jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yong-Jin Kim | Chan‐Ho Yang
Source: NPG Asia Materials (article, 2020-01-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electret formation in transition metal oxides by electrochemical amorphization Abstract Transition metal oxides (TMOs) are an important class of materials that show a wide range of functionalities involving spin, charge, and lattice degrees of freedom. The strong correlation between electrons in d -orbitals and the multivalence nature give rise to a variety of exotic electronic states ranging from insulator to superconductor and cause intriguing phase competition phenomena. Despite a burst of research on the multifarious functionalities in TMOs, little attention has been paid to the formation and integration of an electret—a type of quasi-permanent electric field generator useful for nanoscale functional devices as an electric counterpart to permanent magnets. Here, we find that an electret can be created in LaMnO 3 thin films by tip-induced electric fields, with a considerable surface height change, via solid-state electrochemical amorphization. The surface charge density of the formed electret area reaches ~400 nC cm −2 and persists without significant charge reduction for more than a year. The temporal evolution of the surface height, charge density, and electric potential are systematically examined by scanning probe microscopy. The underlying mechanism is theoretically analyzed based on a drift-diffusion-reaction model, suggesting that positively charged particles, which are likely protons produced by the dissociation of water, play crucial roles as trapped charges and a catalysis to trigger amorphization. Our finding opens a new horizon for multifunctional TMOs. Electret ; Materials science ; Chemical physics ; Electric field ; Phase transition ; Charge density ; Electric charge ; Nanotechnology ; Surface charge ; Condensed matter physics ; Physical chemistry ; Composite material ; Chemistry ; Physics Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides ; Multiferroics and related materials
Authors: Xiao Liu | Shuangfeng Jia | Ming Yang | Yuanting Tang | Yanwei Wen | Shengqi Chu | Jianbo Wang | Bin Shan | Rong Chen
Source: Nature Communications (article, 2020-08-25)
Field: Materials Science — Materials Chemistry

Activation of subnanometric Pt on Cu-modified CeO2 via redox-coupled atomic layer deposition for CO oxidation Improving the low-temperature activity (below 100 °C) and noble-metal efficiency of automotive exhaust catalysts has been a continuous effort to eliminate cold-start emissions, yet great challenges remain. Here we report a strategy to activate the low-temperature performance of Pt catalysts on Cu-modified CeO<sub>2</sub> supports based on redox-coupled atomic layer deposition. The interfacial reducibility and structure of composite catalysts have been precisely tuned by oxide doping and accurate control of Pt size. Cu-modified CeO<sub>2</sub>-supported Pt sub-nanoclusters demonstrate a remarkable performance with an onset of CO oxidation reactivity below room temperature, which is one order of magnitude more active than atomically-dispersed Pt catalysts. The Cu-O-Ce site with activated lattice oxygen anchors deposited Pt sub-nanoclusters, leading to a moderate CO adsorption strength at the interface that facilitates the low-temperature CO oxidation performance. Nanoclusters ; Catalysis ; Redox ; Materials science ; Noble metal ; Atomic layer deposition ; Oxide ; Adsorption ; Chemical engineering ; Deposition (geology) ; Metal ; Layer (electronics) ; Inorganic chemistry ; Nanotechnology ; Chemistry ; Physical chemistry ; Metallurgy Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Nathan Cowieson | Charlotte J. C. Edwards‐Gayle | Katsuaki Inoue | Nikul Khunti | James Doutch | Eugene Williams | Steven Daniels | G. Preece | N A Krumpa | John P. Sutter | Mark D. Tully | Nicholas J. Terrill | Robert P. Rambo
Source: Journal of Synchrotron Radiation (article, 2020-08-18)
Field: Materials Science — Materials Chemistry

