jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sneha Singh | Zaid Bin Junaid | Vinay Vyas | Teekam Singh Kalyanwat | Subhram Subhrajyoti Rana
Source: Carbon Trends (article, 2021-08-04)
Field: Materials Science — Materials Chemistry

Identification of vacancy defects in carbon nanotubes using vibration analysis and machine learning Point vacancies are the most common defects in a carbon nanotube (CNT) lattice. However, there is no standard technique to identify these defects. Moreover, the effect of such vacancies on the vibrational properties of CNTs is unknown. Therefore, this paper presents the first-ever technique for identification of vacancy defects in single-walled carbon nanotubes (SWCNT) using their vibrational analyses and machine learning. 240 SWCNT samples were modelled using molecular-structural-mechanics-approach and their modes obtained using finite element analyses. Then, an analytical expression for fundamental vibration frequency of a vacancy defective SWCNT was derived using log-linear multiple regression. This frequency is found to decrease with: (i) increase in the magnitude of point vacancies; (ii) decrease in diameter; and (iii) increase in length. A polynomial support vector machine (SVM) was developed that successfully classifies pristine and vacancy defective SWCNT samples at test accuracy greater than 90%. The proposed technique for identification of point vacancies combines the developed SVM classification with inverse estimation using the derived expression. This technique will be useful for the most crucial characterization of SWCNTs, i.e., characterizing pristine and vacancy defective samples. The derived expression will be useful in predicting vibration response of vacancy defective SWCNTs, and deciding their applications. Vacancy defect ; Carbon nanotube ; Vibration ; Materials science ; Molecular vibration ; Support vector machine ; Lattice (music) ; Crystallographic defect ; Polynomial ; Algorithm ; Computational chemistry ; Nanotechnology ; Molecule ; Condensed matter physics ; Mathematical analysis ; Computer science ; Mathematics ; Machine learning ; Chemistry ; Physics ; Quantum mechanics ; Acoustics Carbon Nanotubes in Composites ; Force Microscopy Techniques and Applications ; Smart Materials for Construction
Authors: Dejun Jiang | Huiyong Sun | Jike Wang | Chang‐Yu Hsieh | Yuquan Li | Zhenhua Wu | Dongsheng Cao | Jian Wu | Tingjun Hou
Source: Briefings in Bioinformatics (article, 2021-12-24)
Field: Materials Science — Materials Chemistry

Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning Accurate prediction of atomic partial charges with high-level quantum mechanics (QM) methods suffers from high computational cost. Numerous feature-engineered machine learning (ML)-based predictors with favorable computability and reliability have been developed as alternatives. However, extensive expertise effort was needed for feature engineering of atom chemical environment, which may consequently introduce domain bias. In this study, SuperAtomicCharge, a data-driven deep graph learning framework, was proposed to predict three important types of partial charges (i.e. RESP, DDEC4 and DDEC78) derived from high-level QM calculations based on the structures of molecules. SuperAtomicCharge was designed to simultaneously exploit the 2D and 3D structural information of molecules, which was proved to be an effective way to improve the prediction accuracy of the model. Moreover, a simple transfer learning strategy and a multitask learning strategy based on self-supervised descriptors were also employed to further improve the prediction accuracy of the proposed model. Compared with the latest baselines, including one GNN-based predictor and two ML-based predictors, SuperAtomicCharge showed better performance on all the three external test sets and had better usability and portability. Furthermore, the QM partial charges of new molecules predicted by SuperAtomicCharge can be efficiently used in drug design applications such as structure-based virtual screening, where the predicted RESP and DDEC4 charges of new molecules showed more robust scoring and screening power than the commonly used partial charges. Finally, two tools including an online server (http://cadd.zju.edu.cn/deepchargepredictor) and the source code command lines (https://github.com/zjujdj/SuperAtomicCharge) were developed for the easy access of the SuperAtomicCharge services. Computer science ; Software portability ; Partial charge ; Exploit ; Transfer of learning ; Source code ; Usability ; Artificial intelligence ; Machine learning ; Graph ; Theoretical computer science ; Algorithm ; Molecule ; Chemistry ; Human–computer interaction ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Alexa B. Hanson | Cody A. Nizinski | Luther W. McDonald
Source: ACS Omega (article, 2021-07-07)
Field: Chemistry — Inorganic Chemistry

