jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Félix Musil | Max Veit | Alexander Goscinski | Guillaume Fraux | Michael J. Willatt | Markus Stricker | Till Junge | Michele Ceriotti
Source: Refubium (Universitätsbibliothek der Freien Universität Berlin) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Efficient implementation of atom-density representations Physically motivated and mathematically robust atom-centered representations of molecular structures are key to the success of modern atomistic machine learning. They lie at the foundation of a wide range of methods to predict the properties of both materials and molecules and to explore and visualize their chemical structures and compositions. Recently, it has become clear that many of the most effective representations share a fundamental formal connection. They can all be expressed as a discretization of n-body correlation functions of the local atom density, suggesting the opportunity of standardizing and, more importantly, optimizing their evaluation. We present an implementation, named librascal, whose modular design lends itself both to developing refinements to the density-based formalism and to rapid prototyping for new developments of rotationally equivariant atomistic representations. As an example, we discuss smooth overlap of atomic position (SOAP) features, perhaps the most widely used member of this family of representations, to show how the expansion of the local density can be optimized for any choice of radial basis sets. We discuss the representation in the context of a kernel ridge regression model, commonly used with SOAP features, and analyze how the computational effort scales for each of the individual steps of the calculation. By applying data reduction techniques in feature space, we show how to reduce the total computational cost by a factor of up to 4 without affecting the model’s symmetry properties and without significantly impacting its accuracy. Computer science ; Computation ; Theoretical computer science ; Modular design ; Formalism (music) ; Kernel (algebra) ; Context (archaeology) ; Algorithm ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Omar Khatib | Simiao Ren | Jordan M. Malof | Willie J. Padilla
Source: Advanced Optical Materials (article, 2022-05-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Learning the Physics of All‐Dielectric Metamaterials with Deep Lorentz Neural Networks Abstract Deep neural networks (DNNs) have shown marked achievements across numerous research and commercial settings. Part of their success is due to their ability to “learn” internal representations of the input ( x ) that are ideal to attain an accurate approximation () of some unknown function ( f ) that is, y = f ( x ). Despite their universal approximation capability, a drawback of DNNs is that they are black boxes, and it is unknown how or why they work. Thus, the physics discovered by the DNN remains hidden. Here, the condition of causality is enforced through a Lorentz layer incorporated within a deep neural network. This Lorentz NN (LNN) takes in the geometry of an all‐dielectric metasurface, and outputs the causal frequency‐dependent permittivity and permeability . Additionally, this LNN gives the spatial dispersion ( k ) inherent in the effective material parameters, as well as the Lorentz terms, which constitute both and . The ability of the LNN to learn metasurface physics is demonstrated through several examples, and the results are compared to theory and simulations. Lorentz transformation ; Artificial neural network ; Metamaterial ; Physics ; Causality (physics) ; Permittivity ; Deep neural networks ; Statistical physics ; Dielectric ; Theoretical physics ; Computer science ; Quantum mechanics ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Millimeter-Wave Propagation and Modeling
Authors: Samuel Genheden | Esben Jannik Bjerrum
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

PaRoutes: towards a framework for benchmarking retrosynthesis route predictions PaRoutes is a framework benchmarking multi-step retrosynthesis methods. It consists of synthetic routes extracted from the patent literature, stock compounds, as well as scripts to compute route quality and route diversity metrics. Benchmarking ; Retrosynthetic analysis ; Computer science ; Scripting language ; Quality (philosophy) ; Business ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zazilah May | M. K. Alam | Nazrul Anuar Nayan
Source: Sensors (review, 2022-09-02)
Field: Materials Science — Materials Chemistry

