jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Feiyang Xu | Zhiguo Li | Xiang-Rong Chen | Hua-Yun Geng | Lei Liu | Jianbo Hu
Source: Journal of Applied Physics (article, 2024-03-25)
Field: Materials Science — Materials Chemistry

Probing the critical point of MgSiO3 using deep potential simulation The giant impact between proto-Earth and a Mars-sized planet called Theia resulted in the formation of the Earth–Moon system, and the silicate mantles of the initial bodies may have partly been vaporized. Here, we develop a machine learning potential for MgSiO3 based on the data from first-principles calculations to estimate its critical point. The variations in pressure along different isotherms yield the position of the critical point of MgSiO3 at 0.54 g cm−3 and 6750 ± 250 K, which agrees with the previous theoretical estimation. We also simulate the MgSiO3 melt under a spectrum of critical conditions to understand the changes in coordination environment with density and temperature. The fourfold Si–O coordination hardly changes with increasing density at 3000 K. However, with increasing temperature, the dominance of four-coordinated Si–O diminishes rapidly as density decreases. Regarding Mg–O coordination, the overall trend, which varies with temperature and density, remains largely consistent with Si–O but with a greater diversity in the types of coordination due to more bond breaking events. Our work opens a new avenue by employing machine learning methods to estimate the critical point of silicates. Point (geometry) ; Critical point (mathematics) ; Statistical physics ; Geology ; Physics ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; High-pressure geophysics and materials
Authors: Madan R. Gopal | Aditya M. Kunjapur
Source: Current Opinion in Biotechnology (article, 2024-02-27)
Field: Materials Science — Biomaterials

Harnessing biocatalysis to achieve selective functional group interconversion of monomers Deconstruction (building) ; Biocatalysis ; Monomer ; Biochemical engineering ; Macromolecule ; Polymer ; Chemistry ; Biomass (ecology) ; Organic chemistry ; Nanotechnology ; Materials science ; Waste management ; Catalysis ; Biology ; Reaction mechanism ; Engineering ; Ecology biodegradable polymer synthesis and properties ; Catalysis for Biomass Conversion ; Enzyme Catalysis and Immobilization
Authors: Woonbae Sohn | Taekyung Kim | Cheon Woo Moon | Dongbin Shin | Yeji Park | Haneul Jin | Hionsuck Baik
Source: Nanomaterials (article, 2024-10-10)
Field: Materials Science — Materials Chemistry

Unsupervised Learning for the Automatic Counting of Grains in Nanocrystals and Image Segmentation at the Atomic Resolution Identifying the grain distribution and grain boundaries of nanoparticles is important for predicting their properties. Experimental methods for identifying the crystallographic distribution, such as precession electron diffraction, are limited by their probe size. In this study, we developed an unsupervised learning method by applying a Gabor filter to HAADF-STEM images at the atomic level for image segmentation and automatic counting of grains in polycrystalline nanoparticles. The methodology comprises a Gabor filter for feature extraction, non-negative matrix factorization for dimension reduction, and K-means clustering. We set the threshold distance and angle between the clusters required for the number of clusters to converge so as to automatically determine the optimal number of grains. This approach can shed new light on the nature of polycrystalline nanoparticles and their structure-property relationships. Cluster analysis ; Materials science ; Crystallite ; Filter (signal processing) ; Segmentation ; Artificial intelligence ; Non-negative matrix factorization ; Gabor filter ; Unsupervised learning ; Pattern recognition (psychology) ; Image segmentation ; Computer science ; Feature extraction ; Computer vision ; Matrix decomposition ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Runfeng Chen | Qingqing Yan | Tuhanguli Tuoheti | Lin Xu | Qiang Gao | Yan Zhang | Hailong Ren | Lipeng Zheng | Feng Wang | Ya Liu
Source: Plant Methods (article, 2024-05-26)
Field: Chemistry — Analytical Chemistry

