jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tran Doan Huan | Vu Ngoc Tuoc
Source: Physical Review Materials (article, 2023-05-31)
Field: Materials Science — Materials Chemistry

Machine-learning approach for discovery of conventional superconductors First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future discoveries if their reliability can be improved. The main challenge of current ML approaches, typically aiming at predicting the critical temperature ${T}_{\mathrm{c}}$ of a solid from its chemical composition and target pressure, is that the correlations to be learned are deeply hidden, indirect, and uncertain. In this paper, we show that predicting superconductivity at any pressure from the atomic structure is sustainable and reliable. For a demonstration, we curated a diverse data set of 584 atomic structures for which $\ensuremath{\lambda}$ and ${\ensuremath{\omega}}_{log}$, two parameters of the electron-phonon interactions, were computed. We then trained some ML models to predict $\ensuremath{\lambda}$ and ${\ensuremath{\omega}}_{log}$, from which ${T}_{\mathrm{c}}$ can be computed in a postprocessing manner. The models were validated and used to identify two possible superconductors whose ${T}_{\mathrm{c}}\ensuremath{\simeq}10--15$ K at zero pressure. Interestingly, these materials have been synthesized and studied in some other contexts. In summary, the proposed ML approach enables a pathway to directly transfer what can be learned from the high-pressure atomic-level details that correlate with high-${T}_{\mathrm{c}}$ superconductivity to zero pressure. Going forward, this strategy will be improved to better contribute to the discovery of new superconductors. Superconductivity ; Lambda ; Zero (linguistics) ; Machine learning ; Materials science ; Hydride ; Omega ; Condensed matter physics ; Work (physics) ; Artificial intelligence ; Physics ; Computer science ; Thermodynamics ; Metal ; Quantum mechanics ; Metallurgy Machine Learning in Materials Science ; High-pressure geophysics and materials ; Inorganic Chemistry and Materials
Authors: Sonam Kumari | Anuranjan Misra | Amitabh Wahi | Pramod Singh Rathore
Source:  (article, 2023-08-03)
Field: Chemistry — Analytical Chemistry

Quality of Red Wine: Analysis and Comparative Study of Machine Learning Models Wine is an alcoholic beverage made from different varieties of grapes after fermenting of grapes. There are different styles of wine varieties depends upon types of grapes used (with or without peeling of skins of grapes) and strains of yeast like red wine, white wine, rose wine, orange or amber wine. Wine can be made from others fruits, grains, cashew coconut, honey etc. Quality and taste of wine depends upon making process and aging of wine. Top 3 Wine producer countries in world are Italy, Spain, and France as per 2021 report. Red wine contains 5.5 to 20.5% of alcohol in it. Consumption of Red wine in right amount is good for health but consumption in large amount is bad for health. The quality of red wine may be predicted by using artificial intelligence techniques with the help of different chemicals parameters as attributes. In this paper, red wine datasets were used for training and testing purpose for the classification of red wine into 6 categories. 6 classes of data were converted into two class based on the quality index. Four different approaches of machine learning algorithms were applied to predict the classification of red wine on two class datasets. It was found that out of four algorithms, the decision tree classifier predicted better performance result of red wine quality compared to other machine learning classifiers. Wine ; White Wine ; Orange (colour) ; Aging of wine ; Food science ; Artificial intelligence ; Mathematics ; Computer science ; Chemistry Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food
Authors: Mohammed Alsalman | Yousef A. Alghofaili | Ahmer A.B. Baloch | Haydar Alsadah | Abdulmohsen Alsaui | Saad M. Alqahtani | Ali H. Muqaibel | Fahhad H. Alharbi
Source: Computational Materials Science (article, 2023-07-07)
Field: Materials Science — Materials Chemistry

Outliers in Shannon’s effective ionic radii table and the table extension by machine learning Ionic radius ; Outlier ; Computer science ; Data point ; Table (database) ; Ionic bonding ; Algorithm ; Data mining ; Artificial intelligence ; Chemistry ; Ion Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Y.S. Wudil
Source: Results in Engineering (article, 2023-05-30)
Field: Materials Science — Materials Chemistry