Beamline B21: high-throughput small-angle X-ray scattering at Diamond Light Source B21 is a small-angle X-ray scattering (SAXS) beamline with a bending magnet source in the 3 GeV storage ring at the Diamond Light Source Ltd synchrotron in the UK. The beamline utilizes a double multi-layer monochromator and a toroidal focusing optic to deliver 2 × 10<sup>12</sup> photons per second to a 34 × 40 µm (FWHM) focal spot at the in-vacuum Eiger 4M (Dectris) detector. A high-performance liquid chromatography system and a liquid-handling robot make it possible to load solution samples into a temperature-controlled in-vacuum sample cell with a high level of automation. Alternatively, a range of viscous or solid materials may be loaded manually using a range of custom sample cells. A default scattering vector range from 0.0026 to 0.34 Å<sup>-1</sup> and low instrument background make B21 convenient for measuring a wide range of biological macromolecules. The beamline has run a full user programme since 2013. Beamline ; Optics ; Monochromator ; Storage ring ; Synchrotron ; Diamond ; Scattering ; Detector ; Small-angle scattering ; Small-angle X-ray scattering ; Materials science ; Range (aeronautics) ; Photon ; Physics ; Beam (structure) Enzyme Structure and Function ; Advanced X-ray Imaging Techniques ; Biochemical and Molecular Research
Authors: Mortazavi, Bohayra | Novikov, Ivan S. | Podryabinkin, Evgeny V. | Roche, Stephan | Rabczuk, Timon | Shapeev, Alexander V. | Zhuang, Xiaoying
Source: LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials Phononic properties are commonly studied by calculating force constants using the density functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of phonon dispersion relations or thermal properties, but for low-symmetry and nanoporous structures the computational cost quickly becomes very demanding. Moreover, the computational setups may yield nonphysical imaginary frequencies in the phonon dispersion curves, impeding the assessment of phononic properties and the dynamical stability of the considered system. Here, we compute phonon dispersion relations and examine the dynamical stability of a large ensemble of novel materials and compositions. We propose a fast and convenient alternative to DFT simulations which derived from machine-learning interatomic potentials passively trained over computationally efficient ab-initio molecular dynamics trajectories. Our results for diverse two-dimensional (2D) nanomaterials confirm that the proposed computational strategy can reproduce fundamental thermal properties in close agreement with those obtained via the DFT approach. The presented method offers a stable, efficient, and convenient solution for the examination of dynamical stability and exploring the phononic properties of low-symmetry and porous 2D materials. Phonon ; Nanoporous ; Density functional theory ; Stability (learning theory) ; Dispersion (optics) ; Statistical physics ; Materials science ; Ab initio ; Symmetry (geometry) ; Physics ; Computer science ; Quantum mechanics ; Nanotechnology ; Mathematics ; Machine learning ; Geometry Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Sina Stocker | Gábor Cśanyi | Karsten Reuter | Johannes T. Margraf
Source: Nature Communications (article, 2020-10-30)
Field: Materials Science — Materials Chemistry

Machine learning in chemical reaction space Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 10<sup>60</sup> molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for 'reactive' ML, we establish a first-principles database (Rad-6) containing closed and open-shell organic molecules, along with an associated database of chemical reaction energies (Rad-6-RE). We show that the special topology of reaction spaces, with central hub molecules involved in multiple reactions, requires a modification of existing compound space ML-concepts. Showcased by the application to methane combustion, we demonstrate that the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses. Chemical space ; Space (punctuation) ; Computer science ; Data science ; Biology ; Bioinformatics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: O. Anatole von Lilienfeld | Kieron Burke
Source: Nature Communications (article, 2020-09-29)
Field: Materials Science — Materials Chemistry

Retrospective on a decade of machine learning for chemical discovery Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order. Chemical space ; Computer science ; Field (mathematics) ; Data science ; Drug discovery ; Artificial intelligence ; Machine learning ; Bioinformatics ; Biology ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Zibo Pei | Dawei Zhang | Yuanjie Zhi | Tao Yang | Lulu Jin | Dongmei Fu | Xuequn Cheng | Herman Terryn | J.M.C. Mol | Xiaogang Li
Source: Corrosion Science (article, 2020-04-29)
Field: Materials Science — Materials Chemistry

Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning The atmospheric corrosion of carbon steel was monitored by a Fe/Cu type galvanic corrosion sensor for 34 days. Using a random forest (RF)-based machine learning approach, the impacts of relative humidity, temperature and rainfall were identified to be higher than those of airborne particles, sulfur dioxide, nitrogen dioxide, carbon monoxide and ozone on the initial atmospheric corrosion. The RF model demonstrated higher accuracy than artificial neural network (ANN) and support vector regression (SVR) models in predicting instantaneous atmospheric corrosion. The model accuracy can be further improved after taking into consideration of the significant effect of rust formation on the sensor. Corrosion ; Ozone ; Relative humidity ; Materials science ; Humidity ; Carbon dioxide ; Support vector machine ; Random forest ; Wind speed ; Galvanic cell ; Galvanic corrosion ; Environmental science ; Metallurgy ; Meteorology ; Machine learning ; Chemistry ; Computer science Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Concrete Corrosion and Durability
Authors: Alexandre Tkatchenko
Source: Nature Communications (article, 2020-08-17)
Field: Materials Science — Materials Chemistry

Machine learning for chemical discovery Discovering chemicals with desired attributes is a long and painstaking process. Curated datasets containing reliable quantum-mechanical properties for millions of molecules are becoming increasingly available. The development of novel machine learning tools to obtain chemical knowledge from these datasets has the potential to revolutionize the process of chemical discovery. Here, I comment on recent breakthroughs in this emerging field and discuss the challenges for the years to come. Computer science ; Data science ; Process (computing) ; Field (mathematics) ; Quantum chemical ; Drug discovery ; Nanotechnology ; Artificial intelligence ; Bioinformatics ; Chemistry ; Biology ; Molecule ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Maituo Yu | Shuyang Yang | Chunzhi Wu | Noa Marom
Source: npj Computational Materials (article, 2020-11-27)
Field: Materials Science — Materials Chemistry

Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization Abstract Within density functional theory (DFT), adding a Hubbard U correction can mitigate some of the deficiencies of local and semi-local exchange-correlation functionals, while maintaining computational efficiency. However, the accuracy of DFT+U largely depends on the chosen Hubbard U values. We propose an approach to determining the optimal U parameters for a given material by machine learning. The Bayesian optimization (BO) algorithm is used with an objective function formulated to reproduce the band structures produced by more accurate hybrid functionals. This approach is demonstrated for transition metal oxides, europium chalcogenides, and narrow-gap semiconductors. The band structures obtained using the BO U values are in agreement with hybrid functional results. Additionally, comparison to the linear response (LR) approach to determining U demonstrates that the BO method is superior. Bayesian optimization ; Hubbard model ; Density functional theory ; Hybrid functional ; Band gap ; Bayesian probability ; Function (biology) ; Algorithm ; Computer science ; Statistical physics ; Physics ; Mathematics ; Materials science ; Artificial intelligence ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Semiconductor materials and interfaces ; Inorganic Chemistry and Materials
Authors: Ioannis Papakonstantinou | Mark Portnoi | Michael G. Debije
Source: Advanced Energy Materials (article, 2020-11-27)
Field: Chemistry — Physical and Theoretical Chemistry

