jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonas Joos | David Van der Heggen | Lisa I. D. J. Martin | Lucia Amidani | Philippe F. Smet | Zoila Barandiarán | Luis Seijo
Source: Nature Communications (article, 2020-07-20)
Field: Materials Science — Materials Chemistry

Broadband infrared LEDs based on europium-to-terbium charge transfer luminescence Efficient broadband infrared (IR) light-emitting diodes (LEDs) are needed for emerging applications that exploit near-IR spectroscopy, ranging from hand-held electronics to medicine. Here we report broadband IR luminescence, cooperatively originating from Eu<sup>2+</sup> and Tb<sup>3+</sup> dopants in CaS. This peculiar emission overlaps with the red Eu<sup>2+</sup> emission, ranges up to 1200 nm (full-width-at-half-maximum of 195 nm) and is efficiently excited with visible light. Experimental evidence for metal-to-metal charge transfer (MMCT) luminescence is collected, comprising data from luminescence spectroscopy, microscopy and X-ray spectroscopy. State-of-the-art multiconfigurational ab initio calculations attribute the IR emission to the radiative decay of a metastable MMCT state of a Eu<sup>2+</sup>-Tb<sup>3+</sup> pair. The calculations explain why no MMCT emission is found in the similar compound SrS:Eu,Tb and are used to anticipate how to fine-tune the characteristics of the MMCT luminescence. Finally, a near-IR LED for versatile spectroscopic use is manufactured based on the MMCT emission. Luminescence ; Terbium ; Spectroscopy ; Materials science ; Excited state ; Light-emitting diode ; Infrared ; Emission spectrum ; Optoelectronics ; Dopant ; Europium ; Radiative transfer ; Atomic physics ; Optics ; Physics ; Doping ; Spectral line Luminescence Properties of Advanced Materials ; Perovskite Materials and Applications ; Lanthanide and Transition Metal Complexes
Authors: Michael A. Webb | Nicholas E. Jackson | Phwey S. Gil | Juan Pablo
Source: Science Advances (article, 2020-10-21)
Field: Materials Science — Materials Chemistry

Targeted sequence design within the coarse-grained polymer genome The chemical design of polymers with target structural and/or functional properties represents a grand challenge in materials science. While data-driven design approaches are promising, success with polymers has been limited, largely due to limitations in data availability. Here, we demonstrate the targeted sequence design of single-chain structure in polymers by combining coarse-grained modeling, machine learning, and model optimization. Nearly 2000 unique coarse-grained polymers are simulated to construct and analyze machine learning models. We find that deep neural networks inexpensively and reliably predict structural properties with limited sequence information as input. By coupling trained ML models with sequential model-based optimization, polymer sequences are proposed to exhibit globular, swollen, or rod-like behaviors, which are verified by explicit simulations. This work highlights the promising integration of coarse-grained modeling with data-driven design and represents a necessary and crucial step toward more complex polymer design efforts. Computer science ; Sequence (biology) ; Construct (python library) ; Polymer ; Deep learning ; Artificial intelligence ; Machine learning ; Materials science ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: David A. Winkler
Source: Small (review, 2020-06-15)
Field: Materials Science — Materials Chemistry

Role of Artificial Intelligence and Machine Learning in Nanosafety Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety. Context (archaeology) ; Artificial intelligence ; Automation ; Computer science ; Robotics ; Machine learning ; Risk analysis (engineering) ; Engineering ; Robot Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Biosensors and Analytical Detection
Authors: Guanjie Wang | Liyu Peng | Kaiqi Li | Linggang Zhu | Jian Zhou | Naihua Miao | Zhimei Sun
Source: Computational Materials Science (article, 2020-09-23)
Field: Materials Science — Materials Chemistry

