jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Justin E. Griffith | Yusu Chen | Qingsong Liu | Qifeng Wang | Jeffrey Richards | Danielle Tullman‐Ercek | Kenneth R. Shull | Muzhou Wang
Source: Materials Horizons (article, 2022-10-18)
Field: Materials Science — Materials Chemistry

Quantitative high-throughput measurement of bulk mechanical properties using commonly available equipment Machine learning approaches have introduced an urgent need for large datasets of materials properties. However, for mechanical properties, current high-throughput measurement methods typically require complex robotic instrumentation, with enormous capital costs that are inaccessible to most experimentalists. A quantitative high-throughput method using only common lab equipment and consumables with simple fabrication steps is long desired. Here, we present such a technique that can measure bulk mechanical properties in soft materials with a common laboratory centrifuge, multiwell plates, and microparticles. By applying a homogeneous force on the particles embedded inside samples in the multiwell plate using centrifugation, we show that our technique measures the fracture stress of gels with similar accuracy to a rheometer. However, our method has a throughput on the order of 10<sup>3</sup> samples per run and is generalizable to virtually all soft material systems. We hope that our method can expedite materials discovery and potentially inspire the future development of additional high-throughput characterization methods. Throughput ; Rheometer ; Materials science ; Fracture (geology) ; Reliability engineering ; Process engineering ; Nanotechnology ; Composite material ; Computer science ; Rheology ; Engineering ; Telecommunications Machine Learning in Materials Science ; Nanofabrication and Lithography Techniques ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Elizabeth A. Pogue | Alexander New | K. McElroy | Nam Q. Le | Michael J. Pekala | Ian McCue | Eddie Gienger | Janna Domenico | Elizabeth Hedrick | Tyrel M. McQueen | Brandon Wilfong | Christine Piatko | Christopher R. Ratto | Andrew Lennon | Christine Chung | Timothy Montalbano | Gregory Bassen | Christopher D. Stiles
Source: arXiv (Cornell University) (preprint, 2022-12-22)
Field: Materials Science — Materials Chemistry

Closed-loop machine learning for discovery of novel superconductors The discovery of novel materials drives industrial innovation, although the pace of discovery tends to be slow due to the infrequency of "Eureka!" moments. These moments are typically tangential to the original target of the experimental work: "accidental discoveries". Here we demonstrate the acceleration of intentional materials discovery - targeting material properties of interest while generalizing the search to a large materials space with machine learning (ML) methods. We demonstrate a closed-loop ML discovery process targeting novel superconducting materials, which have industrial applications ranging from quantum computing to sensors to power delivery. By closing the loop, i.e. by experimentally testing the results of the ML-generated superconductivity predictions and feeding data back into the ML model to refine, we demonstrate that success rates for superconductor discovery can be more than doubled. In four closed-loop cycles, we discovered a new superconductor in the Zr-In-Ni system, re-discovered five superconductors unknown in the training datasets, and identified two additional phase diagrams of interest for new superconducting materials. Our work demonstrates the critical role experimental feedback provides in ML-driven discovery, and provides definite evidence that such technologies can accelerate discovery even in the absence of knowledge of the underlying physics. Superconductivity ; Computer science ; Pace ; Drug discovery ; Closing (real estate) ; Scientific discovery ; Physics ; Artificial intelligence ; Bioinformatics ; Condensed matter physics ; Biology Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electronic and Structural Properties of Oxides
Authors: Jorge Cervera-Gascó | Adrián Rabadán | Eulogio López-Mata | Manuel Álvarez‐Ortí | José Emilio Pardo González
Source: Food Control (article, 2022-08-01)
Field: Chemistry — Organic Chemistry

Development of the POLIVAR model using neural networks as a tool to predict and identify monovarietal olive oils Olive oil ; Campesterol ; Artificial neural network ; Quality (philosophy) ; Oleic acid ; European union ; Palmitic acid ; Arachidic acid ; Biotechnology ; Computer science ; Biochemical engineering ; Food science ; Artificial intelligence ; Biology ; Botany ; Business ; Engineering ; Fatty acid Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality
Authors: Zeynep Uruk | Alper Kiraz
Source: Journal of Polymer Engineering (article, 2022-12-12)
Field: Chemistry — Analytical Chemistry