Ensemble learning-based investigation of thermal conductivity of Bi2Te2.7Se0.3-based thermoelectric clean energy materials Bi2Te3-based materials are remarkable semiconducting compounds widely employed for clean energy harvesting via thermoelectric effects. Their energy conversion efficiency is assessed based on their thermoelectric figure of merit which depends on the electrical and thermal properties. The measurement of electrical conductivity is often straightforward; however, the thermal conductivity (κ) measurement is incredibly arduous and requires sophisticated experimental devices. Herein, we propose a pioneering machine-learning technique that estimates the value of κ for such materials based on their electrical properties and structural lattice constants (a and c). Decision tree regression (DTR) and Support vector regression (SVR) algorithms were employed to build the thermal conductivity estimators. Afterward, the concept of adaptive boosting was applied to the conventional regressors to improve their prediction accuracy. The performance of the developed models was evaluated based on the R2-value, coefficient of correlation (CC) between the actual and experimental values of κ, mean absolute error, and mean square error. The results obtained based on these metrics indicate that the boosted decision tree outperforms the rest of the models with a CC of 99.4% and an R2-value of 98.8% in the testing phase. The models were also utilized to make predictions in actual physical situations, such as forecasting the thermal conductivities of compounds doped with transition metals or non-metals, and examining how the values of κ are affected by substrate temperature during pulsed laser deposition. The remarkable performance of the models in predicting the thermal conductivity of different configurations of the material with high accuracy underscores its importance to researchers and industry for renewable energy applications. Thermal conductivity ; Boosting (machine learning) ; Thermoelectric effect ; Thermoelectric materials ; Materials science ; Decision tree ; Mean squared error ; Support vector machine ; Computer science ; Ensemble learning ; Machine learning ; Figure of merit ; Estimator ; Artificial intelligence ; Thermodynamics ; Mathematics ; Statistics ; Optoelectronics ; Physics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Supratim Mondal | Yeswanth Sthanikam | Anubhav Kumar | Abhijit Nandy | Sutirtha Chattopadhyay | Debasish Koner | Nandyala Rukmangadha | Narendra Hulikal | Shibdas Banerjee
Source: Analytical Chemistry (article, 2023-05-11)
Field: Chemistry — Spectroscopy

Mass Spectrometry Imaging of Lumpectomy Specimens Deciphers Diacylglycerols as Potent Biomarkers for the Diagnosis of Breast Cancer Detecting breast tumor markers with a fast turnaround time from frozen sections should foster intraoperative histopathology in breast-conserving surgery, reducing the need for a second operation. Hence, rapid label-free discrimination of the spatially resolved molecular makeup between cancer and adjacent normal breast tissue is of growing importance. We performed desorption electrospray ionization mass spectrometry imaging (DESI-MSI) of fresh-frozen excision specimens, including cancer and paired adjacent normal sections, obtained from the lumpectomy of 73 breast cancer patients. The results demonstrate that breast cancer tissue posits sharp metabolic upregulation of diacylglycerol, a lipid second messenger that activates protein kinase C for promoting tumor growth. We identified four specific <i>sn</i>-1,2-diacylglycerols that outperformed all other lipids simultaneously mapped by the positive ion mode DESI-MSI for distinguishing cancers from adjacent normal specimens. This result contrasts with several previous DESI-MSI studies that probed metabolic dysregulation of glycerophospholipids, sphingolipids, and free fatty acids for cancer diagnoses. A random forest-based supervised machine learning considering all detected ion signals also deciphered the highest diagnostic potential of these four diacylglycerols with the top four importance scores. This led us to construct a classifier with 100% overall prediction accuracy of breast cancer by using the parsimonious set of four diacylglycerol biomarkers only. The metabolic pathway analysis suggested that increased catabolism of phosphatidylcholine in breast cancer contributes to diacylglycerol overexpression. These results open up opportunities for mapping diacylglycerol signaling in breast cancer in the context of novel therapeutic and diagnostic developments, including the intraoperative assessment of breast cancer margin status. Breast cancer ; Diacylglycerol kinase ; Mass spectrometry imaging ; Chemistry ; Cancer ; Cancer research ; Context (archaeology) ; Lumpectomy ; Pathology ; Internal medicine ; Biochemistry ; Mass spectrometry ; Medicine ; Biology ; Kinase ; Mastectomy ; Protein kinase C ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Xiaohui Sun | Yang Song | Guiyuan Jiang | Xingying Lan | Chunming Xu
Source: Science China Materials (article, 2023-12-27)
Field: Materials Science — Materials Chemistry