ALKEMIE: An intelligent computational platform for accelerating materials discovery and design Computer science ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Zhennan Huang | Yonggang Yao | Zhenqian Pang | Yifei Yuan | Tangyuan Li | Kun He | Xiaobing Hu | Jian Cheng | Wentao Yao | Yuzi Liu | Anmin Nie | Soroosh Sharifi‐Asl | Meng Cheng | Boao Song | Khalil Amine | Jun Lü | Teng Li | Liangbing Hu | Reza Shahbazian‐Yassar
Source: Nature Communications (article, 2020-12-11)
Field: Materials Science — Materials Chemistry

Direct observation of the formation and stabilization of metallic nanoparticles on carbon supports Direct formation of ultra-small nanoparticles on carbon supports by rapid high temperature synthesis method offers new opportunities for scalable nanomanufacturing and the synthesis of stable multi-elemental nanoparticles. However, the underlying mechanisms affecting the dispersion and stability of nanoparticles on the supports during high temperature processing remain enigmatic. In this work, we report the observation of metallic nanoparticles formation and stabilization on carbon supports through in situ Joule heating method. We find that the formation of metallic nanoparticles is associated with the simultaneous phase transition of amorphous carbon to a highly defective turbostratic graphite (T-graphite). Molecular dynamic (MD) simulations suggest that the defective T-graphite provide numerous nucleation sites for the nanoparticles to form. Furthermore, the nanoparticles partially intercalate and take root on edge planes, leading to high binding energy on support. This interaction between nanoparticles and T-graphite substrate strengthens the anchoring and provides excellent thermal stability to the nanoparticles. These findings provide mechanistic understanding of rapid high temperature synthesis of metal nanoparticles on carbon supports and the origin of their stability. Nanoparticle ; Materials science ; Graphite ; Nucleation ; Nanotechnology ; Carbon fibers ; Thermal stability ; Amorphous carbon ; Chemical engineering ; Amorphous solid ; Crystallography ; Chemistry ; Composite material ; Composite number Graphene research and applications ; Carbon Nanotubes in Composites ; Catalytic Processes in Materials Science
Authors: Andreas C. Scheinost | J. Claußner | Jörg Exner | Manuel Feig | Stefan Findeisen | Christoph Hennig | Kristina O. Kvashnina | Damien Naudet | Damien Prieur | André Roßberg | Moritz Schmidt | Canrong Qiu | P. Colomp | Cédric Cohen | Eric Dettona | Vadim Dyadkin | Thorsten Stumpf
Source: Journal of Synchrotron Radiation (article, 2020-11-23)
Field: Chemistry — Inorganic Chemistry

ROBL-II at ESRF: a synchrotron toolbox for actinide research ROBL-II provides four different experimental stations to investigate actinide and other alpha- and beta-emitting radionuclides at the new EBS storage ring of ESRF within an energy range of 3 to 35 keV. The XAFS station consists of a highly automatized, high sample throughput installation in a glovebox, to measure EXAFS and conventional XANES of samples routinely at temperatures down to 10 K, and with a detection limit in the sub-p.p.m. range. The XES station with its five bent-crystal analyzer, Johann-type setup with Rowland circles of 1.0 and 0.5 m radii provides high-energy resolution fluorescence detection (HERFD) for XANES, XES, and RIXS measurements, covering both actinide L and M edges together with other elements accessible in the 3 to 20 keV energy range. The six-circle heavy duty goniometer of XRD-1 is equipped for both high-resolution powder diffraction as well as surface-sensitive CTR and RAXR techniques. Single crystal diffraction, powder diffraction with high temporal resolution, as well as X-ray tomography experiments can be performed at a Pilatus 2M detector stage (XRD-2). Elaborate radioprotection features enable a safe and easy exchange of samples between the four different stations to allow the combination of several methods for an unprecedented level of information on radioactive samples for both fundamental and applied actinide and environmental research. Glovebox ; XANES ; Actinide ; Materials science ; Goniometer ; Synchrotron ; Analytical Chemistry (journal) ; Synchrotron radiation ; Powder diffraction ; Diffraction ; Chemistry ; Optics ; Crystallography ; Physics ; Nuclear physics ; Nuclear chemistry ; Spectroscopy Radioactive element chemistry and processing ; X-ray Spectroscopy and Fluorescence Analysis ; Nuclear Physics and Applications
Authors: Xinrong Zong | Huamin Hu | Gang Ouyang | Jingwei Wang | Run Shi | Le Zhang | Qingsheng Zeng | Chao Zhu | Shouheng Chen | Chun Cheng | Bing Wang | Han Zhang | Zheng Liu | Wei Huang | Taihong Wang | Lin Wang | Xiaolong Chen
Source: Light Science & Applications (article, 2020-07-02)
Field: Materials Science — Materials Chemistry