Artificial intelligence based prediction models for rubber compounds Abstract In the rubber industry, rheometric properties are critical in defining processing times and temperatures. These parameters of rubber compounds are determined by time-consuming and expensive laboratory studies performed in a rheometer. Artificial intelligence approaches, on the other hand, may be used to estimate rheometric properties in seconds without the need for any samples or laboratory experiments. In this research, artificial neural network, Gaussian process regression, and support vector regression techniques are used to predict minimum and maximum torque, 30% and 60% cure time of a rubber compound using both process parameters and raw material composition as input. The dataset comprises 1128 batches of the selected rubber compound. A detailed sensitivity analysis is performed to determine the best performing hyperparameters and the prediction performances are expressed as mean absolute percentage error (MAPE). Minimum, maximum, and average MAPE values are presented for each artificial intelligence technique. Besides this research contributes to fill the gap in rubber industry literature, the results obtained also strongly improve the existing literature results. Natural rubber ; Artificial neural network ; Mean absolute percentage error ; Hyperparameter ; Materials science ; Rheometer ; Kriging ; Mooney viscosity ; Artificial intelligence ; Support vector machine ; Machine learning ; Biological system ; Statistics ; Computer science ; Mathematics ; Composite material ; Polymer ; Rheology Petroleum Processing and Analysis ; Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems
Authors: Mireia Pérez‐Baeza | Yolanda Martı́n-Biosca | Laura Escuder‐Gilabert | M.J. Medina-Hernández | S. Sagrado
Source: Journal of Chromatography A (article, 2022-04-11)
Field: Chemistry — Spectroscopy

Artificial neural networks to model the enantioresolution of structurally unrelated neutral and basic compounds with cellulose tris(3,5-dimethylphenylcarbamate) chiral stationary phase and aqueous-acetonitrile mobile phases Artificial neural network ; Chemistry ; Biological system ; Acetonitrile ; Tris ; Cross-validation ; Cellulose ; Feature (linguistics) ; Phase (matter) ; Molecule ; Pattern recognition (psychology) ; Chromatography ; Artificial intelligence ; Computer science ; Organic chemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Rosaria Ciriminna | Mario Pagliaro
Source: Topics in Current Chemistry (review, 2022-12-21)
Field: Materials Science — Materials Chemistry

Shape and Stability Matter: Enhanced Catalytic Reactions via Sol–gel-Entrapped Catalysts The possibility to tune the solid catalyst morphology and the unique chemical and physical stability of organosilica-entrapped sol-gel catalysts allow the application of these catalysts to the synthesis of a wide variety of valued molecules, including polymers, manufactured by the fine-chemical industry. Referring to selected independent research achievements, we provide a practice oriented insight on these materials that will hopefully be useful in new, unified catalysis education aimed to foster the uptake of heterogeneous catalysis in the fine and specialty chemical industry. Catalysis ; Chemistry ; Nanotechnology ; Heterogeneous catalysis ; Polymer ; Chemical engineering ; Speciality chemicals ; Chemical stability ; Organic chemistry ; Biochemical engineering ; Combinatorial chemistry ; Materials science Mesoporous Materials and Catalysis ; Polyoxometalates: Synthesis and Applications ; Oxidative Organic Chemistry Reactions
Authors: Wenda Hu | Nicholas R. Jaegers | Austin D. Winkelman | Shiva Murali | Karl T. Mueller | Yong Wang | Jian Zhi Hu
Source: Frontiers in Catalysis (article, 2022-07-19)
Field: Chemistry — Spectroscopy