Recent Advances in Nondestructive Method and Assessment of Corrosion Undercoating in Carbon–Steel Pipelines Carbon-steel pipelines have mostly been utilized in the oil and gas (OG) industry owing to their strength and cost-effectiveness. However, the detection of corrosion under coating poses challenges for nondestructive (ND) pipeline monitoring techniques. One of the challenges is inaccessibility because of the pipeline structure, which leads to undetected corrosion, which possibly leads to catastrophic failure. The drawbacks of the existing ND methods for corrosion monitoring increase the need for novel frameworks in feature extraction, detection, and characterization of corrosion. This study begins with the explanations of the various types of corrosion in the carbon-steel pipeline in the OG industry and its prevention methods. A review of critical sensors integrated with various current ND corrosion monitoring systems is then presented. The importance of acoustic emission (AE) techniques over other ND methods is explained. AE data preprocessing methods are discussed. Several AE-based corrosion detection, prediction, and reliability assessment models for online pipeline condition monitoring are then highlighted. Finally, a discussion with future perspectives on corrosion monitoring followed by the significance and advantages of the emerging AE-based ND monitoring techniques is presented. The trends and identified issues are summarized with several recommendations for improvement in the OG industry. Corrosion ; Nondestructive testing ; Pipeline transport ; Carbon steel ; Forensic engineering ; Engineering ; Construction engineering ; Materials science ; Metallurgy ; Mechanical engineering ; Medicine Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Xiaohan Wu | Yuqian Gu | Ruijing Ge | Martha I. Serna | Yifu Huang | Jack C. Lee | Deji Akinwande
Source: npj 2D Materials and Applications (article, 2022-05-09)
Field: Materials Science — Materials Chemistry

Electron irradiation-induced defects for reliability improvement in monolayer MoS2-based conductive-point memory devices Abstract Monolayer molybdenum disulfide has been previously discovered to exhibit non-volatile resistive switching behavior in a vertical metal-insulator-metal structure, featuring ultra-thin sub-nanometer active layer thickness. However, the reliability of these nascent 2D-based memory devices was not previously investigated for practical applications. Here, we employ an electron irradiation treatment on monolayer MoS 2 film to modify the defect properties. Raman, photoluminescence, and X-ray photoelectron spectroscopy measurements have been performed to confirm the increasing amount of sulfur vacancies introduced by the e-beam irradiation process. The statistical electrical studies reveal the reliability can be improved by up to 1.5× for yield and 11× for average DC cycling endurance in the devices with a moderate radiation dose compared to unirradiated devices. Based on our previously proposed virtual conductive-point model with the metal ion substitution into sulfur vacancy, Monte Carlo simulations have been performed to illustrate the irradiation effect on device reliability, elucidating a clustering failure mechanism. This work provides an approach by electron irradiation to enhance the reliability of 2D memory devices and inspires further research in defect engineering to precisely control the switching properties for a wide range of applications from memory computing to radio-frequency switches. Materials science ; Molybdenum disulfide ; Raman spectroscopy ; Optoelectronics ; Monolayer ; Irradiation ; X-ray photoelectron spectroscopy ; Reliability (semiconductor) ; Electron beam processing ; Nanotechnology ; Composite material ; Optics ; Chemical engineering 2D Materials and Applications ; Advanced Memory and Neural Computing ; Perovskite Materials and Applications
Authors: J. Preethikaharshini | K. Naresh | G. Rajeshkumar | V. Arumugaprabu | Muhammad Khan | Kamran A. Khan
Source: Journal of Materials Science (article, 2022-08-30)
Field: Materials Science — Polymers and Plastics

