jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: So Yeon Kim | Ju Li
Source: Journal of Materiomics (article, 2021-04-01)
Field: Materials Science — Materials Chemistry

Machine learning of metal-ceramic wettability The adhesion and wetting between metal and ceramic is a basic problem in materials science and engineering. For example, past materials selection for metal-ceramic composites has relied on random trials and heuristics due to a limited understanding of their adhesion; the large chemical/structural variability that such interfaces can have hinders the identification of the governing factors. Here based on literature data, we have developed a database with ∼1,000 experimentally measured wetting angles at different temperatures and atmospheric conditions, and come up with a model for the wettability of ionocovalent ceramics (ICs) by metals using a machine learning (ML) algorithm. The random forest model uses the testing temperature and ∼40 features generated based on the chemical compositions of the metal and the ceramic as predictors and exhibits strong predictive power with an R2 of ∼0.86. Moreover, this model and the featurization code are integrated into a single computational pipeline to enable (1) predicting metal-IC wettability of interest and (2) high-throughput searching of ICs with the desired wettability by certain metals in the entire Inorganic Crystallographic Structure Database. As a demonstration of this pipeline, the wettability of a Li-ion and electron insulator (LEI), CaO, by molten Li is estimated and compared with ab initio molecular dynamics simulation result. This ML pipeline can serve as a practical tool for methodical design of materials in systems where certain metal-ceramic wettability is desired. Wetting ; Materials science ; Ceramic ; Pipeline (software) ; Composite material ; Mechanical engineering ; Nanotechnology ; Computer science ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electronic and Structural Properties of Oxides
Authors: Bin Xiong | Xinpeng Zhao | Yunfeng Hu | Haiyou Huang | Yang Liu | Yanjing Su
Source: Materials & Design (article, 2021-08-09)
Field: Materials Science — Materials Chemistry

Machine learning assisted empirical formula augmentation Tuning the martensite transformation temperature through composition design has become an important way to broaden the applicable temperature range of shape memory alloys (SMAs). The empirical formula based on traditional statistics is a key reference for composition design. Due to the lack of experimental data, a large deviation may exist among the prediction results from the empirical formulas obtained by different data sources. In present work, we proposed an augmentation strategy of empirical formula based on a machine learning method to build the relationship between martensite transformation start temperature (Ms) and compositions in Cu-Al-based SMA system. A series of ML models were established by physical and chemical features and a Gaussian radial basis kernel function support vector machine (SVR.rbf) model was screened out based on mathematical and domain knowledge criteria. An augmented empirical formula of Ms as the function of compositions was fitted based on the abundant augmented dataset combined experimental data with predicted data by the SVR.rbf model. Compared with previous empirical formula fitted by small experimental dataset, the accuracy and robustness of the augmented empirical formula was significantly improved without additional experimental cost. This strategy offers a recipe to build empirical formula based on a small experimental dataset. Empirical research ; Gaussian function ; Robustness (evolution) ; Machine learning ; Support vector machine ; Series (stratigraphy) ; Gaussian ; Experimental data ; Empirical modelling ; Gaussian process ; Transformation (genetics) ; Computer science ; Radial basis function ; Range (aeronautics) ; Artificial intelligence ; Function (biology) ; Algorithm ; Materials science ; Artificial neural network ; Mathematics ; Statistics ; Simulation Shape Memory Alloy Transformations ; Microstructure and Mechanical Properties of Steels
Authors: Kavitha Thangavelu | Florent Ravaux | Linda Zou
Source: Environmental Technology & Innovation (article, 2021-08-13)
Field: Materials Science — Surfaces, Coatings and Films