Modelling complex molecular interactions in catalytic materials for energy storage and conversion in nuclear magnetic resonance Nuclear magnetic resonance (NMR) is a non-destructive and atom-specific specific tool that has become a burgeoning analytic method for understanding the detailed molecular interactions in catalysis and energy storage materials. However, the observation of diverse chemical shifts arising from complex molecular interactions makes the interpretation of NMR spectroscopy increasingly challenging, in particular for a novel system without standards for comparison. Density functional theory-NMR (DFT-NMR) is an indispensable tool to mitigate these challenges and provide detailed 3D molecular structures that relate materials and reaction intermediate structures, and information about chemical interactions, dynamics, and reaction mechanisms. This review provides a fundamental background in DFT-NMR relating to theory development, critical parameters for calculating NMR properties, computational accuracy, and the current capabilities. A variety of practical examples from the fields of catalysis and energy storage, including CO 2 capture, are summarized to illustrate the capabilities of DFT-NMR application to date. Last but not least, cautionary notes on the application of these strategies are presented for researchers modeling their own systems. Nuclear magnetic resonance spectroscopy ; Density functional theory ; Molecular dynamics ; Variety (cybernetics) ; Chemistry ; Chemical shift ; Nanotechnology ; Chemical physics ; Materials science ; Computational chemistry ; Biochemical engineering ; Computer science ; Physical chemistry ; Organic chemistry ; Engineering Advanced NMR Techniques and Applications ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Rafael R. Solís | Daily Rodríguez‐Padrón | M.A. Martín‐Lara | M. Calero | Rafael Luque | Mario J. Muñoz‐Batista
Source: Chemosphere (article, 2022-10-10)
Field: Materials Science — Materials Chemistry

Coffee-waste templated CeOx/TiO2 nanostructured materials for selective photocatalytic oxidations Benzaldehyde ; Photocatalysis ; Calcination ; Materials science ; Selectivity ; Anatase ; Benzyl alcohol ; Solvent ; X-ray photoelectron spectroscopy ; Chemical engineering ; Catalysis ; Rutile ; Adsorption ; Environmentally friendly ; Nuclear chemistry ; Chemistry ; Organic chemistry Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Guangzhou Geng | Zhongshan Zhang | Chensheng Li | Ruhao Pan | Yunlong Li | Haifang Yang | Junjie Li
Source: Micromachines (review, 2022-05-30)
Field: Materials Science — Materials Chemistry

Atomic Layer Assembly Based on Sacrificial Templates for 3D Nanofabrication Three-dimensional (3D) nanostructures have attracted widespread attention in physics, chemistry, engineering sciences, and biology devices due to excellent functionalities which planar nanostructures cannot achieve. However, the fabrication of 3D nanostructures is still challenging at present. Reliable fabrication, improved controllability, and multifunction integration are desired for further applications in commercial devices. In this review, a powerful fabrication method to realize 3D nanostructures is introduced and reviewed thoroughly, which is based on atomic layer deposition assisted 3D assembly through various sacrificial templates. The aim of this review is to provide a comprehensive overview of 3D nanofabrication based on atomic layer assembly (ALA) in multifarious sacrificial templates for 3D nanostructures and to present recent advancements, with the ultimate aim to further unlock more potential of this method for nanodevice applications. Nanodevice ; Template ; Nanotechnology ; Nanolithography ; Fabrication ; Nanostructure ; Atomic layer deposition ; Materials science ; Layer (electronics) ; Computer science ZnO doping and properties ; Semiconductor materials and devices ; Nanowire Synthesis and Applications
Authors: Miloud Souiyah | Taoreed O. Owolabi | Saibu Saliu | Talal F. Qahtan | Nahier Aldhafferi | Abdullah Alqahtani
Source: Mathematical Problems in Engineering (article, 2022-04-26)
Field: Materials Science — Materials Chemistry

