jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lukas Gierster | Sesha Vempati | Julia Stähler
Source: Nature Communications (article, 2021-02-12)
Field: Materials Science — Materials Chemistry

Ultrafast generation and decay of a surface metal Band bending at semiconductor surfaces induced by chemical doping or electric fields can create metallic surfaces with properties not found in the bulk, such as high electron mobility, magnetism or superconductivity. Optical generation of such metallic surfaces on ultrafast timescales would be appealing for high-speed electronics. Here, we demonstrate the ultrafast generation of a metal at the (10-10) surface of ZnO upon photoexcitation. Compared to hitherto known ultrafast photoinduced semiconductor-to-metal transitions that occur in the bulk of inorganic semiconductors, the metallization of the ZnO surface is launched by 3-4 orders of magnitude lower photon fluxes. Using time- and angle-resolved photoelectron spectroscopy, we show that the phase transition is caused by photoinduced downward surface band bending due to photodepletion of donor-type deep surface defects. The discovered mechanism is in analogy to chemical doping of semiconductor surfaces and presents a general route for controlling surface-confined metallicity on ultrafast timescales. Photoexcitation ; Semiconductor ; Ultrashort pulse ; Materials science ; Band bending ; Doping ; Chemical physics ; Kelvin probe force microscope ; X-ray photoelectron spectroscopy ; Surface reconstruction ; Surface states ; Nanotechnology ; Optoelectronics ; Condensed matter physics ; Atomic physics ; Chemistry ; Surface (topology) ; Optics ; Physics ; Nuclear magnetic resonance ; Laser ZnO doping and properties ; Electronic and Structural Properties of Oxides ; Ga2O3 and related materials
Authors: Ni Tien | Jian‐Jr Lee | Alvin Kai-Xing Lee | Yen-Hong Lin | Jianxun Chen | Ting-You Kuo | Ming‐You Shie
Source: Cells (article, 2021-10-27)
Field: Materials Science — Biomaterials

Additive Manufacturing of Caffeic Acid-Inspired Mineral Trioxide Aggregate/Poly-ε-Caprolactone Scaffold for Regulating Vascular Induction and Osteogenic Regeneration of Dental Pulp Stem Cells Mineral trioxide aggregate (MTA) is a common biomaterial used in endodontics regeneration due to its antibacterial properties, good biocompatibility and high bioactivity. Surface modification technology allows us to endow biomaterials with the necessary biological targets for activation of specific downstream functions such as promoting angiogenesis and osteogenesis. In this study, we used caffeic acid (CA)-coated MTA/polycaprolactone (PCL) composites and fabricated 3D scaffolds to evaluate the influence on the physicochemical and biological aspects of CA-coated MTA scaffolds. As seen from the results, modification of CA does not change the original structural characteristics of MTA, thus allowing us to retain the properties of MTA. CA-coated MTA scaffolds were shown to have 25% to 55% higher results than bare scaffold. In addition, CA-coated MTA scaffolds were able to significantly adsorb more vascular endothelial growth factors (<i>p</i> < 0.05) secreted from human dental pulp stem cells (hDPSCs). More importantly, CA-coated MTA scaffolds not only promoted the adhesion and proliferation behaviors of hDPSCs, but also enhanced angiogenesis and osteogenesis. Finally, CA-coated MTA scaffolds led to enhanced subsequent in vivo bone regeneration of the femur of rabbits, which was confirmed using micro-computed tomography and histological staining. Taken together, CA can be used as a potently functional bioactive coating for various scaffolds in bone tissue engineering and other biomedical applications in the future. Mineral trioxide aggregate ; Dental pulp stem cells ; Chemistry ; Biomedical engineering ; Scaffold ; Regeneration (biology) ; Biocompatibility ; Angiogenesis ; Biomaterial ; Materials science ; Cell biology ; Dentistry ; Biochemistry ; In vitro ; Cancer research Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials ; Endodontics and Root Canal Treatments
Authors: Zainab Al‐Maqdasi | Liva Pupure | Guan Gong | Nazanin Emami | Roberts Joffe
Source: Journal of Applied Polymer Science (article, 2021-04-05)
Field: Materials Science — Polymers and Plastics