A prediction model of rubber content in the dried root of Taraxacum kok-saghyz Rodin based on near-infrared spectroscopy This research indicates that dried TKS root powder samples are more appropriate for constructing a rubber content prediction model than segmented samples, and the predictive capability of root powder samples is superior to that of root segmented samples. Especially in the elevated rubber content range, the model formulated using the LightGBM algorithm has superior predictive performance, which could offer a theoretical basis for the rapid detection technology of TKS content in the future. Infrared spectroscopy ; Infrared ; Spectroscopy ; Chemistry ; Natural rubber ; Botany ; Physics ; Biology ; Organic chemistry ; Optics Spectroscopy and Chemometric Analyses ; Plant biochemistry and biosynthesis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Erni Salasia Fitri | Risfidian Mohadi | Neza Rahayu Palapa | Susila Arita Rachman | Aldes Lesbani
Source: Environment and Natural Resources Journal (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Composite Layered Double Hydroxide Zn-Al/Magnetic Biochar Modified for Highly Effective Malachite Green Adsorption One of the main needs of humans is water, one source of water pollutantion is from dyes. Adsorption is the most popular method in removing pollutants as it is proven to be efficient. One of the dyes harmful to living things is malachite green. In this study, malachite green dye was removed using a layered double hydroxide (LDH) Zn-Al/magnetic biochar composite. The XRD, FTIR, BET, and VSM analyses show that the LDH Zn-Al, magnetic biochar, and LDH Zn-Al/magnetic biochar composite were successfully produced. The surface area of the Zn-Al/magnetic biochar composite made up of LDH increased from 9.621 m2/g to 99.473 m2/g. The point of zero charge of LDH Zn-Al and magnetic biochar were at pH 6, whereas the composite of these two materials was at pH 7. For LDH Zn-Al, magnetic biochar, and composite LDH Zn-Al/magnetic biochar, pH 8 is the ideal value for adsorption of malachite green. PSO (pseudo-second order) kinetics is the best-fit model. LDH Zn-Al, magnetic biochar, and LDH Zn-Al/magnetic biochar composite had adsorption capacities of 14.472, 15.552, and 25.907 mg/g, respectively, at a temperature of 60°C. Regeneration showed the LDH Zn-Al/magnetic biochar composite had superior and more effective ability compared to LDH Zn-Al and magnetic biochar. Biochar ; Malachite green ; Adsorption ; Composite number ; Chemistry ; Nuclear chemistry ; Fourier transform infrared spectroscopy ; Hydroxide ; Materials science ; Chemical engineering ; Inorganic chemistry ; Composite material ; Pyrolysis ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Phosphorus and nutrient management
Authors: Yannick Schubert | Sandra Luber | Nicola Marzari | Edward Linscott
Source: npj Computational Materials (article, 2024-12-20)
Field: Materials Science — Materials Chemistry

Predicting electronic screening for fast Koopmans spectral functional calculations Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enables the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the effects of electronic screening through scalar, orbital-dependent parameters. These parameters have to be computed for every calculation, making Koopmans spectral functionals more expensive than their DFT counterparts. In this work, we present a machine-learning model that-with minimal training-can predict these screening parameters directly from orbital densities calculated at the DFT level. We show in two prototypical use cases that using the screening parameters predicted by this model, instead of those calculated from linear response, leads to orbital energies that differ by less than 20 meV on average. Since this approach dramatically reduces run times with minimal loss of accuracy, it will enable the application of Koopmans spectral functionals to classes of problems that previously would have been prohibitively expensive, such as the prediction of temperature-dependent spectral properties. More broadly, this work demonstrates that measuring violations of piecewise linearity (i.e., curvature in total energies with respect to occupancies) can be done efficiently by combining frozen-orbital approximations and machine learning. Scalar (mathematics) ; Hybrid functional ; Density functional theory ; Work (physics) ; Curvature ; Piecewise ; Statistical physics ; Computer science ; Physics ; Computational physics ; Mathematics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Yu Liao | Duo Pan
Source: Advanced Composites and Hybrid Materials (article, 2024-12-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Revisiting advanced composites and hybrid materials during 2018–2023 Focus (optics) ; Promotion (chess) ; Process (computing) ; Engineering ethics ; Point (geometry) ; Architectural engineering ; Engineering ; Construction engineering ; Computer science ; Political science ; Physics ; Politics Supercapacitor Materials and Fabrication ; Bone Tissue Engineering Materials ; TiO2 Photocatalysis and Solar Cells
Authors: Khadidja Djilali | Rachida Maachi | Hichem Tahraoui | Zohra Ait Mesbah | Abdeltif Amrane
Source: Journal of Molecular Structure (article, 2024-06-04)
Field: Chemistry — Analytical Chemistry

