jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christina M. Legendre | Emil Damgaard‐Møller | Jacob Overgaard | Dietmar Stalke
Source: European Journal of Inorganic Chemistry (article, 2021-06-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Quest for Optimal 3 d Orbital Splitting in Tetrahedral Cobalt Single‐Molecule Magnets Featuring Colossal Anisotropy and Hysteresis Abstract Only a few four‐coordinated Co 2+ ‐complexes show single‐molecule magnet (SMM) properties without an applied dc field. Common for those is a large magnetic anisotropy generated by the close proximity of the and orbitals. This type of magnetic anisotropy is strongly correlated with the extent of structural distortion from ideal tetrahedral towards linear coordination. Quantification of the governing magneto‐structural correlations is hence crucial for the development of better SMMs. For this purpose, we synthesized and analyzed four significantly distorted tetrahedral cobalt complexes that exhibit extremely large magnetic anisotropies and slow relaxation of magnetization in zero field. The N−Co−N bite angles vary from 70.8 to 72.7°, and magnetic measurements show strong anisotropy with D ‐values in the range from 75 to 114 cm −1 . Ab initio calculations supported the experimental results and highlighted a significant increase of D up to 141 cm −1 , correlated with the reduction of the energy difference between the and orbitals. Based on these magneto‐structural correlations and in contrast to the current assumption that the smaller bite angle is the better, we predict that with an ideal bite angle in the range from 76–78° in distorted Co(N 2 R) 2 complexes, the energy difference between the two important d‐orbitals is at a minimum and hence the magnetic anisotropy is maximized. Chemistry ; Tetrahedron ; Magnet ; Anisotropy ; Cobalt ; Hysteresis ; Condensed matter physics ; Crystallography ; Physics ; Inorganic chemistry ; Quantum mechanics Magnetism in coordination complexes ; Advanced NMR Techniques and Applications ; Lanthanide and Transition Metal Complexes
Authors: Suttinee Phuagkhaopong | Luís Mendes | K. H. Müller | Manja Wobus | Martin Bornhäuser | H Carswell | Iola F. Duarte | F. Philipp Seib
Source: ACS Applied Materials & Interfaces (article, 2021-06-25)
Field: Materials Science — Biomaterials

Silk Hydrogel Substrate Stress Relaxation Primes Mesenchymal Stem Cell Behavior in 2D Tissue-mimetic silk hydrogels are being explored for diverse healthcare applications, including stem cell delivery. However, the impact of stress relaxation of silk hydrogels on human mesenchymal stem cell (MSC) biology is poorly defined. The aim of this study was to fabricate silk hydrogels with tuned mechanical properties that allowed the regulation of MSC biology in two dimensions. The silk content and stiffness of both elastic and viscoelastic silk hydrogels were kept constant to permit direct comparisons. Gene expression of <i>IL-1β</i>, <i>IL-6</i>, <i>LIF</i>, <i>BMP-6</i>, <i>BMP-7</i>, and <i>protein tyrosine phosphatase receptor type C</i> were substantially higher in MSCs cultured on elastic hydrogels than those on viscoelastic hydrogels, whereas this pattern was reversed for <i>insulin</i>, <i>HNF-1A</i>, and <i>SOX-2</i>. Protein expression was also mechanosensitive and the elastic cultures showed strong activation of <i>IL-1β</i> signaling in response to hydrogel mechanics. An elastic substrate also induced higher consumption of glucose and aspartate, coupled with a higher secretion of lactate, than was observed in MSCs grown on viscoelastic substrate. However, both silk hydrogels changed the magnitude of consumption of glucose, pyruvate, glutamine, and aspartate, and also metabolite secretion, resulting in an overall lower metabolic activity than that found in control cells. Together, these findings describe how stress relaxation impacts the overall biology of MSCs cultured on silk hydrogels. Materials science ; Mesenchymal stem cell ; Stress relaxation ; Substrate (aquarium) ; Self-healing hydrogels ; SILK ; Nanotechnology ; Relaxation (psychology) ; Stress (linguistics) ; Biophysics ; Composite material ; Chemical engineering ; Polymer chemistry ; Cell biology ; Neuroscience ; Creep ; Biology Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; RNA Interference and Gene Delivery
Authors: Merve İzmir | Ashma Sharma | Sushant Shendre | Emek G. Durmusoglu | Vijay Kumar Sharma | Farzan Shabani | Hamed Dehghanpour Baruj | Savas Delikanli | Manoj Sharma | Hilmi Volkan Demir
Source: ACS Applied Nano Materials (article, 2021-12-28)
Field: Materials Science — Materials Chemistry