Effect of Diel Cycling Temperature, Relative Humidity, and Synthetic Route on the Surface Morphology and Hydrolysis of α-U<sub>3</sub>O<sub>8</sub> The speciation and morphological changes of α-U<sub>3</sub>O<sub>8</sub> following aging under diel cycling temperature and relative humidity (RH) have been examined. This work advances the knowledge of U-oxide hydration as a result of synthetic route and environmental conditions, ultimately giving novel insight into nuclear material provenance. α-U<sub>3</sub>O<sub>8</sub> was synthesized via the washed uranyl peroxide (UO<sub>4</sub>) and ammonium uranyl carbonate (AUC) synthetic routes to produce unaged starting materials with different morphologies. α-U<sub>3</sub>O<sub>8</sub> from UO<sub>4</sub> is comprised of subrounded particles, while α-U<sub>3</sub>O<sub>8</sub> from AUC contains blocky, porous particles approximately an order of magnitude larger than particles from UO<sub>4</sub>. For aging, a humidity chamber was programmed for continuous daily cycles of 12 "high" hours of 45 °C and 90% RH, and 12 "low" hours of 25 °C and 20% RH. Samples were analyzed at varying intervals of 14, 24, 36, 43, and 54 days. At each aging interval, crystallographic changes were measured via powder X-ray diffraction coupled with whole pattern fitting for quantitative analysis. Morphologic effects were studied via scanning electron microscopy and 12-way classification via machine learning. While all samples were found to have distinguishing morphologic characteristics (93.2% classification accuracy), α-U<sub>3</sub>O<sub>8</sub> from UO<sub>4</sub> had more apparent change with increasing aging time. Nonetheless, α-U<sub>3</sub>O<sub>8</sub> from AUC was found to hydrate more quickly than α-U<sub>3</sub>O<sub>8</sub> from UO<sub>4</sub>, which can likely be attributed to its larger surface area and porous starting material morphology. Relative humidity ; Uranyl ; Scanning electron microscope ; Humidity ; Morphology (biology) ; Porosity ; Specific surface area ; Materials science ; Chemical engineering ; Chemistry ; Composite material ; Organic chemistry ; Geology ; Ion ; Meteorology Radioactive element chemistry and processing ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Michael J. Ford | Yunsik Ohm | Keene Chin | Carmel Majidi
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2021-10-19)
Field: Materials Science — Polymers and Plastics

Composites of functional polymers: Toward physical intelligence using flexible and soft materials Abstract Materials that can assist with perception and responsivity of an engineered machine are said to promote physical intelligence. Physical intelligence may be important for flexible and soft materials that will be used in applications like soft robotics, wearable computers, and healthcare. These applications require stimuli responsivity, sensing, and actuation that allow a machine to perceive and react to its environment. The development of materials that exhibit some form of physical intelligence has relied on functional polymers and composites that contain these polymers. This review will focus on composites of functional polymers that display physical intelligence by assisting with perception, responsivity, or by off-loading computation. Composites of liquid crystal elastomers, shape-memory polymers, hydrogels, self-healing materials, and transient materials and their functionalities are examined with a viewpoint that considers physical intelligence. Graphic Abstract Soft robotics ; Responsivity ; Self-healing hydrogels ; Materials science ; Wearable computer ; Shape-memory polymer ; Computer science ; Physical computing ; Nanotechnology ; Elastomer ; Perception ; Polymer ; Artificial intelligence ; Composite material ; Robot ; Psychology ; Embedded system ; Photodetector Polymer composites and self-healing ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: Jordan De Jesus Silva | Niccolò Bartalucci | Benson J. Jelier | Samantha Grosslight | Tobias Gensch | Claas Schünemann | Bernd Müller | Paul C. J. Kamer | Christophe Copéret | Matthew S. Sigman | Antonio Togni
Source: Helvetica Chimica Acta (article, 2021-10-05)
Field: Chemistry — Organic Chemistry