Advancing thermal stability analysis of haloperidol: Integrative approaches and optimization strategies for enhanced pharmaceutical formulations Differential scanning calorimetry ; Chemistry ; Fourier transform infrared spectroscopy ; Degradation (telecommunications) ; Haloperidol ; Thermal stability ; Thermal analysis ; Chemical engineering ; Process engineering ; Thermodynamics ; Thermal ; Organic chemistry ; Computer science Analytical Methods in Pharmaceuticals ; Thermal and Kinetic Analysis ; Analytical Chemistry and Chromatography
Authors: Adriana Samide | Gabriela Eugenia Iacobescu | Bogdan Tutunaru | Cristian Tigae | Cezar Spînu | Bogdan Oprea
Source: Polymers (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

New Inhibitor Based on Hydrolyzed Keratin Peptides for Stainless Steel Corrosion in Physiological Serum: An Electrochemical and Thermodynamic Study Reducing the impact of some biological fluids on bioimplants involves the control of surface characteristics by modeling the interface architecture and assembling ecofriendly thin films to retard corrosion. Therefore, a mixture of hydrolyzed keratin peptides (HKER) was investigated as a corrosion inhibitor for 304L stainless steel (SS) in physiological serum (PS), using electrochemical measurements associated with optical microscopy and atomic force microscopy (AFM). The tests, performed for various concentrations of the inhibitor at different temperatures, showed that the inhibition efficiency (<i>IE</i>) decreased with a rise in temperature and proportionally increased with the HKER concentration, reaching its maximum level, around 88%, at 25 °C, with a concentration of 40 g L<sup>-1</sup> HKER in physiological serum. The experimental data best fitted the El-Awady adsorption model. The activation parameters (E<sub>a</sub>, ∆Ha and ∆Sa) and the adsorption ones (∆Gads0, ∆Hads, ∆Sads) have highlighted a mixed action mechanism of HKER, revealing that physisorption prevails over chemisorption. AFM parameters, such as the average roughness (<i>R<sub>a</sub></i>), root-mean-square roughness (<i>R<sub>q</sub></i>) and maximum peak-to-valley height (<i>R<sub>p-v</sub></i>), confirmed HKER adsorption, indicating that a smoother surface of the 304L stainless steel was obtained when immersed in a PS-containing inhibitor, compared to the surface designed in blank solution, due to the development of a protective layer on the alloy surface. Physisorption ; Adsorption ; Materials science ; Corrosion ; Surface roughness ; Chemisorption ; Electrochemistry ; Surface finish ; Alloy ; Corrosion inhibitor ; Chemical engineering ; Keratin ; Root mean square ; Hydrolysis ; Nuclear chemistry ; Analytical Chemistry (journal) ; Metallurgy ; Composite material ; Chemistry ; Electrode ; Chromatography ; Physical chemistry ; Organic chemistry Corrosion Behavior and Inhibition ; Dyeing and Modifying Textile Fibers ; Anodic Oxide Films and Nanostructures
Authors: Simon Gramatte | Vladyslav Turlo | O. Politano
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-04-03)
Field: Materials Science — Materials Chemistry