Cellulose acetate-MoS <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e153" altimg="si1.svg"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> amphiphilic Janus-like fibrous sponge for removing oil from wastewater Toluene ; Adsorption ; Sponge ; Materials science ; Wastewater ; Amphiphile ; Chemical engineering ; Polyester ; Nuclear chemistry ; Polymer ; Chemistry ; Organic chemistry ; Composite material ; Waste management Surface Modification and Superhydrophobicity ; Pickering emulsions and particle stabilization ; Solar-Powered Water Purification Methods
Authors: Hamad S. Rashed | Puneet Mishra | Alison Nordon | David Scott Palmer | Matthew J. Baker
Source: Vibrational Spectroscopy (article, 2021-01-08)
Field: Chemistry — Analytical Chemistry

A comparative investigation of two handheld near-ir spectrometers for direct forensic examination of fibres in-situ Spectrometer ; Mobile device ; Textile ; Computer science ; Near-infrared spectroscopy ; Forensic examination ; Noise (video) ; Materials science ; Overtone ; Artificial intelligence ; Pattern recognition (psychology) ; Optics ; Composite material ; Engineering ; Forensic engineering ; Spectral line ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Industrial Vision Systems and Defect Detection
Authors: Jinseok Ryu | Hyeohn Kim | Ryeong Myeong Kim | Sung‐Tae Kim | Jaeyeon Jo | Sangmin Lee | Ki Tae Nam | Young‐Chang Joo | Gyu‐Chul Yi | Jaejin Lee | Miyoung Kim
Source: Ultramicroscopy (article, 2021-05-13)
Field: Materials Science — Materials Chemistry

Dimensionality reduction and unsupervised clustering for EELS-SI Hyperspectral imaging ; Cluster analysis ; Dimensionality reduction ; Pattern recognition (psychology) ; Reduction (mathematics) ; Cluster (spacecraft) ; Spectral line ; Curse of dimensionality ; Computer science ; Artificial intelligence ; Spectral clustering ; Principal component analysis ; Unsupervised learning ; Biological system ; Mathematics ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Sergei Manzhos | Giacomo Giorgi | Johann Lüder | Manabu Ihara
Source: Advances in Physics X (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond Plasmonic particles and nanostructures are widely used in photovoltaic and photonics. Surface plasmons were found to enhance different types of solar cells including plasmonic DSSCs, plasmonic solid semiconductor solar cells, plasmonic organic solar cells, and plasmonic perovskite solar cell. Size, composition, and shape of plasmonic nanoparticles as well as nanometer-distance control between particles are key design factors of plasmonic nanostructures. Modeling is rapidly gaining in importance for mechanistic understanding and rational design of plasmonic nanostructures. We review the modeling approaches used to model plasmon resonance features of nanostructures, from classical approaches that can routinely handle most particle sizes used in solar cells to approaches beyond classical electrodynamics such as ab initio approaches based on time-dependent density functional theory (TD-DFT). We highlight recently emerging approaches which have the potential to significantly enhance modeling capabilities in the coming years, in particular, by allowing atomistic (ab initio) modeling at realistic length scales, i.e. of particle sizes beyond 10 nm which are of most interest to plasmonic solar cells but remain problematic with traditional DFT-based techniques, such as density functional tight binding (DFTB) based approaches, time-dependent orbital-free DFT, and machine learning-based approaches, as well as many-body perturbation theory which is expected to gain usage with advances in computing power. Plasmon ; Plasmonic solar cell ; Nanotechnology ; Nanostructure ; Ab initio ; Density functional theory ; Materials science ; Photonics ; Plasmonic nanoparticles ; Nanoparticle ; Optoelectronics ; Solar cell ; Physics ; Chemistry ; Polymer solar cell ; Computational chemistry ; Quantum mechanics Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Yao Du | Penghua Ying | Jin Zhang
Source: Carbon (article, 2021-08-18)
Field: Materials Science — Materials Chemistry