Review of advanced techniques for manufacturing biocomposites: non-destructive evaluation and artificial intelligence-assisted modeling Natural fiber-reinforced polymer composites (NFRPCs) are being widely used in aerospace, marine, automotive and healthcare applications due to their sustainability, low cost and ecofriendly nature. The NFRPCs manufactured through conventional and computer-controlled intelligent manufacturing techniques may contain internal and external defects. Traditionally, the microstructure of NFRPCs at different stages of manufacturing was obtained using destructive techniques which have stringent sample size restrictions and may cause a decrease in residual properties of composites due to destructive scanning. However, these complications can be overcome by using non-destructive evaluation (NDE) and artificial intelligence (AI) techniques. This review highlights the impact of NDE and AI on the improvement of emerging manufacturing systems. We have discussed the classification of biocomposites, their manufacturing techniques, recyclability and strategies to improve mechanical properties. Further, the use of different types of contact and non-contact NDE techniques in understanding the microstructural variations during manufacturing, machining and the parameters that affect the mechanical performance of NFRPCs are discussed. The use of NDE images in developing the geometrical and computational models of NFRPCs is presented. We have highlighted the importance of AI technology in enhancing the quality of NDE images, improving the microstructural information before post-processing the data, and minimizing the analysis time and identifying the defects and damages in NFRPCs. In the end, we presented the application of NDE techniques and AI technology in efficient generation of digital material twins of NFRPCs, which will be useful to design next-generation biocomposites. Aerospace ; Automotive industry ; Machining ; Materials science ; Manufacturing engineering ; Nondestructive testing ; Solid mechanics ; Mechanical engineering ; Computer science ; Process engineering ; Engineering ; Composite material Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Material Selection and Properties
Authors: Ivelina Shishkova | Dicho Stratiev | Iliyan Kolev | Svetoslav Nenov | Dimitar Nedanovski | Krassimir Atanassov | Vitaly Ivanov | Simeon Ribagin
Source: Energies (article, 2022-10-20)
Field: Chemistry — Analytical Chemistry

Challenges in Petroleum Characterization—A Review 252 literature sources and about 5000 crude oil assays were reviewed in this work. The review has shown that the petroleum characterization can be classified in three categories: crude oil assay; SARA characterization; and molecular characterization. It was found that the range of petroleum property variation is so wide that the same crude oil property cannot be measured by the use of a single standard method. To the best of our knowledge for the first time the application of the additive rule to predict crude oil asphaltene content from that of the vacuum residue multiplied by the vacuum residue TBP yield was examined. It was also discovered that a strong linear relation between the contents of C5-, and C7-asphaltenes in crude oil and derived thereof vacuum residue fraction exists. The six parameter Weibull extreme function showed to best fit the TBP data of all crude oil types, allowing construction of a correct TBP curve and detection of measurement errors. A new SARA reconstitution approach is proposed to overcome the poor SARA analysis mass balance when crude oils with lower density are analyzed. The use of a chemometric approach with combination of spectroscopic data was found very helpful in extracting information about the composition of complex petroleum matrices consisting of a large number of components. Asphaltene ; Crude oil ; Petroleum ; Residue (chemistry) ; Characterization (materials science) ; Petroleum engineering ; Weibull distribution ; Fraction (chemistry) ; Environmental science ; Mass fraction ; Chemistry ; Mathematics ; Materials science ; Chromatography ; Statistics ; Geology ; Organic chemistry ; Nanotechnology Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Phase Equilibria and Thermodynamics
Authors: Jinhyuk Lim | Ajinkya C. Hire | Yundi Quan | J. S. Kim | Stephen Xie | Shubham Sinha | Ravhi S. Kumar | Dmitry Popov | Changyong Park | Russell J. Hemley | Yogesh K. Vohra | J. J. Hamlin | Richard G. Hennig | P. J. Hirschfeld | G. R. Stewart
Source: Nature Communications (article, 2022-12-22)
Field: Materials Science — Materials Chemistry