Do we really need machine learning interatomic potentials for modeling amorphous metal oxides? Case study on amorphous alumina by recycling an existing ab initio database Abstract In this study, we critically evaluate the performance of various interatomic potentials/force fields against a benchmark ab initio database for bulk amorphous alumina. The interatomic potentials tested in this work include all major fixed charge and variable charge models developed to date for alumina. Additionally, we introduce a novel machine learning interatomic potential constructed using the NequIP framework based on graph neural networks. Our findings reveal that the fixed-charge potential developed by Matsui and coworkers offers the most optimal balance between computational efficiency and agreement with ab initio data for stoichiometric alumina. Such balance cannot be provided by machine learning potentials when comparing performance with Matsui potential on the same computing infrastructure using a single Graphical Processing Unit. For non-stoichiometric alumina, the variable charge potentials, in particular ReaxFF, exhibit an impressive concordance with density functional theory calculations. However, our NequIP potentials trained on a small fraction of the ab initio database easily surpass ReaxFF in terms of both accuracy and computational performance. This is achieved without large overhead in terms of potential fitting and fine-tuning, often associated with the classical potential development process as well as training of standard deep neural network potentials, thus advocating for the use of data-efficient machine learning potentials like NequIP for complex cases of non-stoichiometric amorphous oxides. Amorphous solid ; Ab initio ; Interatomic potential ; Materials science ; Amorphous metal ; Metal ; Database ; Chemical physics ; Computational chemistry ; Computer science ; Metallurgy ; Chemistry ; Molecular dynamics ; Crystallography ; Organic chemistry Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Gavin P. Andrews | Alice Culkin | David S. Jones | Shu Li
Source: Molecular Pharmaceutics (article, 2024-08-13)
Field: Chemistry — Analytical Chemistry

In Situ Eutectic Formation in a Polymeric Matrix via Hot-Melt Reactive Extrusion and the Use of Partial Least Squares Regression Modeling for Reaction Yield Determination There has been a significant volume of work investigating the design and synthesis of new crystalline multicomponent systems via examining complementary functional groups that can reliably interact through the formation of noncovalent bonds, such as hydrogen bonds (H-bonds). Crystalline multicomponent molecular adducts formed using this approach, such as cocrystals, salts, and eutectics, have emerged as drug product intermediates that can lead to effective drug property modifications. Recent advancement in the production for these multicomponent molecular adducts has moved from batch techniques that rely upon intensive solvent use to those that are solvent-free, continuous, and industry-ready, such as reactive extrusion. In this study, a novel eutectic system was found when processing albendazole and maleic acid at a 1:2 molar ratio and successfully prepared using mechanochemical methods including liquid-assisted grinding and hot-melt reactive extrusion. The produced eutectic was characterized to exhibit a 100 °C reduction in melting temperature and enhanced dissolution performance (>12-fold increase at 2 h point), when compared to the native drug compound. To remove handling of the eutectic as a formulation intermediate, an end-to-end continuous-manufacturing-ready process enables feeding of the raw parent reagents in their respective natural forms along with a chosen polymeric excipient, Eudragit EPO. The formation of the eutectic was confirmed to have taken place in situ in the presence of the polymer, with the reaction yield determined using a multivariate calibration model constructed by combining spectroscopic analysis with partial least-squares regression modeling. The ternary extrudates exhibited a dissolution profile similar to that of the 1:2 prepared eutectic, suggesting a physical distribution (or suspension) of the in situ synthesized eutectic contents within the polymeric matrix. Eutectic system ; In situ ; Partial least squares regression ; Yield (engineering) ; Extrusion ; Matrix (chemical analysis) ; Materials science ; Reactive extrusion ; Chemistry ; Chemical engineering ; Biological system ; Chromatography ; Metallurgy ; Mathematics ; Statistics ; Organic chemistry ; Engineering Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography
Authors: Edgar Galvan | Ana K. Larios Galvez | America M. Ramirez Arteaga | R. López-Sesenes | Jose Gonzalo Gonzalez Rodriguez
Source: Scientific Reports (article, 2024-10-08)
Field: Materials Science — Materials Chemistry