Fundamentals and catalytic applications of single-atom alloys Atom (system on chip) ; Catalysis ; Materials science ; Metal ; Adsorption ; Dissociation (chemistry) ; Nanotechnology ; Characterization (materials science) ; Chemical physics ; Physical chemistry ; Chemistry ; Metallurgy ; Computer science ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Muhammad Hasnain Jameel | Muhammad Sufi Roslan | Mohd Zul Hilmi Mayzan | Mohd Arif Agam | Z.I. Zaki | Ahmed M. Fallatah
Source: Royal Society Open Science (article, 2023-07-01)
Field: Materials Science — Polymers and Plastics

Investigation of structural, electronic and optical properties of two-dimensional MoS <sub>2</sub> -doped-V <sub>2</sub> O <sub>5</sub> composites for photocatalytic application: a density functional theory study In the present research, the structural, electronic and optical properties of transition metal dichalcogenide-doped transition metal oxides MoS<sub>2</sub>-doped-V<sub>2</sub>O<sub>5</sub> with various doping concentrations (<i>x</i> = 1-3%) of MoS<sub>2</sub> atoms are studied by using first principles calculation. The generalized gradient approximation Perdew-Burke-Ernzerhof simulation approach is used to investigate the energy bandgap (<i>E<sub>g</sub></i>) of orthorhombic structures. We examined the energy bandgap (<i>E<sub>g</sub></i>) decrement from 2.76 to 1.30 eV with various doping (<i>x</i> = 1-3%) of molybdenum disulfide (MoS<sub>2</sub>) atoms. The bandgap nature shows that the material is a well-known direct bandgap semiconductor. MoS<sub>2</sub> doping (<i>x</i> = 1-3%) atoms in pentoxide (V<sub>2</sub>O<sub>5</sub>) creates the extra gamma active states which contribute to the formation of conduction and valance bands. MoS<sub>2</sub>-doped-V<sub>2</sub>O<sub>5</sub> composite is a proficient photocatalyst, has a large surface area for absorption of light, decreases the electron-hole pairs recombination rate and increases the charge transport. A comprehensive study of optical conductivity reveals that strong peaks of MoS<sub>2</sub>-doped-V<sub>2</sub>O<sub>5</sub> increase in ultraviolet spectrum region with small shifts at larger energy bands through increment doping <i>x</i> = 1-3% atoms of MoS<sub>2</sub>. A significant decrement was found in the reflectivity due to the decrement in the bandgap with doping. The optical properties significantly increased by the decrement of bandgap (<i>E<sub>g</sub></i>). Two-dimensional MoS<sub>2</sub>-doped-V<sub>2</sub>O<sub>5</sub> composite has high energy absorption, optical conductivity and refractive index, and is an appropriate material for photocatalytic applications. Density functional theory ; Doping ; Materials science ; Composite material ; Photocatalysis ; Composite number ; Optoelectronics ; Computational chemistry ; Chemistry Transition Metal Oxide Nanomaterials ; Advanced Photocatalysis Techniques ; Gas Sensing Nanomaterials and Sensors
Authors: Veronica Mănescu | Iulian Antoniac | Aurora Antoniac | Dan Lăptoiu | Gheorghe Păltânea | Robert Ciocoiu | Iosif Vasile Nemoianu | Lucian Gheorghe Gruionu | Horațiu Dura
Source: Biomimetics (article, 2023-12-17)
Field: Materials Science — Biomaterials