Creating superconductivity in WB2 through pressure-induced metastable planar defects High-pressure electrical resistivity measurements reveal that the mechanical deformation of ultra-hard WB<sub>2</sub> during compression induces superconductivity above 50 GPa with a maximum superconducting critical temperature, T<sub>c</sub>of 17 K at 91 GPa. Upon further compression up to 187 GPa, the T<sub>c</sub>gradually decreases. Theoretical calculations show that electron-phonon mediated superconductivity originates from the formation of metastable stacking faults and twin boundaries that exhibit a local structure resembling MgB<sub>2</sub> (hP3, space group 191, prototype AlB<sub>2</sub>). Synchrotron x-ray diffraction measurements up to 145 GPa show that the ambient pressure hP12 structure (space group 194, prototype WB<sub>2</sub>) continues to persist to this pressure, consistent with the formation of the planar defects above 50 GPa. The abrupt appearance of superconductivity under pressure does not coincide with a structural transition but instead with the formation and percolation of mechanically-induced stacking faults and twin boundaries. The results identify an alternate route for designing superconducting materials. Metastability ; Superconductivity ; Planar ; Materials science ; Condensed matter physics ; Physics ; Computer science ; Quantum mechanics Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Superconductivity in MgB2 and Alloys
Authors: Junwei Liu | Haowen Sun | Yuwei Peng | Ligen Chen | Wei Xu | Rong Shao
Source: Molecules (article, 2022-05-25)
Field: Materials Science — Biomaterials

Preparation and Characterization of Natural Silk Fibroin Hydrogel for Protein Drug Delivery In recent years, hydrogels have been widely used as drug carriers, especially in the area of protein delivery. The natural silk fibroin produced from cocoons of the Bombyx mori silkworm possesses excellent biocompatibility, significant bioactivity, and biodegradability. Therefore, silk fibroin-based hydrogels are arousing widespread interest in biomedical research. In this study, a process for extracting natural silk fibroin from raw silk textile yarns was established, and three aqueous solutions of silk fibroin with different molecular weight distributions were successfully prepared by controlling the degumming time. Silk fibroin was dispersed in the aqueous solution as "spherical" aggregate particles, and the smaller particles continuously accumulated into large particles. Finally, a silk fibroin hydrogel network was formed. A rheological analysis showed that as the concentration of the silk fibroin hydrogel increased its storage modulus increased significantly. The degradation behavior of silk fibroin hydrogel in different media verified its excellent stability, and the prepared silk fibroin hydrogel had good biocompatibility and an excellent drug-loading capacity. After the protein model drug BSA was loaded, the cumulative drug release within 12 h reached 80%. We hope that these investigations will promote the potential utilities of silk fibroin hydrogels in clinical medicine. Fibroin ; Biocompatibility ; SILK ; Self-healing hydrogels ; Bombyx mori ; Materials science ; Drug delivery ; Sericin ; Chemical engineering ; Aqueous solution ; Polymer science ; Chemistry ; Polymer chemistry ; Composite material ; Nanotechnology ; Organic chemistry ; Biochemistry Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Polymer Surface Interaction Studies
Authors: Ana N. Nunes | Alexandra Borges | Ana A. Matias | Maria Rosário Bronze | Joana Oliveira
Source: Molecules (review, 2022-01-07)
Field: Chemistry — Analytical Chemistry