Development and Molecular Understanding of a Pd‐Catalyzed Cyanation of Aryl Boronic Acids Enabled by High‐Throughput Experimentation and Data Analysis Abstract A synthetic method for the palladium‐catalyzed cyanation of aryl boronic acids using bench stable and non‐toxic N ‐cyanosuccinimide has been developed. High‐throughput experimentation facilitated the screen of 90 different ligands and the resultant statistical data analysis identified that ligand σ‐donation, π‐acidity and sterics are key drivers that govern yield. Categorization into three ligand groups – monophosphines, bisphosphines and miscellaneous – was performed before the analysis. For the monophosphines, the yield of the reaction increases for strong σ‐donating, weak π‐accepting ligands, with flexible pendant substituents. For the bisphosphines, the yield predominantly correlates with ligand lability. The applicability of the designed reaction to a wider substrate scope was investigated, showing good functional group tolerance in particular with boronic acids bearing electron‐withdrawing substituents. This work outlines the development of a novel reaction, coupled with a fast and efficient workflow to gain understanding of the optimal ligand properties for the design of improved palladium cross‐coupling catalysts. Chemistry ; Cyanation ; Ligand (biochemistry) ; Aryl ; Yield (engineering) ; Palladium ; Combinatorial chemistry ; Catalysis ; Boronic acid ; Substrate (aquarium) ; Steric effects ; Organic chemistry ; Receptor ; Biochemistry Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Janis Wirth | Silvan Englisch | Dominik Drobek | Benjamin Apeleo Zubiri | Mingjian Wu | Nicola Taccardi | Narayanan Raman | Peter Wasserscheid | Erdmann Spiecker
Source: Catalysts (article, 2021-07-01)
Field: Materials Science — Surfaces, Coatings and Films

Unraveling Structural Details in Ga-Pd SCALMS Systems Using Correlative Nano-CT, 360° Electron Tomography and Analytical TEM We present a comprehensive structural and analytical characterization of the highly promising supported catalytically active liquid metal solutions (SCALMS) system. This novel catalyst shows excellent performance for alkane dehydrogenation, especially in terms of resistance to coking. SCALMS consists of a porous support containing catalytically active low-melting alloy particles (e.g., Ga-Pd) featuring a complex structure, which are liquid at reaction temperature. High-resolution 3D characterization at various length scales is required to reveal the complex pore morphology and catalytically active sites’ location. Nano X-ray computed tomography (nano-CT) in combination with electron tomography (ET) enables nondestructive and scale-bridging 3D materials research. We developed and applied a correlative approach using nano-CT, 360°-ET and analytical transmission electron microscopy (TEM) to decipher the morphology, distribution and chemical composition of the Ga-Pd droplets of the SCALMS system over several length scales. Utilizing ET-based segmentations of nano-CT reconstructions, we are able to reliably reveal the homogenous porous support network with embedded Ga-Pd droplets featuring a nonhomogenous elemental distribution of Ga and Pd. In contrast, large Ga-Pd droplets with a high Ga/Pd ratio are located on the surface of SCALMS primary particles, whereas the droplet size and the Ga/Pd ratio decreases while advancing into the porous volume. Our studies reveal new findings about the complex structure of SCALMS which are required to understand its superior catalytic performance. Furthermore, advancements in lab-based nano-CT imaging are presented by extending the field of view (FOV) of a single experiment via a multiple region-of-interest (ROI) stitching approach. Electron tomography ; Materials science ; Characterization (materials science) ; Porosity ; Nano- ; Transmission electron microscopy ; Nanoscopic scale ; Tomography ; Nanotechnology ; Scanning transmission electron microscopy ; Chemical engineering ; Optics ; Composite material Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Ryan B. Jadrich | Christopher Ticknor | Jeffery A. Leiding
Source: The Journal of Chemical Physics (article, 2021-06-28)
Field: Materials Science — Materials Chemistry