Black phosphorus-based van der Waals heterostructures for mid-infrared light-emission applications Mid-infrared (MIR) light-emitting devices play a key role in optical communications, thermal imaging, and material analysis applications. Two-dimensional (2D) materials offer a promising direction for next-generation MIR devices owing to their exotic optical properties, as well as the ultimate thickness limit. More importantly, van der Waals heterostructures-combining the best of various 2D materials at an artificial atomic level-provide many new possibilities for constructing MIR light-emitting devices of large tuneability and high integration. Here, we introduce a simple but novel van der Waals heterostructure for MIR light-emission applications built from thin-film BP and transition metal dichalcogenides (TMDCs), in which BP acts as an MIR light-emission layer. For BP-WSe<sub>2</sub> heterostructures, an enhancement of ~200% in the photoluminescence intensities in the MIR region is observed, demonstrating highly efficient energy transfer in this heterostructure with type-I band alignment. For BP-MoS<sub>2</sub> heterostructures, a room temperature MIR light-emitting diode (LED) is enabled through the formation of a vertical PN heterojunction at the interface. Our work reveals that the BP-TMDC heterostructure with efficient light emission in the MIR range, either optically or electrically activated, provides a promising platform for infrared light property studies and applications. Heterojunction ; van der Waals force ; Optoelectronics ; Materials science ; Light-emitting diode ; Photoluminescence ; Infrared ; Light emission ; Diode ; Nanotechnology ; Optics ; Chemistry ; Physics ; Molecule 2D Materials and Applications ; Perovskite Materials and Applications ; MXene and MAX Phase Materials
Authors: Thong Le Ba | Omid Mahian | Somchai Wongwises | Imre Miklós Szilágyi
Source: Journal of Thermal Analysis and Calorimetry (article, 2020-01-31)
Field: Materials Science — Materials Chemistry

Review on the recent progress in the preparation and stability of graphene-based nanofluids Abstract Graphene has attracted much attention from the science world because of its mechanical, thermal, and physical properties. Graphene nanofluid is well known for its easy synthesis, longer suspension stability, higher heat conductivity, lower erosion, corrosion, larger surface area/volume ratio, and lower demand for pumping power. This article is an audit of experimental outcome about the preparation and stability of graphene-based nanofluids. Numerous researches to prepare and stabilize graphene-based nanofluids have been developed, and it is indispensable to create a complete list of the approaches. This research work outlines the advancement on preparation and assessment methods and the techniques to enhance the stability of graphene nanofluids and outlook prospects. Nanofluid ; Graphene ; Materials science ; Thermal conductivity ; Nanotechnology ; Suspension (topology) ; Thermal stability ; Work (physics) ; Chemical engineering ; Nanoparticle ; Thermodynamics ; Composite material ; Mathematics ; Engineering ; Physics Graphene research and applications ; Diamond and Carbon-based Materials Research ; High voltage insulation and dielectric phenomena
Authors: Colin A. Grambow | Lagnajit Pattanaik | William H. Green
Source: Scientific Data (article, 2020-05-08)
Field: Materials Science — Materials Chemistry

Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry Reaction times, activation energies, branching ratios, yields, and many other quantitative attributes are important for precise organic syntheses and generating detailed reaction mechanisms. Often, it would be useful to be able to classify proposed reactions as fast or slow. However, quantitative chemical reaction data, especially for atom-mapped reactions, are difficult to find in existing databases. Therefore, we used automated potential energy surface exploration to generate 12,000 organic reactions involving H, C, N, and O atoms calculated at the ωB97X-D3/def2-TZVP quantum chemistry level. We report the results of geometry optimizations and frequency calculations for reactants, products, and transition states of all reactions. Additionally, we extracted atom-mapped reaction SMILES, activation energies, and enthalpies of reaction. We believe that this data will accelerate progress in automated methods for organic synthesis and reaction mechanism generation-for example, by enabling the development of novel machine learning models for quantitative reaction prediction. Transition state ; Chemistry ; Reaction mechanism ; Elementary reaction ; Quantum chemistry ; Chemical reaction ; Branching (polymer chemistry) ; Atom (system on chip) ; Computational chemistry ; Quantum ; Reaction rate ; Organic reaction ; Potential energy surface ; Chemical physics ; Computer science ; Molecule ; Organic chemistry ; Catalysis ; Kinetics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yanfei Guan | Connor W. Coley | Haoyang Wu | Duminda S. Ranasinghe | Esther Heid | Thomas J. Struble | Lagnajit Pattanaik | William H. Green | Klavs F. Jensen
Source: Chemical Science (article, 2020-12-22)
Field: Materials Science — Materials Chemistry

Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors Accurate and rapid evaluation of whether substrates can undergo the desired the transformation is crucial and challenging for both human knowledge and computer predictions. Despite the potential of machine learning in predicting chemical reactivity such as selectivity, popular feature engineering and learning methods are either time-consuming or data-hungry. We introduce a new method that combines machine-learned reaction representation with selected quantum mechanical descriptors to predict regio-selectivity in general substitution reactions. We construct a reactivity descriptor database based on <i>ab initio</i> calculations of 130k organic molecules, and train a multi-task constrained model to calculate demanded descriptors on-the-fly. The proposed platform enhances the inter/extra-polated performance for regio-selectivity predictions and enables learning from small datasets with just hundreds of examples. Furthermore, the proposed protocol is demonstrated to be generally applicable to a diverse range of chemical spaces. For three general types of substitution reactions (aromatic C-H functionalization, aromatic C-X substitution, and other substitution reactions) curated from a commercial database, the fusion model achieves 89.7%, 96.7%, and 97.2% top-1 accuracy in predicting the major outcome, respectively, each using 5000 training reactions. Using predicted descriptors, the fusion model is end-to-end, and requires approximately only 70 ms per reaction to predict the selectivity from reaction SMILES strings. Construct (python library) ; Selectivity ; Representation (politics) ; Computer science ; Reactivity (psychology) ; Artificial intelligence ; Quantum chemical ; Machine learning ; Feature (linguistics) ; On the fly ; Transformation (genetics) ; Chemistry ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Linfeng Zhang | Mohan Chen | Xifan Wu | Handong Wang | E Weinan | Roberto Car
Source: Physical review. B./Physical review. B (article, 2020-07-22)
Field: Materials Science — Materials Chemistry

Deep neural network for the dielectric response of insulators Fully anharmonic calculations of the dielectric response of insulators require costly $a\phantom{\rule{0}{0ex}}b$ $i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o$ molecular dynamics simulations. Here, the authors show that this electronic response property can be described efficiently by a deep neural network that retains the accuracy of $a\phantom{\rule{0}{0ex}}b$ $i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o$ molecular dynamics. The scheme is demonstrated with calculations of the infrared absorption spectrum of liquid water at standard conditions, and of the evolution of the spectrum of crystalline ice undergoing a pressure-induced structural transformation from molecular ice VII to ionic ice X. Wannier function ; Ab initio ; Artificial neural network ; Dielectric ; Ab initio quantum chemistry methods ; Statistical physics ; Symmetry (geometry) ; Charge (physics) ; Condensed matter physics ; Computer science ; Computational physics ; Materials science ; Physics ; Quantum mechanics ; Artificial intelligence ; Mathematics ; Molecule Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods
Authors: Xiugang Wu | Chun‐Ying Huang | Deng‐Gao Chen | Denghui Liu | Chi-Chi Wu | Keh-Jiunh Chou | Bin Zhang | Yafei Wang | Yu Liu | Elise Y. Li | Weiguo Zhu | Pi‐Tai Chou
Source: Nature Communications (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

