain fields. The findings represent a significant advancement in comprehending the electronic behavior of twisted 2D materials and introduce a novel methodological framework with far‐reaching implications for twistronics and the investigation of other materials within the nanoscience domain. Materials science ; Nanoscopic scale ; Band gap ; Nanostructure ; Scanning transmission electron microscopy ; Electron energy loss spectroscopy ; Nanotechnology ; Transmission electron microscopy ; Optoelectronics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Suwon Bae | Marcus M. Noack | Kevin G. Yager
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Surface enrichment dictates block copolymer orientation Orientation of block copolymer (BCP) morphology in thin films is critical to applications as nanostructured coatings. Despite being well-studied, the ability to control BCP orientation across all possible block constituents remains challenging. Here, we deploy coarse-grained molecular dynamics simulations to study diblock copolymer ordering in thin films, focusing on chain makeup, substrate surface energy, and surface tension disparity between the two constituent blocks. We explore the multi-dimensional parameter space of ordering using a machine-learning approach, where an autonomous loop using a Gaussian process (GP) control algorithm iteratively selects high-value simulations to compute. The GP kernel was engineered to capture known symmetries. The trained GP model serves as both a complete map of system response, and a robust means of extracting material knowledge. We demonstrate that the vertical orientation of BCP phases depends on several counter-balancing energetic contributions, including entropic and enthalpic material enrichment at interfaces, distortion of morphological objects through the film depth, and of course interfacial energies. BCP lamellae are found more resistant to these effects, and thus more robustly form vertical orientations across a broad range of conditions; while BCP cylinders are found to be highly sensitive to surface tension disparity. Copolymer ; Materials science ; Orientation (vector space) ; Surface tension ; Molecular dynamics ; Gaussian ; Surface energy ; Surface (topology) ; Distortion (music) ; Biological system ; Block (permutation group theory) ; Nanotechnology ; Chemical physics ; Computer science ; Composite material ; Geometry ; Polymer ; Physics ; Optoelectronics ; Mathematics ; Thermodynamics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advancements in Photolithography Techniques
Authors: Yueqian Wang | Daniel Julian | Mahmoud A. E. Ibrahim | Connor Chin | Saketh Bhattiprolu | Ethan Franco | Jesús Pérez‐Ríos
Source: Journal of Molecular Spectroscopy (article, 2023-10-30)
Field: Chemistry — Spectroscopy

The database of spectroscopic constants of diatomic molecules (DSCDM): A dynamic and user-friendly interface for molecular physics and spectroscopy Diatomic molecule ; Spectroscopy ; Upload ; Interface (matter) ; Molecular spectroscopy ; Excited state ; Constant (computer programming) ; Database ; Molecule ; Computer science ; Atomic physics ; Physics ; Materials science ; Quantum mechanics ; World Wide Web Spectroscopy and Laser Applications ; Spectroscopy and Quantum Chemical Studies ; Atmospheric Ozone and Climate
Authors: Biao Yin | Nicholas Josselyn | Thomas Considine | John Kelley | Berend Rinderspacher | Robert E. Jensen | James F. Snyder | Ziming Zhang | Elke A. Rundensteiner
Source:  (article, 2023-12-15)
Field: Materials Science — Metals and Alloys

DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Guided and Class-Balanced Losses Corrosion is a prevalent issue in numerous industrial fields, causing expenses nearing $3 trillion or 4% of the GDP annually with safety threats and environmental pollution. To timely qualify and validate new corrosion-inhibiting materials on a large scale, accurate and efficient corrosion assessment is crucial. Yet it is hindered by a lack of automatic tools for expert-level corrosion segmentation of material science experimental images. Developing such tools is challenging due to limited domain-valid data, image artifacts visually similar to corrosion, various corrosion morphology, strong class imbalance, and millimeter-precision corrosion boundaries. To help the community address these challenges, we curate the first expert-level segmentation annotations for a real-world image dataset [1] for scientific corrosion segmentation. In addition, we design a deep learning based model, called DeepSC-Edge that achieves guidance of ground-truth edge learning by adopting a novel loss that avoids over-fitting to edges. It also is enriched by integrating a class-balanced loss that improves segmentation with small area but crucial edges of interest for scientific corrosion assessment. Our dataset and methods pave the way to advanced deep-learning models for corrosion assessment and generation – promoting new research to connect computer vision and material science discovery. Once the appropriate approvals have been cleared, we expect to release the code and data at: https://arl.wpi.edu/ Enhanced Data Rates for GSM Evolution ; Class (philosophy) ; Computer science ; Segmentation ; Image segmentation ; Corrosion ; Artificial intelligence ; Materials science ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Non-Destructive Testing Techniques
Authors: José-Luis Morales-Reyes | Héctor‐Gabriel Acosta‐Mesa | Elia Nora Aquino-Bolaños | Socorro Herrera‐Meza | Aldo Márquez-Grajales
Source: Journal of Agricultural Engineering (article, 2023-08-01)
Field: Chemistry — Analytical Chemistry

Anthocyanins estimation in homogeneous bean landrace (&lt;em&gt;Phaseolus vulgaris&lt;/em&gt; L.) using probabilistic representation and convolutional neural networks Studying chemical components in food of natural origin allows us to understand their nutritional contents. However, nowadays, this analysis is performed using invasive methods that destroy the sample under study. These methods are also expensive and time-consuming. Computer vision is a non-invasive alternative to determine the nutritional contents through digital image processing to obtain the colour properties. This work employed a probability mass function (PMF) in colour spaces HSI (hue, saturation, intensity) and CIE L*a*b* (International Commission on Illumination) as inputs for a convolutional neural network (CNN) to estimate the anthocyanin contents in landraces of homogeneous colour. This proposal is called AnthEstNet (Anthocyanins Estimation Net). Before applying the CNN, a methodology was used to take digital images of the bean samples and extract their colourimetric properties represented by PMF. AnthEstNet was compared against regression methods and artificial neural networks (ANN) with different characterisation in the same colour spaces. The performance was measured using precision metrics. Results suggest that AnthEstNet presented a behaviour statistically equivalent to the invasive method results (pH differential method). For probabilistic representation in channels H and S, AnthEstNet obtained a precision value of 87.68% with a standard deviation of 10.95 in the test set of samples. As to root mean square error (RMSE) and R2, this configuration was 0.49 and 0.94, respectively. On the other hand, AnthEstNet, with probabilistic representations on channels a* and b* of the CIE L*a*b* colour model, reached a precision value of 87.49% with a standard deviation of 11.84, an RMSE value of 0.51, and an R2 value of 0.93. Mathematics ; Convolutional neural network ; Mean squared error ; Artificial intelligence ; Pattern recognition (psychology) ; Standard deviation ; Hue ; Phaseolus ; Statistics ; Digital image ; Probabilistic logic ; Computer science ; Image processing ; Image (mathematics) ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Phytochemicals and Antioxidant Activities
Authors: Aleksei Malyshev | Juan Miguel Arrazola | A. I. Lvovsky
Source: ArXiv.org (preprint, 2023-10-06)
Field: Materials Science — Materials Chemistry