Blue-Emitting CdSe Nanoplatelets Enabled by Sulfur-Alloyed Heterostructures for Light-Emitting Diodes with Low Turn-on Voltage Colloidal nanoplatelets (NPLs) have emerged as the last class of semiconductor nanocrystals for their potential optoelectronic applications. The heterostructures of these nanocrystals can achieve high photoluminescence quantum yield and enhanced photostability, along with color purity. Such advantages make them a promising candidate for solution-processable light-emitting diodes (LEDs). However, to date, blue-emitting CdSe nanoplatelets (NPLs) exhibit poor photoluminescence quantum yield and also typically suffer from a rolled-up morphology. To mitigate these problems in this work, we propose and demonstrate efficient alloyed 4 ML CdSe1–xSx nanoplatelets having a CdS crown with enhanced photoluminescence quantum yields (up to 60%) in the blue region (462–487 nm). We successfully used these NPLs as an electrically driven active emitter in the blue-emitting NPL-LEDs with a low turn-on voltage of ∼4 V. The Commission Internationale de L’Eclairage (CIE) coordinates of (0.23, 0.14) were obtained for these blue-emitting NPL-LEDs. These emitters could potentially open up the opportunity for full-color displays using these NPL-based blue LEDs in conjunction with the red and green ones. Heterojunction ; Optoelectronics ; Materials science ; Light-emitting diode ; Diode ; Sulfur ; Turn (biochemistry) ; Voltage ; Blue light ; Nanotechnology ; Chemistry ; Electrical engineering ; Metallurgy ; Engineering Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Marc Vila | Chuang‐Han Hsu | José H. García | L. Antonio Benítez | Xavier Waintal | Sergio O. Valenzuela | Vitor M. Pereira | Stephan Roche
Source: Physical Review Research (article, 2021-12-30)
Field: Materials Science — Materials Chemistry

Low-symmetry topological materials for large charge-to-spin interconversion: The case of transition metal dichalcogenide monolayers The spin polarization induced by the spin Hall effect (SHE) in thin films typically points out of the plane. This is rooted on the specific symmetries of traditionally studied systems, not in a fundamental constraint. Recently, experiments on few-layer ${\mathrm{MoTe}}_{2}$ and ${\mathrm{WTe}}_{2}$ showed that the reduced symmetry of these strong spin-orbit coupling materials enables a new form of canted spin Hall effect, characterized by concurrent in-plane and out-of-plane spin polarizations. Here, through quantum transport calculations on realistic device geometries, including disorder, we predict a very large gate-tunable SHE figure of merit ${\ensuremath{\lambda}}_{s}{\ensuremath{\theta}}_{xy}\ensuremath{\approx}1--50$ nm in ${\mathrm{MoTe}}_{2}$ and ${\mathrm{WTe}}_{2}$ monolayers that significantly exceeds values of conventional SHE materials. This stems from a concurrent long spin diffusion length (${\ensuremath{\lambda}}_{s}$) and charge-to-spin interconversion efficiency as large as ${\ensuremath{\theta}}_{xy}\ensuremath{\approx}80$%, originating from momentum-invariant (persistent) spin textures together with large spin Berry curvature along the Fermi contour, respectively. Generalization to other materials and specific guidelines for unambiguous experimental confirmation are proposed, paving the way toward exploiting such phenomena in spintronic devices. These findings vividly emphasize how crystal symmetry and electronic topology can govern the intrinsic SHE and spin relaxation, and how they may be exploited to broaden the range and efficiency of spintronic materials and functionalities. Condensed matter physics ; Spintronics ; Spin (aerodynamics) ; Physics ; Spin Hall effect ; Topology (electrical circuits) ; Topological insulator ; Spin polarization ; Spin diffusion ; Quantum mechanics ; Materials science ; Electron ; Mathematics 2D Materials and Applications ; Topological Materials and Phenomena ; Advanced Memory and Neural Computing
Authors: Yuan Yang | Yao Lin | Yurong Deng | Xiandeng Hou | Lu Yang | Chengbin Zheng
Source: Microchimica Acta (article, 2021-04-23)
Field: Chemistry — Analytical Chemistry