Electrochemical, gravimetric and surface studies of Phalaris canariensis oil extract as corrosion inhibitor for 316 L type stainless steel in H2O-LiCl mixtures The corrosion inhibition action of Phalaris canariensis extract on 316 L stainless steel in the H<sub>2</sub>O-35 (wt%) LiCl mixture at different temperatures has been evaluated with the aid of weight loss, potentiodynamic polarization curves, linear polarization resistance and electrochemical impedance spectroscopy tests. These studies were complemented by Fourier Transformed Infrared spectroscopy, FTIR, gas/mass chromatography analytical techniques and detailed scanning electronic microscopy studies. Results have indicated that Phalaris canariensis extract is an efficient inhibitor, with an efficiency that increases with its concentration, but it decreases as the temperature increases. Phalaris canariensis extract is physically adsorbed onto stainless steel according to a Temkin type of adsorption isotherm. Phalaris canariensis extract affected both anodic and cathodic electrochemical reactions with a stronger effect on the anodic ones, acting, thus, as a mixed type of inhibitor. Main compounds contained in the Phalaris canariensis extract were palmitic and oleic acids, responsible for its inhibitory properties. Gravimetric analysis ; Corrosion ; Electrochemistry ; Materials science ; Metallurgy ; Corrosion inhibitor ; Nuclear chemistry ; Chemistry ; Electrode ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Abdulqader Faris Abdulqader | Laith Baqir Salman | Yousif Fuad Nasr | Nataliia Bodnar | Rana Hikmat
Source: Radioelectronics Nanosystems Information Technologies (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Graphene Based Composites Material Characteristics and Industrial Uses lot of interest due to its outstanding mechanical, thermal, and electrical characteristics. Its incorporation into composite materials has received much research to capitalize on these properties in various applications. Objective: This research aims to conduct a thorough evaluation and analysis of the mechanical characteristics of graphene-based composites, as well as to investigate their practical applications in various sectors. Methods: A complete literature assessment was undertaken, emphasizing advances in graphene-based composite production, characterization, and applications. Experimental data from several research were gathered to assess the improvement of mechanical characteristics such as tensile strength, elasticity, and impact resistance owing to graphene inclusion. Results: According to the investigation, graphene-based composites have much better mechanical characteristics than those without graphene. The degree of augmentation varies according to the matrix material, graphene content, and production process used. Notably, aerospace, automotive, and electronics sectors have begun to use these composites to take advantage of their better strength-to-weight ratios and endurance. Conclusion: Graphene-based composites have to advance material science and engineering by significantly improving mechanical characteristics. Their many industrial applications demonstrate graphene's adaptability and revolutionizing material design and functionality in various fields. Further research and development are required to optimize graphene integration in commercial goods and fully realize its disruption. Composite material ; Materials science ; Graphene ; Nanotechnology Graphene research and applications ; Graphene and Nanomaterials Applications ; Internet of Things and AI
Authors: Andrea Fantasia | Fabrizio Rovaris | Omar Abou El Kheir | Anna Marzegalli | Daniele Lanzoni | Laura Pessina | Penghao Xiao | Chuan Zhou | Lei Li | Graeme Henkelman | Emilio Scalise | Francesco Montalenti
Source: The Journal of Chemical Physics (article, 2024-07-02)
Field: Materials Science — Materials Chemistry

Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium We introduce a data-driven potential aimed at the investigation of pressure-dependent phase transitions in bulk germanium, including the estimate of kinetic barriers. This is achieved by suitably building a database including several configurations along minimum energy paths, as computed using the solid-state nudged elastic band method. After training the model based on density functional theory (DFT)-computed energies, forces, and stresses, we provide validation and rigorously test the potential on unexplored paths. The resulting agreement with the DFT calculations is remarkable in a wide range of pressures. The potential is exploited in large-scale isothermal-isobaric simulations, displaying local nucleation in the R8 to β-Sn pressure-induced phase transformation, taken here as an illustrative example. Germanium ; Isobaric process ; Density functional theory ; Nucleation ; Isothermal process ; Materials science ; Kinetic energy ; Phase (matter) ; Phase transition ; Range (aeronautics) ; Interatomic potential ; Transformation (genetics) ; Thermodynamics ; Statistical physics ; Chemistry ; Computational chemistry ; Physics ; Molecular dynamics ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Surface and Thin Film Phenomena