Exploiting racemism enhanced organic room-temperature phosphorescence to demonstrate Wallach’s rule in the lighting chiral chromophores The correlation between molecular packing structure and its room-temperature phosphorescence (RTP), hence rational promotion of the intensity, remains unclear. We herein present racemism enhanced RTP chiral chromophores by 2,2-bis-(diphenylphosphino)-1,1-napthalene (rac-BINAP) in comparison to its chiral counterparts. The result shows that rac-BINAP in crystal with denser density, consistent with a long standing Wallach's rule, exhibits deeper red RTP at 680 nm than that of the chiral counterparts. The cross packing between alternative R- and S- forms in rac-BINAP crystal significantly retards the bimolecular quenching pathway, triplet-triplet annihilation (TTA), and hence suppresses the non-radiative pathway, boosting the RTP intensity. The result extends the Wallach's rule to the fundamental difference in chiral-photophysics. In electroluminescence, rac-BINAP exhibits more balanced fluorescence versus phosphorescence intensity by comparison with that of photoluminescence, rendering a white-light emission. The result paves an avenue en route for white-light organic light emitting diodes via full exploitation of intrinsic fluorescence and phosphorescence. Phosphorescence ; Chromophore ; Photochemistry ; Fluorescence ; Photoluminescence ; Electroluminescence ; Materials science ; Chemistry ; Optoelectronics ; Optics ; Physics ; Nanotechnology Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Organic Light-Emitting Diodes Research
Authors: Ahmed I. Osman | Charlie Farrell | Ala’a H. Al‐Muhtaseb | John Harrison | David W. Rooney
Source: Scientific Reports (article, 2020-02-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The production and application of carbon nanomaterials from high alkali silicate herbaceous biomass Herein, value-added materials such as activated carbon and carbon nanotubes were synthesized from low-value Miscanthus × giganteus lignocellulosic biomass. A significant drawback of using Miscanthus in an energy application is the melting during the combustion due to its high alkali silicate content. An application of an alternative approach was proposed herein for synthesis of activated carbon from Miscanthus × giganteus, where the produced activated carbon possessed a high surface area and pore volume of 0.92 cm<sup>3</sup>.g<sup>-1</sup> after two activation steps using phosphoric acid and potassium hydroxide. The S<sub>BET</sub> of the raw biomass, after first activation and second activation methods showed 17, 1142 and 1368 m<sup>2</sup>.g<sup>-1</sup>, respectively. Transforming this otherwise waste material into a useful product where its material properties can be utilized is an example of promoting the circular economy by valorising waste lignocellulosic biomass to widely sought-after high surface area activated carbon and subsequently, unconventional multi-walled carbon nanotubes. This was achieved when the activated carbon produced was mixed with nitrogen-based material and iron precursor, where it produced hydrophilic multi-wall carbon nanotubes with a contact angle of θ = 9.88°, compared to the raw biomass. synthesised materials were tested in heavy metal removal tests using a lead solution, where the maximum lead absorption was observed for sample AC-K, with a 90% removal capacity after the first hour of testing. The synthesis of these up-cycled materials can have potential opportunities in the areas of wastewater treatment or other activated carbon/carbon nanotube end uses with a rapid cycle time. Miscanthus ; Activated carbon ; Potassium hydroxide ; Biomass (ecology) ; Materials science ; Lignocellulosic biomass ; Carbon fibers ; Miscanthus sinensis ; Raw material ; BET theory ; Specific surface area ; Chemical engineering ; Pulp and paper industry ; Chemistry ; Waste management ; Lignin ; Bioenergy ; Biofuel ; Organic chemistry ; Composite material ; Catalysis ; Adsorption ; Composite number ; Agronomy Supercapacitor Materials and Fabrication ; Catalysis for Biomass Conversion ; Aerogels and thermal insulation