In-site and solvent-free exfoliation of porous graphene oxide from pencil lead fiber for solid-phase microextraction of cadmium ion before GF-AAS determination Solid-phase microextraction ; Graphene ; Cadmium ; Solvent ; Materials science ; Oxide ; Exfoliation joint ; Pencil (optics) ; Ion ; Inorganic chemistry ; Chemistry ; Chromatography ; Nanotechnology ; Organic chemistry ; Gas chromatography–mass spectrometry ; Metallurgy ; Optics ; Mass spectrometry Analytical chemistry methods development ; Electron and X-Ray Spectroscopy Techniques ; Advanced biosensing and bioanalysis techniques
Authors: Oyawale Adetunji Moses | Wei Chen | Mukhtar Lawan Adam | Zhuo Wang | Kaili Liu | Junming Shao | Zhengsheng Li | Wentao Li | Chensu Wang | Haitao Zhao | Cheng Heng Pang | Zongyou Yin | Xue‐Feng Yu
Source: Materials Reports Energy (article, 2021-07-05)
Field: Materials Science — Materials Chemistry

Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions Technological advancements in recent decades have greatly transformed the field of material chemistry. Juxtaposing the accentuating energy demand with the pollution associated, urgent measures are required to ensure energy maximization, while reducing the extended experimental time cycle involved in energy production. In lieu of this, the prominence of catalysts in chemical reactions, particularly energy related reactions cannot be undermined, and thus it is critical to discover and design catalyst, towards the optimization of chemical processes and generation of sustainable energy. Most recently, artificial intelligence (AI) has been incorporated into several fields, particularly in advancing catalytic processes. The integration of intensive data set, machine learning models and robotics, provides a very powerful tool in modifying material synthesis and optimization by generating multifarious dataset amenable with machine learning techniques. The employment of robots automates the process of dataset and machine learning models integration in screening intermetallic surfaces of catalyst, with extreme accuracy and swiftness comparable to a number of human researchers. Although, the utilization of robots in catalyst discovery is still in its infancy, in this review we summarize current sway of artificial intelligence in catalyst discovery, briefly describe the application of databases, machine learning models and robots in this field, with emphasis on the consolidation of these monomeric units into a tripartite flow process. We point out current trends of machine learning and hybrid models of first principle calculations (DFT) for generating dataset, which is integrable into autonomous flow process of catalyst discovery. Also, we discuss catalyst discovery for renewable energy related reactions using this tripartite flow process with predetermined descriptors. Artificial intelligence ; Machine learning ; Computer science ; Robot ; Robotics Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Electrocatalysts for Energy Conversion
Authors: Bowen Gong | Kun Yang | J. Lian | Jianwei Wang
Source: Computational Materials Science (article, 2021-08-29)
Field: Materials Science — Materials Chemistry