Time‐dependent properties of graphene nanoplatelets reinforced high‐density polyethylene Abstract The deformation of polymers at constant applied stress is one of their major drawbacks, limiting their use in advanced applications. The study of this property using classical techniques requires extensive testing over long periods of time. It is well known that reinforced polymers show improved behavior over time compared to their neat counterparts. In this study, the effect of adding different amounts of graphene nanoplatelets (GNPs) on the time‐dependent properties of high‐density polyethylene (HDPE) is investigated using short‐term creep tests and load/unload recovery tests. The results are discussed in terms of the test profile and the influence of loading history. Viscoplasticity/viscoelasticity analysis is performed using Zapas model and by comparing creep, creep compliance and pure viscoelasticity curves. The results show that the reinforcement of 15 wt% GNP have the most significant effect on the time‐dependent behavior, reducing the strain by more than 50%. The creep compliance curves show that nano‐reinforced HDPE behaves nonlinearly viscoelastically even at very low stresses. In addition to demonstrating the effect of nano‐reinforcement, the discussion of the results concludes that the influence of loading history can be quite significant and should not be neglected in the design and evaluation of material behavior. Creep ; High-density polyethylene ; Viscoelasticity ; Materials science ; Viscoplasticity ; Composite material ; Reinforcement ; Polyethylene ; Polymer ; Deformation (meteorology) ; Stress (linguistics) ; Constitutive equation ; Structural engineering ; Finite element method Polymer Nanocomposites and Properties ; Graphene research and applications ; Tribology and Wear Analysis
Authors: Maria Fala | Vencel Somai | Andreas Dannhorn | Grégory Hamm | Katherine Gibson | Dominique‐Laurent Couturier | Richard L. Hesketh | Alan J. Wright | Zoltán Takáts | Josephine Bunch | Simon T. Barry | Richard J. A. Goodwin | Kevin M. Brindle
Source: Magnetic Resonance in Medicine (article, 2021-01-09)
Field: Chemistry — Spectroscopy

Comparison of <sup>13</sup>C MRI of hyperpolarized [1‐<sup>13</sup>C]pyruvate and lactate with the corresponding mass spectrometry images in a murine lymphoma model The inhomogeneous distribution of labeled lactate observed in the MRS images was confirmed by MS imaging of the corresponding tumor sections. The images acquired using both techniques show that the rate of <sup>13</sup> C label exchange between the injected pyruvate and endogenous tumor lactate pool is more correlated with the rate of pyruvate delivery to the tumor cells and is less affected by the endogenous lactate concentration. Chemistry ; Nuclear medicine ; In vivo ; Nuclear magnetic resonance ; Medicine ; Physics ; Biology Advanced NMR Techniques and Applications ; Advanced MRI Techniques and Applications ; Atomic and Subatomic Physics Research
Authors: Yi Luo | Saientan Bag | Orysia Zaremba | Jacopo Andreo | Stefan Wuttke | Manuel Tsotsalas | Pascal Friederich
Source:  (preprint, 2021-08-04)
Field: Chemistry — Inorganic Chemistry

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning Despite rapid progress in the field of metal-organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: (i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, (ii) training and optimizing ML models by employing the MOF database, and (iii) predicting the synthesis conditions for new MOF structures. The ML models even at an initial stage exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey. Computer science ; Rationalization (economics) ; Field (mathematics) ; Artificial intelligence ; Machine learning ; Data mining ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Sangwon Lee | Ha-Eun Byun | Mujin Cheon | Jihan Kim | Jay H. Lee
Source: Korean Journal of Chemical Engineering (review, 2021-09-08)
Field: Materials Science — Materials Chemistry