First principles reactive simulation for equation of state prediction The high cost of density functional theory (DFT) has hitherto limited the ab initio prediction of the equation of state (EOS). In this article, we employ a combination of large scale computing, advanced simulation techniques, and smart data science strategies to provide an unprecedented ab initio performance analysis of the high explosive pentaerythritol tetranitrate (PETN). Comparison to both experiment and thermochemical predictions reveals important quantitative limitations of DFT for EOS prediction and thus the assessment of high explosives. In particular, we find that DFT predicts the energy of PETN detonation products to be systematically too high relative to the unreacted neat crystalline material, resulting in an underprediction of the detonation velocity, pressure, and temperature at the Chapman-Jouguet state. The energetic bias can be partially accounted for by high-level electronic structure calculations of the product molecules. We also demonstrate a modeling strategy for mapping chemical composition across a wide parameter space with limited numerical data, the results of which suggest additional molecular species to consider in thermochemical modeling. Equation of state ; Structural equation modeling ; State (computer science) ; Computer science ; Physics ; Thermodynamics ; Machine learning ; Algorithm Machine Learning in Materials Science ; Protein Structure and Dynamics ; Model Reduction and Neural Networks
Authors: Yifeng Huo | Samantha Curry | Andrew Trowbridge | Xurong Xu | Chaoyang Jiang
Source: Materials Advances (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface-enhanced Raman scattering-based molecular encoding with gold nanostars for anticounterfeiting applications Star-shaped gold nanoparticles with encoded molecular information can be used in security inks for anticounterfeiting applications. Colloidal gold ; Raman scattering ; Nanotechnology ; Encoding (memory) ; Materials science ; Nanoparticle ; Raman spectroscopy ; Computer science ; Optics ; Physics ; Artificial intelligence Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Mina Maruyama | Susumu Okada
Source: ACS Applied Nano Materials (article, 2021-03-15)
Field: Materials Science — Materials Chemistry

Carrier Redistribution in van der Waals Nanostructures Consisting of Bilayer Graphene and Buckybowl: Implications for Piezoelectric Devices The geometric and electronic structures of a complex nanomaterial consisting of a sumanene (C21H12) and bilayer graphene are investigated in terms of the molecular conformation between the layers using density functional theory. Our calculations indicated that the sumanene-intercalated bilayer graphene is the key material for designing the piezoelectric devices capable of supplying an electric energy density of 30 μWh/cm2. The dipole moment of a bowl-shaped hydrocarbon sumanene induces an electrostatic potential difference between the graphene layers, resulting in charge transfer between the graphene layers. Holes and electrons are induced on the graphene layers located at the convex and edge sides of sumanene, respectively, with the densities of 4.4 × 1012 and 6.6 × 1011/cm2. Furthermore, the two-dimensional nanospacing between the graphene layers changes the molecular conformation of sumanene from a bowl to flat structure at 0.8 GPa. van der Waals force ; Graphene ; Piezoelectricity ; Redistribution (election) ; Materials science ; Nanostructure ; Nanotechnology ; Bilayer ; Bilayer graphene ; Optoelectronics ; Chemistry ; Composite material ; Membrane ; Molecule ; Organic chemistry Graphene research and applications ; 2D Materials and Applications ; Carbon Nanotubes in Composites
Authors: Suresh Thapa | Sydney Provence | Devin Jessup | Jason Lapano | Matthew Brahlek | Jerzy T. Sadowski | Petra Reinke | Wencan Jin | R. Comès
Source: Journal of Vacuum Science & Technology A Vacuum Surfaces and Films (article, 2021-07-22)
Field: Materials Science — Materials Chemistry

Correlating surface stoichiometry and termination in SrTiO3 films grown by hybrid molecular beam epitaxy Hybrid oxide molecular beam epitaxy (hMBE), a thin-film deposition technique in which transition metal cations are delivered using a metal-organic precursor, has emerged as the state-of-the-art approach to the synthesis of electronic-grade complex oxide films with a stoichiometric growth window. However, numerous questions remain regarding the chemical mechanisms of the growth process and the surface properties of the resulting films. To examine these properties, thin film SrTiO3 (STO) was prepared by hMBE using a titanium tetraisopropoxide (TTIP) precursor for Ti delivery and an elemental Sr source on annealed STO and Nb-doped STO substrates with varying TTIP:Sr flux ratios to examine the conditions for the reported stoichiometric growth window. The films were transferred in vacuo to an x-ray photoelectron spectroscopy system to study the surface elemental composition. Samples were examined using x-ray diffraction to compare our surface sensitive results with previously reported measurements of the bulk of the films in the literature. Ex situ studies by atomic force microscopy, scanning tunneling microscopy, and low-energy electron microscopy confirmed the presence of surface reconstructions and an Ehrlich–Schwoebel barrier consistent with A-site SrO termination. We find that a surface exhibiting a mixture of SrO and TiO2 termination or a full SrO termination is necessary to obtain stoichiometric adsorption-controlled growth. These results indicate that surface Sr is necessary to maintain the chemical equilibrium for stoichiometric growth during the hMBE process, which is important for the design of future interfacial systems using this technique. Stoichiometry ; X-ray photoelectron spectroscopy ; Molecular beam epitaxy ; Materials science ; Thin film ; Oxide ; Scanning tunneling microscope ; Analytical Chemistry (journal) ; Strontium titanate ; Epitaxy ; Chemical engineering ; Nanotechnology ; Layer (electronics) ; Chemistry ; Physical chemistry Electronic and Structural Properties of Oxides ; Semiconductor materials and devices ; Magnetic and transport properties of perovskites and related materials
Authors: Bruce Napier | Ole Bang | Christos Markos | Peter M. Moselund | Laurent Huot | Frans J. M. Harren | Amir Khodabakhsh | H. Martin | Floria Ottonello Briano | Laurent Balet | Steve Lecomte | Christian Petersen | Niels Møller Israelsen | David Bastviken | Magnus Gålfalk | Łukasz Kubiszyn | Piotr Warzybok
Source: Journal of Physics Photonics (article, 2021-05-26)
Field: Chemistry — Spectroscopy