Machine learning-enabled prediction of chemical durability of A2B2O7 pyrochlore and fluorite Durability ; Ionic radius ; Electronegativity ; Actinide ; Fluorite ; Materials science ; Ionic bonding ; Leaching (pedology) ; Pyrochlore ; Machine learning ; Computer science ; Chemistry ; Ion ; Metallurgy ; Composite material ; Environmental science ; Nuclear chemistry Nuclear materials and radiation effects ; Radioactive element chemistry and processing ; Nuclear Materials and Properties
Authors: Eric Qu | Andrew M. Jimenez | Sanat K. Kumar | Kai Zhang
Source: Macromolecules (article, 2021-04-02)
Field: Materials Science — Surfaces, Coatings and Films

Quantifying Nanoparticle Assembly States in a Polymer Matrix through Deep Learning There is great interest in controlling the spatial dispersion of inorganic nanoparticles (NPs) in an organic polymer matrix because this centrally underpins the property enhancements obtained from these hybrid materials. Currently, qualitative information on NP spatial distribution is obtained by visual inspection of transmission electron microscopy (TEM) images. Quantitative information is only indirectly obtained through the use of scattering probes such as small-angle X-ray/neutron scattering (SAXS/SANS). While the main challenge, that scattering probes operate in reciprocal space, can be remedied by Fourier inverting the data into real space, a much harder issue deconvolves the contribution of the particle form factor (which is affected by the details of the NP size and shape) from the structure factor that contains information on NP spatial distribution. These problems become acute when we deal with the popular topic of NPs grafted with polymer chains because the polymeric corona and hence the particle form factor becomes context-dependent and is hard to quantify. To make progress, we develop and apply a deep-learning-based image analysis method to quantify the distribution of spherical NPs in a polymer matrix directly from their real-space TEM images. A dataset of NP detection (DOPAD) is built by manually labeling particle positions on experimental TEM images of diverse polymer composite systems. A convolutional neural network object detection model is then trained on DOPAD. Together with sliding-window and merging algorithms, an automated pipeline is established, which takes a large TEM image as input and extracts NP locations and sizes. We validate the structural information resulting from this method against SAXS-derived structural information for NPs ordered by polymer crystallization and then use it to distinguish between different states of the assembly of polymer-grafted NPs in a polymer matrix achieved by using their surfactancy. We show that this data-rich protocol allows us to draw critical facets of experimental behavior which have previously not been accessible. The DOPAD dataset, Python source code, and trained model are shared on GitHub at https://dopad.github.io. Scattering ; Materials science ; Deep learning ; Computer science ; Context (archaeology) ; Polymer ; Matrix (chemical analysis) ; Nanoparticle ; Convolutional neural network ; Small-angle X-ray scattering ; Artificial intelligence ; Biological system ; Nanotechnology ; Optics ; Physics ; Composite material Electron and X-Ray Spectroscopy Techniques ; Enhanced Oil Recovery Techniques ; Machine Learning in Materials Science
Authors: Babak Valipour Goodarzi | Ahmad Reza Bahramian
Source: Journal of Thermal Analysis and Calorimetry (article, 2021-07-04)
Field: Chemistry — Spectroscopy

Applying machine learning for predicting thermal conductivity coefficient of polymeric aerogels Aerogel ; Thermal conductivity ; Materials science ; Machine learning ; Thermal ; Thermal insulation ; Thermal conduction ; Artificial intelligence ; Composite material ; Computer science ; Thermodynamics ; Physics Aerogels and thermal insulation ; Gas Sensing Nanomaterials and Sensors ; Catalytic Processes in Materials Science
Authors: Yong Yi | Liming Wang | Zhengying Chen
Source: Renewable Energy (article, 2021-05-15)
Field: Materials Science — Materials Chemistry

Adaptive global kernel interval SVR-based machine learning for accelerated dielectric constant prediction of polymer-based dielectric energy storage Dielectric ; Interval (graph theory) ; Kernel (algebra) ; Energy storage ; Polymer ; Materials science ; Energy (signal processing) ; Constant (computer programming) ; Computer science ; Artificial intelligence ; Mathematics ; Composite material ; Thermodynamics ; Statistics ; Physics ; Optoelectronics ; Discrete mathematics ; Combinatorics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Fuel Cells and Related Materials
Authors: Amit Gupta | Sabyasachi Chakraborty | Raghunathan Ramakrishnan
Source: Machine Learning Science and Technology (article, 2021-02-05)
Field: Materials Science — Materials Chemistry