Prediction and optimization of the thermal transport in hybrid carbon-boron nitride honeycombs using machine learning Thermal conductivity ; Materials science ; Boron nitride ; Phonon ; Doping ; Thermal ; Carbon fibers ; Phonon scattering ; Carbon nitride ; Inverse ; Composite material ; Composite number ; Optoelectronics ; Condensed matter physics ; Thermodynamics ; Geometry ; Chemistry ; Mathematics ; Physics Thermal properties of materials ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Jin-Kyu Kang | Yoon-Gu Kim | Seung-Chan Lee | Ho‐Young Jang | Suk-Hyun Yoo | Song-Bae Kim
Source: Microporous and Mesoporous Materials (article, 2021-10-15)
Field: Chemistry — Spectroscopy

Artificial neural network and response surface methodology modeling for diclofenac removal by quaternized mesoporous silica SBA-15 in aqueous solutions Aqueous solution ; Diclofenac ; Mesoporous material ; Mesoporous silica ; Chemical engineering ; Chemistry ; Materials science ; Chromatography ; Organic chemistry ; Catalysis ; Engineering ; Biochemistry Analytical Chemistry and Chromatography ; Mesoporous Materials and Catalysis ; Analytical Chemistry and Sensors
Authors: Marina Godino‐Ojer | Sergio Morales‐Torres | Elena Pérez‐Mayoral | Francisco J. Maldonado‐Hódar
Source: Journal of environmental chemical engineering (article, 2021-11-26)
Field: Chemistry — Organic Chemistry

Enhanced catalytic performance of ZnO/carbon materials in the green synthesis of poly-substituted quinolines A highly efficient methodology for the selective synthesis of nitrogen heterocycles via Friedländer reaction using carbon materials supported ZnO catalysts under the green chemistry domain is presented. The influence of the physicochemical properties of different carbon supports, in particular an activated carbon (AC), multi-walled carbon nanotubes (MWCNT) and a carbon aerogel (CA), on the catalytic performance is discussed. The developed catalysts are easily prepared by simple incipient wetness impregnation and a subsequent thermal treatment. These ZnO/carbon catalysts showed a great performance in the Friedländer condensation of 2-amino-5-chlorobenzaldehyde and carbonylic compounds with enolizable hydrogens, under solvent-free and mild conditions, affording in all cases selectively a total conversion to the corresponding quinoline. Both the Zn loading in combination with the developed microporosity of the selected carbon supports seem to be the key factors determining the catalytic performance. Yields obtained with ZnO/carbon composites catalysts are superior to those obtained by others widely used in fine chemistry such as, Zn-catalysts supported on mesoporous silica (SBA-15) and Zn-metal-organic-frameworks (MOF). Catalysis ; Aerogel ; Carbon fibers ; Quinoline ; Mesoporous material ; Materials science ; Chemistry ; Carbon nanotube ; Chemical engineering ; Organic chemistry ; Inorganic chemistry ; Nanotechnology ; Composite material Multicomponent Synthesis of Heterocycles ; Nanomaterials for catalytic reactions ; Chemical Synthesis and Reactions
Authors: Felix T. Bölle | Arghya Bhowmik | Tejs Vegge | Juan Maria García Lastra | Ivano E. Castelli
Source: Batteries & Supercaps (article, 2021-06-08)
Field: Materials Science — Materials Chemistry

Automatic Migration Path Exploration for Multivalent Battery Cathodes using Geometrical Descriptors Abstract Stable and fast ionic conductors for magnesium cathode materials have the prospect of enabling high energy density batteries beyond current Lithium‐ion technologies. So far, only a few candidate materials have been identified leading to data only being scarcely available to the community. Here, we present a systematic study, in the framework of Density Functional Theory, including the estimation of the migration barrier for 16 materials through employing Nudged Elastic Band (NEB) calculations. By introducing a path finder algorithm based on the idea of Voronoi tessellations, we show that an estimate of the transition state configuration can be extracted automatically prior to running NEB‐calculations. Using geometrical descriptors in combination with a principal component analysis it is possible to further sub‐group the migration paths. This approach also extends to materials which are not part of the study, making it a viable approach to more efficiently explore crystal structures with distinguishable migration characteristics. Path (computing) ; Cathode ; Battery (electricity) ; Voronoi diagram ; Principal component analysis ; State (computer science) ; Electrical conductor ; Trajectory Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advancements in Battery Materials
Authors: Jiaxing Qu | Adam Balvanz | Sviatoslav Baranets | Svilen Bobev | Prashun Gorai
Source: Materials Horizons (article, 2021-11-26)
Field: Materials Science — Materials Chemistry