Ultra-broadband infrared gas sensor for pollution detection: the TRIAGE project Abstract Air pollution is one of the largest risk factors for disease or premature death globally, yet current portable monitoring technology cannot provide adequate protection at a local community level. Within the TRIAGE project, a smart, compact and cost-effective air quality sensor network will be developed for the hyperspectral detection of gases which are relevant for atmospheric pollution monitoring or dangerous for human health. The sensor is based on a mid-infrared supercontinuum source, providing ultra-bright emission across the 2–10 µ m wavelength region. Within this spectral range, harmful gaseous species can be detected with high sensitivity and selectivity. The spectroscopic sensor, which includes a novel multi-pass cell and detector, enables a smart robust photonic sensing system for real-time detection. With built-in chemometric analysis and cloud connection, the sensor will feed advanced deep-learning algorithms for various analyses, ranging from long-term continental trends in air pollution to urgent local warnings and alerts. Community-based distributed pollution sensing tests will be verified on municipal building rooftops and local transport platforms. Broadband ; Triage ; Infrared ; Remote sensing ; Environmental science ; Pollution ; Computer science ; Telecommunications ; Medical emergency ; Optics ; Medicine ; Geography ; Physics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Gas Sensing Nanomaterials and Sensors
Authors: F. Scholz | Mustafa Çevik | Philipp Hallensleben | P. Thomé | Gunther Eggeler | Jan Frenzel
Source: Materials (article, 2021-08-28)
Field: Materials Science — Materials Chemistry

A 3D Analysis of Dendritic Solidification and Mosaicity in Ni-Based Single Crystal Superalloys Ni-based single crystal superalloys contain microstructural regions that are separated by low-angle grain boundaries. This gives rise to the phenomenon of mosaicity. In the literature, this type of defect has been associated with the deformation of dendrites during Bridgman solidification. The present study introduces a novel serial sectioning method that allows to rationalize mosaicity on the basis of spatial dendrite growth. Optical wide-field micrographs were taken from a series of cross sections and evaluated using quantitative image analysis. This allowed to explore the growth directions of close to 2500 dendrites in a large specimen volume of approximately 450 mm<sup>3</sup>. The application of tomography in combination with the rotation vector base-line electron back-scatter diffraction method allowed to analyze how small angular differences evolve in the early stages of solidification. It was found that the microstructure consists of dendrites with individual growth directions that deviate up to ≈4° from the average growth direction of all dendrites. Generally, individual dendrite growth directions coincide with crystallographic <001> directions. The quantitative evaluation of the rich data sets obtained with the present method aims at contributing to a better understanding of elementary processes that govern competitive dendrite growth and crystal mosaicity. Mosaicity ; Materials science ; Superalloy ; Dendrite (mathematics) ; Diffraction ; Microstructure ; Crystallography ; Electron backscatter diffraction ; Directional solidification ; Rotation (mathematics) ; Deformation (meteorology) ; Optics ; Composite material ; Geometry ; X-ray crystallography ; Physics ; Chemistry ; Mathematics Solidification and crystal growth phenomena ; High Temperature Alloys and Creep ; Aluminum Alloy Microstructure Properties