Alternative Extraction and Downstream Purification Processes for Anthocyanins Anthocyanins are natural pigments displaying different attractive colors ranging from red, violet, to blue. These pigments present health benefits that increased their use in food, nutraceuticals, and the cosmetic industry. However, anthocyanins are mainly extracted through conventional methods that are time-consuming and involve the use of organic solvents. Moreover, the chemical diversity of the obtained complex extracts make the downstream purification step challenging. Therefore, the growing demand of these high-value pigments has stimulated the interest in designing new, safe, cost-effective, and tunable strategies for their extraction and purification. The current review focuses on the potential application of compressed fluid-based (such as subcritical and supercritical fluid extraction and pressurized liquid extraction) and deep eutectic solvents-based extraction methods for the recovery of anthocyanins. In addition, an updated review of the application of counter-current chromatography for anthocyanins purification is provided as a faster and cost-effective alternative to preparative-scale HPLC. Extraction (chemistry) ; Nutraceutical ; Supercritical fluid extraction ; Chromatography ; Chemistry ; Pigment ; Biochemical engineering ; Organic chemistry ; Food science Chromatography in Natural Products ; Tea Polyphenols and Effects ; Phytochemicals and Antioxidant Activities
Authors: Pauline Robert | Charlotte Brault | Renaud Rincent | Vincent Segura
Source: Methods in molecular biology (review, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Phenomic Selection: A New and Efficient Alternative to Genomic Selection Recently, it has been proposed to switch molecular markers to near-infrared (NIR) spectra for inferring relationships between individuals and further performing phenomic selection (PS), analogous to genomic selection (GS). The PS concept is similar to genomic-like omics-based (GLOB) selection, in which molecular markers are replaced by endophenotypes, such as metabolites or transcript levels, except that the phenomic information obtained for instance by near-infrared spectroscopy (NIRS ) has usually a much lower cost than other omics. Though NIRS has been routinely used in breeding for several decades, especially to deal with end-product quality traits, its use to predict other traits of interest and further make selections is new. Since the seminal paper on PS , several publications have advocated the use of spectral acquisition (including NIRS and hyperspectral imaging) in plant breeding towards PS , potentially providing a scope of what is possible. In the present chapter, we first come back to the concept of PS as originally proposed and provide a classification of selected papers related to the use of phenomics in breeding. We further provide a review of the selected literature concerning the type of technology used, the preprocessing of the spectra, and the statistical modeling to make predictions. We discuss the factors that likely affect the efficiency of PS and compare it to GS in terms of predictive ability. Finally, we propose several prospects for future work and application of PS in the context of plant breeding. Phenomics ; Selection (genetic algorithm) ; Genomic selection ; Context (archaeology) ; Computational biology ; Hyperspectral imaging ; Preprocessor ; Computer science ; Biotechnology ; Biology ; Biochemical engineering ; Data science ; Artificial intelligence ; Genomics ; Engineering ; Genome ; Genetics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Genetic and phenotypic traits in livestock
Authors: A. Alhadhrami | Gehad G. Mohamed | Ahmed H. Sadek | Sameh H. Ismail | A. A. Ebnalwaled | Abdulraheem S. A. Almalki
Source: Materials (article, 2022-11-18)
Field: Materials Science — Biomaterials

Behavior of Silica Nanoparticles Synthesized from Rice Husk Ash by the Sol–Gel Method as a Photocatalytic and Antibacterial Agent Silica nanoparticles (SiO<sub>2</sub> NPs) are one of the most well-studied inorganic nanoparticles for many applications. They offer the advantages of tunable size, biocompatibility, porous structure, and larger surface area. Thus, in this study, a high yield of SiO<sub>2</sub> NPs was produced via the chemical treatment of rice husk ash by the sol-gel method. Characteristics of the prepared SiO<sub>2</sub> NPs were validated using different characterization techniques. Accordingly, the phase, chemical composition, morphological, and spectroscopic properties of the prepared sample were studied. The average particle size of the SiO<sub>2</sub> NPs was found to be approximately 60-80 nm and the surface area was 78.52 m²/g. The prepared SiO<sub>2</sub> NPs were examined as photocatalysts for the degradation of methyl orange (MO) dye under UV irradiation. It was found that the intensity of the characteristic absorption band of MO decreased gradually with exposure time increasing, which means the successful photodegradation of MO by SiO<sub>2</sub> NPs. Moreover, the antibacterial activity of obtained SiO<sub>2</sub> NPs was investigated by counting the coliform bacteria in the surface water using the most probable number (MPN) index method. The results revealed that the MPN of coliform bacteria untreated and treated by SiO<sub>2</sub> NPs was estimated to be 170 CFU/100 mL and 10 CFU/100 mL, respectively, resulting in bacterial growth inhibition of 94.12%. Husk ; Photocatalysis ; Materials science ; Nanoparticle ; Sol-gel ; Chemical engineering ; Nanotechnology ; Catalysis ; Chemistry ; Organic chemistry Clay minerals and soil interactions ; Glass properties and applications ; Recycling and utilization of industrial and municipal waste in materials production