The Hidden Potential of Luminescent Solar Concentrators Abstract The luminescent solar concentrator (LSC), originally introduced almost four decades ago as a potential alternative/complement to silicon solar cells, has since evolved to a versatile photovoltaic (PV) solution with realistic potential for seamless integration into the urban architectural landscape. Yet, a popular perception of the device still persists: the LSC is mostly seen as just a low‐efficiency solar panel. This review challenges this outdated notion and argues that the LSC is, to the contrary, a powerful and highly adaptive photonic platform with many more capabilities and potential than only generating electricity from sunlight. The field has seen a rapidly expanding application portfolio over the last few years, with LSCs now considered in various sensing applications, “smart” windows, chemical reactors, horticulture, and even in optical communication and real‐time responsive systems. The main goal of this work is to shed light onto this alternative application space and highlight the LSC's unique spectral manipulation, light distribution, and light concentration properties, and as a result, to encourage the participation from a broader range of disciplines into LSC research with the ultimate aim of stimulating the development of novel, LSC inspired technologies. Photovoltaic system ; Materials science ; Nanotechnology ; Engineering physics ; Portfolio ; Systems engineering ; Photovoltaics ; Computer science ; Electricity ; Architectural engineering ; Electrical engineering ; Engineering ; Business Photochemistry and Electron Transfer Studies ; Spectroscopy and Quantum Chemical Studies ; Organic Light-Emitting Diodes Research
Authors: Tao Liang | Shabnam B. Goudari | Changle Chen
Source: Nature Communications (article, 2020-01-17)
Field: Chemistry — Organic Chemistry

A simple and versatile nickel platform for the generation of branched high molecular weight polyolefins The development of high-performance transition metal catalysts has long been a major driving force in academic and industrial polyolefin research. Late transition metal-based olefin polymerization catalysts possess many unique properties, such as the ability to generate variously branched polyolefins using only ethylene as the feedstock and the capability of incorporating polar functionalized comonomers without protecting agents. Here we report the synthesis and (co)polymerization studies of a simple but extremely versatile α-imino-ketone nickel system. This type of catalyst is easy to synthesize and modify, and it is thermally stable and highly active during ethylene polymerization without the addition of any cocatalysts. Despite the sterically open nature, these catalysts can generate branched Ultra-High-Molecular-Weight polyethylene and copolymerize ethylene with a series of polar comonomers. The versatility of this platform has been further demonstrated through the synthesis of a dinuclear nickel catalyst and the installation of an anchor for catalyst heterogenization. Polyolefin ; Catalysis ; Polymerization ; Materials science ; Polyethylene ; Ethylene ; Nickel ; Olefin fiber ; Transition metal ; Polymer chemistry ; Organic chemistry ; Chemical engineering ; Chemistry ; Nanotechnology ; Polymer ; Composite material Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis ; Synthetic Organic Chemistry Methods
Authors: Pascal Friederich | Gabriel dos Passos Gomes | Riccardo De Bin | Alán Aspuru‐Guzik | David Balcells
Source: Chemical Science (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as water splitting and CO<sub>2</sub> reduction. The key steps underlying homogeneous catalysis require a specific combination of electronic and steric effects from the ligands bound to the metal center. Finding the optimal combination of ligands is a challenging task due to the exceedingly large number of possibilities and the non-trivial ligand-ligand interactions. The classic example of Vaska's complex, <i>trans</i>-[Ir(PPh<sub>3</sub>)<sub>2</sub>(CO)(Cl)], illustrates this scenario. The ligands of this species activate iridium for the oxidative addition of hydrogen, yielding the dihydride <i>cis</i>-[Ir(H)<sub>2</sub>(PPh<sub>3</sub>)<sub>2</sub>(CO)(Cl)] complex. Despite the simplicity of this system, thousands of derivatives can be formulated for the activation of H<sub>2</sub>, with a limited number of ligands belonging to the same general categories found in the original complex. In this work, we show how DFT and machine learning (ML) methods can be combined to enable the prediction of reactivity within large chemical spaces containing thousands of complexes. In a space of 2574 species derived from Vaska's complex, data from DFT calculations are used to train and test ML models that predict the H<sub>2</sub>-activation barrier. In contrast to experiments and calculations requiring several days to be completed, the ML models were trained and used on a laptop on a time-scale of minutes. As a first approach, we combined Bayesian-optimized artificial neural networks (ANN) with features derived from autocorrelation and deltametric functions. The resulting ANNs achieved high accuracies, with mean absolute errors (MAE) between 1 and 2 kcal mol<sup>-1</sup>, depending on the size of the training set. By using a Gaussian process (GP) model trained with a set of selected features, including fingerprints, accuracy was further enhanced. Remarkably, this GP model minimized the MAE below 1 kcal mol<sup>-1</sup>, by using only 20% or less of the data available for training. The gradient boosting (GB) method was also used to assess the relevance of the features, which was used for both feature selection and model interpretation purposes. Features accounting for chemical composition, atom size and electronegativity were found to be the most determinant in the predictions. Further, the ligand fragments with the strongest influence on the H<sub>2</sub>-activation barrier were identified. Chemical space ; Space (punctuation) ; Computer science ; Chemistry ; Materials science ; Biochemistry ; Drug discovery ; Operating system Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies
Authors: Boris Lakard
Source: Applied Sciences (review, 2020-09-22)
Field: Materials Science — Polymers and Plastics