Bone Regeneration Induced by Patient-Adapted Mg Alloy-Based Scaffolds for Bone Defects: Present and Future Perspectives Treatment of bone defects resulting after tumor surgeries, accidents, or non-unions is an actual problem linked to morbidity and the necessity of a second surgery and often requires a critical healthcare cost. Although the surgical technique has changed in a modern way, the treatment outcome is still influenced by patient age, localization of the bone defect, associated comorbidities, the surgeon approach, and systemic disorders. Three-dimensional magnesium-based scaffolds are considered an important step because they can have precise bone defect geometry, high porosity grade, anatomical pore shape, and mechanical properties close to the human bone. In addition, magnesium has been proven in in vitro and in vivo studies to influence bone regeneration and new blood vessel formation positively. In this review paper, we describe the magnesium alloy's effect on bone regenerative processes, starting with a short description of magnesium's role in the bone healing process, host immune response modulation, and finishing with the primary biological mechanism of magnesium ions in angiogenesis and osteogenesis by presenting a detailed analysis based on a literature review. A strategy that must be followed when a patient-adapted scaffold dedicated to bone tissue engineering is proposed and the main fabrication technologies are combined, in some cases with artificial intelligence for Mg alloy scaffolds, are presented with examples. We emphasized the microstructure, mechanical properties, corrosion behavior, and biocompatibility of each study and made a basis for the researchers who want to start to apply the regenerative potential of magnesium-based scaffolds in clinical practice. Challenges, future directions, and special potential clinical applications such as osteosarcoma and persistent infection treatment are present at the end of our review paper. Scaffold ; Biocompatibility ; Bone healing ; Biomedical engineering ; Regeneration (biology) ; Materials science ; Magnesium alloy ; Regenerative medicine ; Medicine ; Magnesium ; Surgery ; Metallurgy ; Stem cell Magnesium Alloys: Properties and Applications ; Bone Tissue Engineering Materials ; Hydrogen Storage and Materials
Authors: Zhuo Wang | Rana Dabaja | Lei Chen | Mihaela Banu
Source: Scientific Reports (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Machine learning unifies flexibility and efficiency of spinodal structure generation for stochastic biomaterial design Porous biomaterials design for bone repair is still largely limited to regular structures (e.g. rod-based lattices), due to their easy parameterization and high controllability. The capability of designing stochastic structure can redefine the boundary of our explorable structure-property space for synthesizing next-generation biomaterials. We hereby propose a convolutional neural network (CNN) approach for efficient generation and design of spinodal structure-an intriguing structure with stochastic yet interconnected, smooth, and constant pore channel conducive to bio-transport. Our CNN-based approach simultaneously possesses the tremendous flexibility of physics-based model in generating various spinodal structures (e.g. periodic, anisotropic, gradient, and arbitrarily large ones) and comparable computational efficiency to mathematical approximation model. We thus successfully design spinodal bone structures with target anisotropic elasticity via high-throughput screening, and directly generate large spinodal orthopedic implants with desired gradient porosity. This work significantly advances stochastic biomaterials development by offering an optimal solution to spinodal structure generation and design. Flexibility (engineering) ; Spinodal ; Biomaterial ; Computer science ; Spinodal decomposition ; Artificial intelligence ; Materials science ; Mathematics ; Chemistry ; Nanotechnology ; Statistics Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Cellular and Composite Structures
Authors: Dan Han | Bonan Zhu | Zenghua Cai | Kieran B. Spooner | Stefan S. Rudel | Wolfgang Schnick | Thomas Bein | David O. Scanlon | H. Ebert
Source: Matter (article, 2023-11-15)
Field: Materials Science — Materials Chemistry

Discovery of multi-anion antiperovskites X6NFSn2 (X = Ca, Sr) as promising thermoelectric materials by computational screening The thermoelectric performance of existing perovskites lags far behind that of state-of-the-art thermoelectric materials such as SnSe. Despite halide perovskites showing promising thermoelectric properties, namely, high Seebeck coefficients and ultralow thermal conductivities, their thermoelectric performance is significantly restricted by low electrical conductivities. Here, we explore new multi-anion antiperovskites X6NFSn2 (X = Ca, Sr, and Ba) via B-site anion mutation in antiperovskite and global structure searches and demonstrate their phase stability by first-principles calculations. Ca6NFSn2 and Sr6NFSn2 exhibit decent Seebeck coefficients and ultralow lattice thermal conductivities (<1 W m−1 K−1). Notably, Ca6NFSn2 and Sr6NFSn2 show remarkably larger electrical conductivities compared to the halide perovskite CsSnI3. The combined superior electrical and thermal properties of Ca6NFSn2 and Sr6NFSn2 lead to high thermoelectric figures of merit (ZTs) of ∼1.9 and ∼2.3 at high temperatures. Our exploration of multi-anion antiperovskites X6NFSn2 (X = Ca, Sr) realizes the “phonon-glass, electron-crystal” concept within the antiperovskite structure. Antiperovskite ; Thermoelectric effect ; Materials science ; Thermoelectric materials ; Halide ; Ion ; Seebeck coefficient ; Condensed matter physics ; Perovskite (structure) ; Thermal conductivity ; Engineering physics ; Nanotechnology ; Thermodynamics ; Crystallography ; Chemistry ; Inorganic chemistry ; Composite material ; Physics ; Nitride Thermal Expansion and Ionic Conductivity ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Shahabaldin Jafarzadeh | Arman Farzaneh | Vahid Haddadi‐Asl | Iman Sahebi Jouibari
Source: Polymer Composites (review, 2023-09-07)
Field: Materials Science — Polymers and Plastics