Specific Surface Area Characterization of Spinel Ferrite Nanostructure Based Compounds for Photocatalysis and Other Applications Using Extreme Learning Machine Method Nanocrystalline spinel ferrite based compounds are technological driven materials with interesting potentials in photocatalysis for renewable energy generation, gas sensing for pollution control, magnetic drug delivery, rod antennas, storage media (high density) and supercapacitive materials, among others. Specific surface area of spinel ferrite based compounds contributes immensely to the application of this semiconductor in industrial domains. Experimental determination of specific surface area is laborious and costly and consumes appreciable time. Compositional substitutions in crystal structure effectively improve physical properties and enhance specific surface area through alteration of moment distribution between tetrahedral oxygen sites and octahedral coordination. With the aid of distorted lattice parameters due to compositional substitution and the spinel ferrite nanocrystallite size as model descriptors, this present work models the specific surface area of spinel ferrite nanomaterial through extreme learning machine (ELM) based intelligent modeling method. The developed sigmoid activation function-based ELM (S-ELM) model shows superior performance over genetic algorithm based support vector regression (GBSVR) and stepwise regression (STWR) models existing in the literature with performance improvement of 61.31% and 70.01%, respectively, using root mean square error performance metric. The significances of cobalt and lanthanum compositional substitution on the specific surface area of spinel ferrite nanomaterials were investigated using S-ELM model. Ease of implementation of S-ELM model as compared with the existing GBSVR model, coupled with the demonstrated improved performance and persistent closeness of its predictions with the experimental values, would be highly meritorious for quick and precise characterization of specific surface area of spinel ferrite nanomaterials for various desired applications. Spinel ; Materials science ; Ferrite (magnet) ; Extreme learning machine ; Specific surface area ; Nanomaterials ; Nanotechnology ; Biological system ; Computer science ; Machine learning ; Composite material ; Metallurgy ; Chemistry Magnetic Properties and Synthesis of Ferrites ; Machine Learning and ELM ; Nanomaterials for catalytic reactions
Authors: Xia Long | Lihua Zhang | Zheng Tan | Bowei Zhou
Source: Energy Advances (article, 2022-12-26)
Field: Materials Science — Materials Chemistry

Progress on 2D–2D heterostructured hybrid materials for efficient electrocatalysis 2D–2D hybrids have large heterointerfaces and strong interactions between the two components, leading to strong coupling effects that influence the electronic structure of active sites and benefit charge transfer during energy conversion reactions. Electrocatalyst ; Coupling (piping) ; Materials science ; Charge (physics) ; Charge exchange ; Chemical physics ; Nanotechnology ; Energy transfer ; Energy transformation ; Optoelectronics ; Chemistry ; Electrode ; Physics ; Electrochemistry ; Physical chemistry ; Composite material 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Perovskite Materials and Applications
Authors: Neha Verma | Vishal Jagota | Alimuddin Alimuddin | Arnold C. Alguno | Manik Rakhra | K. Chanthirasekaran | Betty Nokobi Dugbakie
Source: Journal of Nanomaterials (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Morphological, Structural, and Optical Properties of Doped/Codoped ZnO Nanocrystals Film Prepared by Spin Coating Technique and Their Gas Sensing Application In today’s world of electronics, nanomaterial applications pose a challenge. The spin coating approach was used to create nanostructured ZnO with wurtzite structure in a recent study. Antimony doping, aluminum, and antimony codoping with 2.0 percent were used to make these films. The impact of doped and codoped films on structural, optical properties, and morphological has been examined using a variety of characterization approaches. A ZnO nanocrystal with a diameter of 20‐30 nm was discovered using XRD (X‐ray diffraction). According to SEM (scanning electron microscope) scans, the grain size is in the 80‐120 nm region. The use of Fourier transform infrared spectroscopy (FTIR) to detect elemental elements was studied, and the peak at 400‐520 cm -1 was identified as ZnO. The optical properties of doped and codoped ZnO were checked, and it was discovered that antimony‐doped ZnO has a larger band gap than Al and antimony‐codoped ZnO. This proved that ZnO may be used in gas sensors and solar cells. The gas response of a static gas sensor system based on Sb‐doped films was measured and compared to Al‐ and Sb‐codoped films in the presence of ethanol vapor. Materials science ; Wurtzite crystal structure ; Doping ; Spin coating ; Antimony ; Scanning electron microscope ; Nanocrystal ; Nanomaterials ; Fourier transform infrared spectroscopy ; Band gap ; Nanotechnology ; Coating ; Optoelectronics ; Analytical Chemistry (journal) ; Chemical engineering ; Zinc ; Composite material ; Metallurgy ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Copper-based nanomaterials and applications
Authors: Saskia Schimmel | Wenhao Sun | Natasha Dropka
Source: Crystals (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence for Crystal Growth and Characterization The Special Issue on “Artificial Intelligence for Crystal Growth and Characterization” comprises six original articles in this emerging field of research [...] Characterization (materials science) ; Field (mathematics) ; Artificial intelligence ; Crystal (programming language) ; Computer science ; Materials science ; Nanotechnology ; Mathematics Crystallization and Solubility Studies
Authors: Heather J. Kulik | Pratyush Tiwary
Source: MRS Bulletin (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence in computational materials science Nanotechnology ; Data science ; Domain (mathematical analysis) ; Nucleation ; Computer science ; Engineering ethics ; Management science ; Materials science ; Engineering ; Chemistry ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Nicholas G. Taylor | Marcus H. Reis | Travis P. Varner | Johann L. Rapp | Alexis Sarabia | Frank A. Leibfarth
Source: Polymer Chemistry (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