Electrochemical Biosensors Based on Conducting Polymers: A Review Conducting polymers are an important class of functional materials that has been widely applied to fabricate electrochemical biosensors, because of their interesting and tunable chemical, electrical, and structural properties. Conducting polymers can also be designed through chemical grafting of functional groups, nanostructured, or associated with other functional materials such as nanoparticles to provide tremendous improvements in sensitivity, selectivity, stability and reproducibility of the biosensor’s response to a variety of bioanalytes. Such biosensors are expected to play a growing and significant role in delivering the diagnostic information and therapy monitoring since they have advantages including their low cost and low detection limit. Therefore, this article starts with the description of electroanalytical methods (potentiometry, amperometry, conductometry, voltammetry, impedometry) used in electrochemical biosensors, and continues with a review of the recent advances in the application of conducting polymers in the recognition of bioanalytes leading to the development of enzyme based biosensors, immunosensors, DNA biosensors, and whole-cell biosensors. Biosensor ; Nanotechnology ; Conductometry ; Amperometry ; Conductive polymer ; Materials science ; Polymer ; Electrochemistry ; Chemistry ; Electrode ; Chromatography Conducting polymers and applications ; Electrochemical sensors and biosensors ; Electrochemical Analysis and Applications
Authors: Panagiota S. Perlepe | Itziar Oyarzabal | Aaron Mailman | Morgane Yquel | М.С. Платунов | Iurii Dovgaliuk | Mathieu Rouzières | Philippe Négrier | Denise Mondieig | Elizaveta A. Suturina | Marie‐Anne Dourges | Sébastien Bonhommeau | Rebecca A. Musgrave | Kasper S. Pedersen | Dmitry Chernyshov | F. Wilhelm | Andreï Rogalev | Corine Mathonière | Rodolphe Clérac
Source: Science (article, 2020-10-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metal-organic magnets with large coercivity and ordering temperatures up to 242°C Magnets derived from inorganic materials (e.g., oxides, rare-earth-based, and intermetallic compounds) are key components of modern technological applications. Despite considerable success in a broad range of applications, these inorganic magnets suffer several drawbacks, including energetically expensive fabrication, limited availability of certain constituent elements, high density, and poor scope for chemical tunability. A promising design strategy for next-generation magnets relies on the versatile coordination chemistry of abundant metal ions and inexpensive organic ligands. Following this approach, we report the general, simple, and efficient synthesis of lightweight, molecule-based magnets by postsynthetic reduction of preassembled coordination networks that incorporate chromium metal ions and pyrazine building blocks. The resulting metal-organic ferrimagnets feature critical temperatures up to 242°C and a 7500-oersted room-temperature coercivity. Coercivity ; Magnet ; Metal ; Materials science ; Condensed matter physics ; Metallurgy ; Physics Magnetism in coordination complexes ; Organic and Molecular Conductors Research ; Advanced NMR Techniques and Applications