A review on electrically conductive polyurethane nanocomposites: From principle to application Abstract Polyurethanes (PUs) thanks to the occurrence of phase separation and biphasic structure prevail over other polymer matrixes for impregnation of conductive fillers and production of conductive composites. A number of carbonous fillers including carbon black, carbon fiber, carbon nanotube, graphite and its derivatives, and fullerene can be loaded into the PU matrixes to impart electrical conductivity. The properties of the carbonous fillers, the filler/PU interactions, and the method of compositing determine the conductive performance of the composite. It is possible to modulate the properties of the conductive PU composites and employ them for a number of electronic applications including but not limited to the strain sensors, electromagnetic interference shielding materials, supercapacitors, and tissue engineering scaffolds. In the present review, first, the distinctive properties of PU as a matrix of conductive composites have been discussed. Then, various carbon fillers loaded into the PU matrixes and their structural and conductive properties have been debated. Thereafter, the fabrication methods of the conductive composites as well as the influential parameters on the electrical properties of the composites have been reviewed to provide an insight into how fulfill a PU composite with the desired electrical performance. And finally, a number of applications of conductive PU composites have been put forward. Highlights Polyurethanes are very capable matrixes for conductive composites. Carbonous fillers can be loaded into the PU matrixes to impart conductivity. Conductive PU composites are employed for a number of electronic applications. Herein, properties, fillers, and applications of PU composites are reviewed. Materials science ; Composite material ; Electrical conductor ; Electromagnetic shielding ; Graphite ; Carbon black ; Carbon nanotube ; Nanocomposite ; Composite number ; Thermoplastic polyurethane ; Polyurethane ; Filler (materials) ; Conductive polymer ; Polymer ; Elastomer ; Natural rubber Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Sofia Dembski | Thomas Schwarz | Maximilian Oppmann | Shahbaz Tareq Bandesha | Jörn Schmid | Sarah Wenderoth | Karl Mandel | Jan Hansmann
Source: Scientific Reports (article, 2023-07-15)
Field: Materials Science — Materials Chemistry

Establishing and testing a robot-based platform to enable the automated production of nanoparticles in a flexible and modular way Robotic systems facilitate relatively simple human-robot interaction for non-robot experts, providing the flexibility to implement different processes. In this context, shorter process times, as well as an increased product and process quality could be achieved. Robots short time-consuming processes, take over ergonomically unfavorable tasks and work efficiently all the time. In addition, flexible production is possible while maintaining or even increasing safety. This study describes the successful development of a dual-arm robot-based modular infrastructure and the establishment of an automated process for the reproducible production of nanoparticles. As proof of concept, a manual synthesis protocol for silica nanoparticle preparation with a diameter of about 200 nm as building blocks for photonic crystals was translated into a fully automated process. All devices and components of the automated system were optimized and adapted according to the synthesis requirements. To demonstrate the benefit of the automated nanoparticle production, manual (synthesis done by lab technicians) and automated syntheses were benchmarked. To this end, different processing parameters (time of synthesis procedure, accuracy of dosage etc.) and the properties of the produced nanoparticles were compared. We demonstrate that the use of the robot not only increased the synthesis accuracy and reproducibility but reduced the personnel time and costs up to 75%. Modular design ; Robot ; Computer science ; Production (economics) ; Self-reconfiguring modular robot ; Software engineering ; Manufacturing engineering ; Engineering ; Artificial intelligence ; Operating system ; Mobile robot ; Robot control Nanoparticles: synthesis and applications ; TiO2 Photocatalysis and Solar Cells ; Nanotechnology research and applications