Computational design of thermoelectric alloys through optimization of transport and dopability Alloying is a common technique to optimize the functional properties of materials for thermoelectrics, photovoltaics, energy storage <i>etc.</i> Designing thermoelectric (TE) alloys is especially challenging because it is a multi-property optimization problem, where the properties that contribute to high TE performance are interdependent. In this work, we develop a computational framework that combines first-principles calculations with alloy and point defect modeling to identify alloy compositions that optimize the electronic, thermal, and defect properties. We apply this framework to design n-type Ba<sub>2(1-<i>x</i>)</sub>Sr<sub>2<i>x</i></sub>CdP<sub>2</sub> Zintl thermoelectric alloys. Our predictions of the crystallographic properties such as lattice parameters and site disorder are validated with experiments. To optimize the conduction band electronic structure, we perform band unfolding to sketch the effective band structures of alloys and find a range of compositions that facilitate band convergence and minimize alloy scattering of electrons. We assess the n-type dopability of the alloys by extending the standard approach for computing point defect energetics in ordered structures. Through the application of this framework, we identify an optimal alloy composition range with the desired electronic and thermal transport properties, and n-type dopability. Such a computational framework can also be used to design alloys for other functional applications beyond TE. Thermoelectric effect ; Materials science ; Engineering physics ; Nanotechnology ; Mechanical engineering ; Engineering ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Faezeh Fathi | Samad Nejad Ebrahimi | Ana I. G. Valadão | Nelson Andrade | Anabela S. G. Costa | Cláudia Silva | Alireza Fathi | Peyman Salehi | Fátima Martel | Rita C. Alves | M. Beatriz P.P. Oliveira
Source: Molecules (article, 2021-09-30)
Field: Chemistry — Analytical Chemistry

Exploring Gunnera tinctoria: From Nutritional and Anti-Tumoral Properties to Phytosome Development Following Structural Arrangement Based on Molecular Docking <i>Gunnera tinctoria</i>, an underexplored invasive plant found in Azores, Portugal, was studied regarding its nutritional, antioxidant, and antitumoral properties. Higher antioxidant activity was found in baby leaves, followed by adult leaves and inflorescences. A phenolic fraction of the plant was enriched using adsorbent resin column chromatography (Diaion<sup>TM</sup> HP20LX, and Relite EXA90). Antitumoral effects were observed with the enriched fractions in breast (MCF-7) and pancreatic (AsPC-1) cancer cell lines, being more pronounced in the latter. To improve protection and membrane absorption rates of phenolic compounds, nano-phytosomes and cholesterol-conjugated phytosomes coated with natural polymers were loaded with the enriched fraction. The particles were characterized, and their physiochemical properties were evaluated and compared. All samples presented anionic charge and nanometer size in relation to the inner layer and micrometer size regarding the external layers. In addition, the molecular arrangement of phenolics within both types of phytosomes were studied for the first time by molecular docking. Polarity and molecular size were key factors on the molecular arrangement of the lipid bilayer. In conclusion, <i>G. tinctoria</i> showed to be an interesting source of nutrients and phenolic compounds with anti-tumoral potential. Moreover, phytosome loading with these compounds can increase their stability and bioavailability having in view future applications. Chemistry ; Bioavailability ; Antioxidant ; Proanthocyanidin ; Conjugated system ; Flavanone ; Quercetin ; Botany ; Organic chemistry ; Polymer ; Flavonoid ; Biology ; Polyphenol Heavy Metals in Plants ; Selenium in Biological Systems ; Silymarin and Mushroom Poisoning
Authors: Cesare Roncaglia | Daniele Rapetti | Riccardo Ferrando
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Regression and clustering algorithms for AgCu nanoalloys: from mixing energy predictions to structure recognition The lowest-energy structures of AgCu nanoalloys are searched for by global optimization algorithms for sizes 100 and 200 atoms depending on composition. Even though the AgCu system is very weakly miscible in macroscopic samples, the mixing energy for these nanoalloys turns out to be clearly negative for both sizes, a result which is attributed to the stabilization of non-crystalline Cu@Ag core-shell structures at the nanoscale. The mixing energy is a quantity nowadays unknown in its functional form, so that its prediction may take advantage of machine learning techniques. A support vector regressor is then implemented to successfully predict the mixing energy of AgCu nanoalloys of both sizes. Moreover, with the help of unsupervised learning algorithms, it is shown that the automatic classification of such nanoalloys into different physically meaningful structural families is indeed possible. Finally, thanks to the harmonic superposition approximation, the temperature-dependent probabilities of such structural families are calculated. Mixing (physics) ; Superposition principle ; Cluster analysis ; Energy (signal processing) ; Harmonic ; Computer science ; Algorithm ; Statistical physics ; Materials science ; Artificial intelligence ; Physics ; Mathematics ; Statistics ; Mathematical analysis Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Aljaž Gaber | Miha Pavšič
Source: International Journal of Molecular Sciences (review, 2021-08-23)
Field: Materials Science — Materials Chemistry