Machine learning-based discovery of molecules, crystals, and composites: A perspective review Perspective (graphical) ; Computer science ; Property (philosophy) ; Bayesian optimization ; Artificial intelligence ; Machine learning ; Bayesian network ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Joseph W. Lowdon | Hikaru Ishikura | Malene K. Kvernenes | Manlio Caldara | Thomas J. Cleij | Bart van Grinsven | Kasper Eersels | Hanne Diliën
Source: Computation (article, 2021-09-24)
Field: Chemistry — Analytical Chemistry

Identifying Potential Machine Learning Algorithms for the Simulation of Binding Affinities to Molecularly Imprinted Polymers Molecularly imprinted polymers (MIPs) are synthetic receptors engineered towards the selective binding of a target molecule; however, the manner in which MIPs interact with other molecules is of great importance. Being able to rapidly analyze the binding of potential molecular interferences and determine the selectivity of a MIP can be a long tedious task, being time- and resource-intensive. Identifying computational models capable of reliably predicting and reporting the binding of molecular species is therefore of immense value in both a research and commercial setting. This research therefore sets focus on comparing the use of machine learning algorithms (multitask regressor, graph convolution, weave model, DAG model, and inception) to predict the binding of various molecular species to a MIP designed towards 2-methoxphenidine. To this end, each algorithm was “trained” with an experimental dataset, teaching the algorithms the structures and binding affinities of various molecular species at varying concentrations. A validation experiment was then conducted for each algorithm, comparing experimental values to predicted values and facilitating the assessment of each approach by a direct comparison of the metrics. The research culminates in the construction of binding isotherms for each species, directly comparing experimental vs. predicted values and identifying the approach that best emulates the real-world data. Affinities ; Molecularly imprinted polymer ; Binding affinities ; Computer science ; Machine learning ; Algorithm ; Artificial intelligence ; Selectivity ; Chemistry ; Stereochemistry Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications
Authors: Shaoping Xiao | Phillip Deierling | Siamak Attarian | Ahmed El Tuhami
Source: Computers & Structures (article, 2021-03-18)
Field: Materials Science — Materials Chemistry

Machine learning in multiscale modeling of spatially tailored materials with microstructure uncertainties Microscale chemistry ; Multiscale modeling ; Artificial neural network ; Machine learning ; Volume fraction ; Artificial intelligence ; Computer science ; Microstructure ; Materials science ; Uncertainty quantification ; Deformation (meteorology) ; Mechanical engineering ; Engineering ; Mathematics ; Composite material Machine Learning in Materials Science ; Composite Material Mechanics ; Advanced Electron Microscopy Techniques and Applications
Authors: Xueyao Zhou | Yaolong Zhang | Rongrong Yin | Ce Hu | Bin Jiang
Source: Chinese Journal of Chemistry (article, 2021-06-03)
Field: Materials Science — Materials Chemistry

Neural Network Representations for Studying <scp>Gas‐Surface</scp> Reaction Dynamics: Beyond the <scp>Born‐Oppenheimer</scp> Static Surface Approximation<sup>†</sup> Abstract In the past a few years, there has been significant progress in theoretical characterizations of gas‐surface reaction dynamics at the atomic level. One of the major breakthroughs is the machine learning representations of the potential energy surfaces and related properties for molecules on metal surfaces from first‐principles, particularly neural networks based methods. In this review, we focus on recent advances of the development and applications of high‐dimensional symmetry‐preserving neural network representations in gas‐surface systems, which have enabled efficient Born‐Oppenheimer molecular dynamics simulations with inclusion of all molecular and surface degrees of freedom, as well as some nonadiabatic molecular dynamics simulations with effective treatment of hot electrons, at the density function theory level. Despite these advances, further challenges remain. More accurate electronic structure theories and more efficient machine learning (and active learning) algorithms are needed towards a more quantitative description of more complex gas‐surface reactions involving multiple surfaces and adsorbates or multiple electronic states. Chemistry ; Potential energy surface ; Molecular dynamics ; Born–Oppenheimer approximation ; Surface (topology) ; Reaction dynamics ; Artificial neural network ; Molecule ; Degrees of freedom (physics and chemistry) ; Electronic structure ; Chemical physics ; Statistical physics ; Nanotechnology ; Computational chemistry ; Quantum mechanics ; Physics ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Kazuhiko Ishikiriyama
Source: Thermochimica Acta (article, 2021-12-25)
Field: Materials Science — Materials Chemistry