Autoregressive Neural Quantum States with Quantum Number Symmetries Neural quantum states have established themselves as a powerful and versatile family of ansatzes for variational Monte Carlo simulations of quantum many-body systems. Of particular prominence are autoregressive neural quantum states (ANQS), which enjoy the expressibility of deep neural networks, and are equipped with a procedure for fast and unbiased sampling. Yet, the non-selective nature of autoregressive sampling makes incorporating quantum number symmetries challenging. In this work, we develop a general framework to make the autoregressive sampling compliant with an arbitrary number of quantum number symmetries. We showcase its advantages by running electronic structure calculations for a range of molecules with multiple symmetries of this kind. We reach the level of accuracy reported in previous works with more than an order of magnitude speedup and achieve chemical accuracy for all studied molecules, which is a milestone unreported so far. Combined with the existing effort to incorporate space symmetries, our approach expands the symmetry toolbox essential for any variational ansatz and brings the ANQS closer to being a competitive choice for studying challenging quantum many-body systems. Ansatz ; Quantum ; Computer science ; Statistical physics ; Sampling (signal processing) ; Artificial neural network ; Quantum computer ; Physics ; Quantum mechanics ; Artificial intelligence ; Telecommunications Machine Learning in Materials Science ; Quantum many-body systems ; Quantum, superfluid, helium dynamics
Authors: Azminul Jaman | Anouk S. Goossens | J. J. L. van Rijn | L. van der Zee | T. Banerjee
Source: Frontiers in Nanotechnology (article, 2023-02-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Morphology control of volatile resistive switching in La0.67Sr0.33MnO3 thin films on LaAlO3 (001) The development of in-memory computing hardware components based on different types of resistive materials is an active research area. These materials usually exhibit analog memory states originating from a wide range of physical mechanisms and offer rich prospects for their integration in artificial neural networks. The resistive states are classified as either non-volatile or volatile, and switching occurs when the material properties are triggered by an external stimulus such as temperature, current, voltage, or electric field. The non-volatile resistance state change is typically achieved by the switching layer’s local redox reaction that involves both electronic and ionic movement. In contrast, a volatile change in the resistance state arises due to the transition of the switching layer from an insulator to a metal. Here, we demonstrate volatile resistive switching in twinned LaAlO 3 onto which strained thin films of La 0.67 Sr 0.33 MnO 3 (LSMO) are deposited. An electric current induces phase transition that triggers resistive switching, close to the competing phase transition temperature in LSMO, enabled by the strong correlation between the electronic and magnetic ground states, intrinsic to such materials. This phase transition, characterized by an abrupt resistance change, is typical of a metallic to insulating behavior, due to Joule heating, and manifested as a sharp increase in the voltage with accompanying hysteresis. Our results show that such Joule heating-induced hysteretic resistive switching exhibits different profiles that depend on the substrate texture along the current path, providing an interesting direction toward new multifunctional in-memory computing devices. Materials science ; Joule heating ; Thin film ; Hysteresis ; Resistive touchscreen ; Phase transition ; Non-volatile memory ; Optoelectronics ; Metal–insulator transition ; Nanotechnology ; Electrical resistance and conductance ; Condensed matter physics ; Electric field ; Electrical engineering ; Composite material ; Metal Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Junhuang Jiang | Defang Ouyang | Robert O. Williams
Source: AAPS PharmSciTech (article, 2023-04-18)
Field: Materials Science — Materials Chemistry

Predicting Glass-Forming Ability of Pharmaceutical Compounds by Using Machine Learning Technologies Interpretability ; Solubility ; Support vector machine ; Amorphous solid ; Crystallization ; Machine learning ; Random forest ; Drug ; Computer science ; Glass transition ; In silico ; Intermolecular force ; Feature selection ; Pharmaceutical sciences ; Artificial intelligence ; Materials science ; Chemistry ; Biological system ; Molecule ; Organic chemistry ; Polymer ; Pharmacology ; Medicine Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Drug Solubulity and Delivery Systems
Authors: Ryohei Hosoya | Hiroshi Morita
Source: Macromolecules (article, 2023-08-23)
Field: Materials Science — Materials Chemistry

Machine Learning for Analyzing Phase-Separated Structures of ABA Triblock Copolymer Blends in Elongation An industrial thermoplastic material can form various metastable and complex phase-separated structures, depending on the molding process, resulting in various mechanical properties during deformation. Therefore, understanding the relationship between the morphology and the stress under deformation is important in material design. This study proposed a machine-learning-based morphology identification method and accordingly revealed the structural factor that affects the tensile stress of a neat ABA triblock copolymer as well as ABA-based blends with various compositional symmetricities, A block lengths, and B block lengths by combining this method with coarse-grained simulations as well as principal component analysis. By using this technique, researchers can visually and numerically analyze the morphological changes that occur with increasing strain. Copolymer ; Materials science ; Elongation ; Morphology (biology) ; Thermoplastic elastomer ; Ultimate tensile strength ; Thermoplastic ; Deformation (meteorology) ; Molding (decorative) ; Phase (matter) ; Composite material ; Stress (linguistics) ; Block (permutation group theory) ; Stress–strain curve ; Process (computing) ; Polymer ; Computer science ; Geometry ; Chemistry ; Mathematics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: Linruize Tang | Min Zhao | Shuaikai Shi | Jie Chen | Jinwei Li | Qiang Li | Rengang Li
Source:  (article, 2023-11-14)
Field: Chemistry — Analytical Chemistry