A dual initiator approach for oxygen tolerant RAFT polymerization Reversible-deactivation radical polymerizations are privileged approaches for the synthesis of functional and hybrid materials. A bottleneck for conducting these processes is the need to maintain oxygen free conditions. Herein we report a broadly applicable approach to "polymerize through" oxygen using the synergistic combination of two radical initiators having different rates of homolysis. The <i>in situ</i> monitoring of the concentrations of oxygen and monomer simultaneously provided insight into the function of the two initiators and enabled the identification of conditions to effectively remove dissolved oxygen and control polymerization under open-to-air conditions. By understanding how the surface area to volume ratio of reaction vessels influence open-to-air polymerizations, well-defined polymers were produced using acrylate, styrenic, and methacrylate monomers, which each represent an expansion of scope for the "polymerizing through" oxygen approach. Demonstration of this method in tubular reactors using continuous flow chemistry provided a more complete structure-reactivity understanding of how reaction headspace influences PTO RAFT polymerizations. Raft ; Dual (grammatical number) ; Reversible addition−fragmentation chain-transfer polymerization ; Polymerization ; Chemistry ; Polymer chemistry ; Oxygen ; Chemical engineering ; Radical polymerization ; Polymer ; Organic chemistry ; Engineering Advanced Polymer Synthesis and Characterization ; Photopolymerization techniques and applications ; biodegradable polymer synthesis and properties
Authors: Diego M. Ruiz | Gustavo Pasquale | José J. Martínez | Gustavo P. Romanelli
Source: Green Processing and Synthesis (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Advances in novel activation methods to perform green organic synthesis using recyclable heteropolyacid catalysis Abstract Highly functionalized, high value added bioactive molecules are generally obtained by synthetic procedures that are highly selective, economical, with high atom economy, and environmentally friendly. Following these guidelines, the use of recoverable solid catalysts, nonpolluting substrates, or toxic organic solvent contributes greatly to these demands. In the last three decades, heteropolyacids (HPAs) and its derivatives have received great attention as recyclable solid catalysts, due to their strong Brönsted acidity, excellent oxidizing capacity under mild conditions, and various reuse cycles without appreciable loss of their catalytic activity. However, new activation methods should be investigated to improve the sustainability of a process using HPAs. In this review, we report the latest advances associated with the synthesis of potentially bioactive molecules using more energy efficient alternatives such as microwaves, ultrasound, mechanochemistry, and photochemistry to minimize the energy consumption associated with organic synthesis. The transformations studied include construction reaction, heterocycle synthesis, selective oxidation, and biomass recovery. Catalysis ; Oxidizing agent ; Environmentally friendly ; Green chemistry ; Organic synthesis ; Combinatorial chemistry ; Atom economy ; Reuse ; Chemistry ; Organic chemistry ; Materials science ; Biochemical engineering ; Nanotechnology ; Waste management ; Reaction mechanism Polyoxometalates: Synthesis and Applications ; Chemical Synthesis and Reactions ; Multicomponent Synthesis of Heterocycles