Polymer informatics based on the quantitative structure-property relationship using a machine-learning framework for the physical properties of polymers in the ATHAS data bank Polymer ; Physical property ; Quantitative structure–activity relationship ; Crystallinity ; Glass transition ; Artificial neural network ; Biological system ; Amorphous solid ; Materials science ; Molecular descriptor ; Heat capacity ; Mean squared error ; Melting point ; Characterization (materials science) ; Computer science ; Machine learning ; Thermodynamics ; Chemistry ; Mathematics ; Nanotechnology ; Organic chemistry ; Composite material ; Statistics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Yejian Wu | Chengyun Zhang | Ling Wang | Hongliang Duan
Source: Chemical Communications (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A graph-convolutional neural network for addressing small-scale reaction prediction We describe a graph-convolutional neural network (GCN) model, the reaction prediction capabilities of which are as potent as those of the transformer model based on sufficient data, and we adopt the Baeyer-Villiger oxidation reaction to explore their performance differences based on limited data. The top-1 accuracy of the GCN model (90.4%) is higher than that of the transformer model (58.4%). Convolutional neural network ; Computer science ; Transformer ; Graph ; Scale (ratio) ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Theoretical computer science ; Engineering ; Physics ; Voltage Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Marcos del Cueto | Alessandro Troisi
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Determining usefulness of machine learning in materials discovery using simulated research landscapes When existing experimental data are combined with machine learning (ML) to predict the performance of new materials, the data acquisition bias determines ML usefulness and the prediction accuracy. In this context, the following two conditions are highly common: (i) constructing new unbiased data sets is too expensive and the global knowledge effectively does not change by performing a limited number of novel measurements; (ii) the performance of the material depends on a limited number of physical parameters, much smaller than the range of variables that can be changed, albeit such parameters are unknown or not measurable. To determine the usefulness of ML under these conditions, we introduce the concept of simulated research landscapes, which describe how datasets of arbitrary complexity evolve over time. Simulated research landscapes allow us to use different discovery strategies to compare standard materials exploration with ML-guided explorations, i.e. we can measure quantitatively the benefit of using a specific ML model. We show that there is a window of opportunity to obtain a significant benefit from ML-guided strategies. The adoption of ML can take place too soon (not enough information to find patterns) or too late (dense datasets only allow for negligible ML benefit), and the adoption of ML can even slow down the discovery process in some cases. We offer a qualitative guide on when ML can accelerate the discovery of new best-performing materials in a field under specific conditions. The answer in each case depends on factors like data dimensionality, corrugation and data collection strategy. We consider how these factors may affect the ML prediction capabilities and discuss some general trends. Machine learning ; Computer science ; Artificial intelligence ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Lihong Han | Xingrun Chen | Qian Wang | Yingjie Chen | Mingfei Xu | Liyuan Wu | Changcheng Chen | Pengfei Lu | Pengfei Guan
Source: Computational Materials Science (article, 2021-09-03)
Field: Materials Science — Materials Chemistry

Neural network potential for studying the thermal conductivity of Sn Thermal conductivity ; Materials science ; Conductivity ; Chemistry ; Nanotechnology ; Composite material ; Physical chemistry Machine Learning in Materials Science ; Thermal properties of materials ; Thermal and Kinetic Analysis
Authors: Shuhui Guan | Zhi‐Pan Liu
Source: Chinese Journal of Chemical Physics (article, 2021-04-01)
Field: Materials Science — Materials Chemistry