Tobacco Impurities Detection with Deep Image Segmentation Method on Hyperspectral Imaging The detection of impurities in tobacco raw materials is an important task, and the performance of detection algorithms directly affects the quality of tobacco products. The hyperspectral imaging system simultaneously captures the visible and near-infrared spectra containing rich spectral information that can accurately reflect material categories. In this paper, we propose a novel deep learning-based tobacco impurities detection method on hyperspectral data. It is a U-net like structure that progressively extracts multi-scale discriminant features and fuses them to find the impurities in the scene. Experiments conducted on the collected dataset show that the proposed segmentation model yields superior performance compared to other detection methods. Hyperspectral imaging ; Artificial intelligence ; Segmentation ; Pattern recognition (psychology) ; Computer science ; Impurity ; Image segmentation ; Deep learning ; Computer vision ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Leila Saranjam | Miroslava Nedyalkova | Elisabet Fuguet | Vasil Simeonov | Francesc Mas | Sergio Madurga
Source: Molecules (article, 2023-07-28)
Field: Chemistry — Spectroscopy

Collection of Partition Coefficients in Hexadecyltrimethylammonium Bromide, Sodium Cholate, and Lithium Perfluorooctanesulfonate Micellar Solutions: Experimental Determination and Computational Predictions This study focuses on determining the partition coefficients (logP) of a diverse set of 63 molecules in three distinct micellar systems: hexadecyltrimethylammonium bromide (HTAB), sodium cholate (SC), and lithium perfluorooctanesulfonate (LPFOS). The experimental log <i>p</i> values were obtained through micellar electrokinetic chromatography (MEKC) experiments, conducted under controlled pH conditions. Then, Quantum Mechanics (QM) and machine learning approaches are proposed for the prediction of the partition coefficients in these three micellar systems. In the applied QM approach, the experimentally obtained partition coefficients were correlated with the calculated values for the case of the 15 solvent mixtures. Using Density Function Theory (DFT) with the B3LYP functional, we calculated the solvation free energies of 63 molecules in these 16 solvents. The combined data from the experimental partition coefficients in the three micellar formulations showed that the 1-propanol/water combination demonstrated the best agreement with the experimental partition coefficients for the SC and HTAB micelles. Moreover, we employed the SVM approach and k-means clustering based on the generation of the chemical descriptor space. The analysis revealed distinct partitioning patterns associated with specific characteristic features within each identified class. These results indicate the utility of the combined techniques when we want an efficient and quicker model for predicting partition coefficients in diverse micelles. Partition coefficient ; Chemistry ; Micellar electrokinetic chromatography ; Micelle ; Partition (number theory) ; Solvation ; Density functional theory ; Molecule ; Micellar solutions ; Solvent ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Aqueous solution ; Organic chemistry ; Chromatography ; Mathematics ; Physics Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Chemical and Physical Properties in Aqueous Solutions
Authors: Emily Ryan | Athar Roshandelpoor | Zoe A. Pollard | Jillian L. Goldfarb | Pirooz Vakili
Source: MRS Communications (article, 2023-09-13)
Field: Materials Science — Materials Chemistry

Prospective on methods of design of experiments for limited data scenarios in materials design and engineering Computer science ; Software deployment ; Rendering (computer graphics) ; Data collection ; Throughput ; Data mining ; Systems engineering ; Machine learning ; Data science ; Artificial intelligence ; Software engineering ; Engineering Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Manufacturing Process and Optimization