Revving up <sup>13</sup>C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules Abstract The requirement for accelerated and quantitatively accurate screening of nuclear magnetic resonance spectra across the small molecules chemical compound space is two-fold: (1) a robust ‘local’ machine learning (ML) strategy capturing the effect of the neighborhood on an atom’s ‘near-sighted’ property—chemical shielding; (2) an accurate reference dataset generated with a state-of-the-art first-principles method for training. Herein we report the QM9-NMR dataset comprising isotropic shielding of over 0.8 million C atoms in 134k molecules of the QM9 dataset in gas and five common solvent phases. Using these data for training, we present benchmark results for the prediction transferability of kernel-ridge regression models with popular local descriptors. Our best model, trained on 100k samples, accurately predicts isotropic shielding of 50k ‘hold-out’ atoms with a mean error of less than 1.9 ppm. For the rapid prediction of new query molecules, the models were trained on geometries from an inexpensive theory. Furthermore, by using a Δ-ML strategy, we quench the error below 1.4 ppm. Finally, we test the transferability on non-trivial benchmark sets that include benchmark molecules comprising 10–17 heavy atoms and drugs. Kernel (algebra) ; Benchmark (surveying) ; Isotropy ; Molecule ; Electromagnetic shielding ; Chemical space ; Chemical shift ; Atom (system on chip) ; Transferability ; Artificial intelligence ; Computer science ; Machine learning ; Algorithm ; Molecular physics ; Chemistry ; Computational chemistry ; Physics ; Nuclear magnetic resonance ; Mathematics ; Discrete mathematics ; Quantum mechanics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Elizabeth Lee | Thomas Ludwig | Boyuan Yu | Aayush R. Singh | François Gygi | Jens K. Nørskov | Juan Pablo
Source: The Journal of Physical Chemistry Letters (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium In heterogeneous catalysis, free energy profiles of reactions govern the mechanisms, rates, and equilibria. Energetics are conventionally computed using the harmonic approximation (HA), which requires determination of critical states <i>a priori.</i> Here, we use neural networks to efficiently sample and directly calculate the free energy surface (FES) of a prototypical heterogeneous catalysis reaction-the dissociation of molecular nitrogen on ruthenium-at density-functional-theory-level accuracy. We find that the vibrational entropy of surface atoms, often neglected in HA for transition metal catalysts, contributes significantly to the reaction barrier. The minimum free energy path for dissociation reveals an "on-top" adsorbed molecular state prior to the transition state. While a previously reported flat-lying molecular metastable state can be identified in the potential energy surface, it is absent in the FES at relevant reaction temperatures. These findings demonstrate the importance of identifying critical points self-consistently on the FES for reactions that involve considerable entropic effects. Metastability ; Catalysis ; Dissociation (chemistry) ; Ruthenium ; Potential energy surface ; Chemical physics ; Chemistry ; Energy landscape ; Transition state ; Density functional theory ; Potential energy ; Thermodynamics ; Computational chemistry ; Molecule ; Atomic physics ; Physical chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: Zhantao Chen | Nina Andrejevic | Tess Smidt | Zhiwei Ding | Qian Xu | Chi, Yen-Ting | Quynh T. Thanh Nguyen | Ahmet Alatas | Jing Kong | Mingda Li
Source:  (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Direct Prediction of Phonon Density of States With Euclidean Neural Networks. Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density-of-states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high-quality prediction using a small training set of ≈103 examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high-performance thermal storage materials and phonon-mediated&amp;nbsp;superconductors. Phonon ; Artificial neural network ; Euclidean distance ; Euclidean geometry ; Statistical physics ; Condensed matter physics ; Materials science ; Physics ; Computer science ; Mathematics ; Artificial intelligence ; Geometry Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Thermal properties of materials