Theoretical aspects on doped-zirconia for solid oxide fuel cells: From structure to conductivity Solid oxide fuel cells (SOFCs) are regarded to be a key clean energy system to convert chemical energy (e.g. H2 and O2) into electrical energy with high efficiency, low carbon footprint, and fuel flexibility. The electrolyte, typically doped zirconia, is the “state of the heart” of the fuel cell technologies, determining the performance and the operating temperature of the overall cells. Yttria stabilized zirconia (YSZ) have been widely used in SOFC due to its excellent oxide ion conductivity at high temperature. The composition and temperature dependence of the conductivity has been hotly studied in experiment and, more recently, by theoretical simulations. The characterization of the atomic structure for the mixed oxide system with different compositions is the key for elucidating the conductivity behavior, which, however, is of great challenge to both experiment and theory. This review presents recent theoretical progress on the structure and conductivity of YSZ electrolyte. We compare different theoretical methods and their results, outlining the merits and deficiencies of the methods. We highlight the recent results achieved by using stochastic surface walking global optimization with global neural network potential (SSW-NN) method, which appear to agree with available experimental data. The advent of machine-learning atomic simulation provides an affordable, efficient and accurate way to understand the complex material phenomena as encountered in solid electrolyte. The future research directions for design better electrolytes are also discussed. Electrolyte ; Oxide ; Yttria-stabilized zirconia ; Conductivity ; Materials science ; Solid oxide fuel cell ; Hydrogen fuel ; Cubic zirconia ; Energy transformation ; Nanotechnology ; Doping ; Fuel cells ; Engineering physics ; Chemical engineering ; Computer science ; Ceramic ; Chemistry ; Optoelectronics ; Composite material ; Engineering ; Physical chemistry ; Thermodynamics ; Physics ; Electrode ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electronic and Structural Properties of Oxides
Authors: Jan Weinreich | Martín Leandro Paleico | Jörg Behler
Source: The Journal of Physical Chemistry C (article, 2021-06-30)
Field: Materials Science — Materials Chemistry

Properties of α-Brass Nanoparticles II: Structure and Composition Nanoparticles have become increasingly interesting for a wide range of applications because in principle it is possible to tailor their properties by controlling size, shape, and composition. One of these applications is heterogeneous catalysis, and a fundamental understanding of the structural details of the nanoparticles is essential for any knowledge-based improvement of reactivity and selectivity. In this work, we investigate the atomic structure of brass nanoparticles containing up to 5000 atoms as a typical example for a binary alloy consisting of Cu and Zn. As systems of this size are too large for electronic structure calculations, in our simulations, we use a recently parameterized machine learning potential providing close to density functional theory accuracy. This potential is employed for a structural characterization as a function of chemical composition by various types of simulations such as Monte Carlo in the semigrand canonical ensemble and simulated annealing molecular dynamics. Our analysis reveals that the distribution of both elements in the nanoparticles is inhomogeneous, and zinc accumulates in the outermost layer, while the first subsurface layer shows an enrichment of copper. Only for high zinc concentrations, alloying can be found in the interior of the nanoparticles, and regular patterns corresponding to crystalline bulk phases of α-brass can then be observed. The surfaces of the investigated clusters exhibit well-ordered single-crystal facets, which can give rise to grain boundaries inside the clusters. The melting temperature of the nanoparticles is found to decrease with increasing zinc-atom fraction, a trend which is well known also for the bulk phase diagram of brass. Nanoparticle ; Materials science ; Molecular dynamics ; Brass ; Annealing (glass) ; Chemical physics ; Alloy ; Copper ; Nanotechnology ; Chemistry ; Computational chemistry ; Metallurgy Nanoporous metals and alloys ; nanoparticles nucleation surface interactions ; Microstructure and mechanical properties