Modeling and Structure Determination of Homo-Oligomeric Proteins: An Overview of Challenges and Current Approaches Protein homo-oligomerization is a very common phenomenon, and approximately half of proteins form homo-oligomeric assemblies composed of identical subunits. The vast majority of such assemblies possess internal symmetry which can be either exploited to help or poses challenges during structure determination. Moreover, aspects of symmetry are critical in the modeling of protein homo-oligomers either by docking or by homology-based approaches. Here, we first provide a brief overview of the nature of protein homo-oligomerization. Next, we describe how the symmetry of homo-oligomers is addressed by crystallographic and non-crystallographic symmetry operations, and how biologically relevant intermolecular interactions can be deciphered from the ordered array of molecules within protein crystals. Additionally, we describe the most important aspects of protein homo-oligomerization in structure determination by NMR. Finally, we give an overview of approaches aimed at modeling homo-oligomers using computational methods that specifically address their internal symmetry and allow the incorporation of other experimental data as spatial restraints to achieve higher model reliability. Protein structure ; Homology modeling ; Intermolecular force ; Molecule ; Chemistry ; Computational biology ; Crystallography ; Biology ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Glycosylation and Glycoproteins Research
Authors: Lauren Katz | Alessandra Tata | Michael Woolman | Arash Zarrine‐Afsar
Source: Metabolites (review, 2021-09-28)
Field: Chemistry — Spectroscopy

Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies. Lipidome ; Profiling (computer programming) ; Lipidomics ; Mass spectrometry ; Context (archaeology) ; Computational biology ; Computer science ; Bioinformatics ; Chemistry ; Chromatography ; Biology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Stanislav Musikhin | S. Talebi-Moghaddam | Joel C. Corbin | Gregory J. Smallwood | Christof Schulz | Kyle J. Daun
Source: Carbon (article, 2021-06-18)
Field: Materials Science — Materials Chemistry

Crumpled few-layer graphene: Connection between morphology and optical properties Materials science ; Graphene ; Graphite ; Refractive index ; Discrete dipole approximation ; Soot ; Rayleigh scattering ; Particle size ; Nanoparticle ; Absorption (acoustics) ; Particle (ecology) ; Colloid ; Optics ; Nanotechnology ; Composite material ; Scattering ; Optoelectronics ; Chemical engineering ; Chemistry ; Physics Graphene research and applications ; Electrohydrodynamics and Fluid Dynamics ; Laser-Ablation Synthesis of Nanoparticles