jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saeed Aliakbari Sani | G.R. Ebrahimi | H. Vafaeenezhad | Ali Reza Kiani Rashid
Source: Journal of Magnesium and Alloys (article, 2018-05-23)
Field: Materials Science — Biomaterials

Modeling of hot deformation behavior and prediction of flow stress in a magnesium alloy using constitutive equation and artificial neural network (ANN) model The aim of the present study was to investigate the modeling and prediction of the high temperature flow characteristics of a cast magnesium (Mg–Al–Ca) alloy by both constitutive equation and ANN model. Toward this end, hot compression experiments were performed in 250–450 °C and in strain rates of 0.001–1 s−1. The true stress of alloy was first and foremost described by the hyperbolic sine function in an Arrhenius-type of constitutive equation taking the effects of strain, strain rate and temperature into account. Predictions indicated that unlike low strain rates and high temperature with dominant DRX activation, in relatively high strain rate and low temperature values, the precision of the models become decreased due to activation of twinning phenomenon. At that moment and for a better evaluation of twinning effect during deformation, a feed-forward back propagation ANN was developed to study the flow behavior of the investigated alloy. Then, the performance of the two suggested models has been assessed using a statistical criterion. The comparative assessment of the gained results specifies that the well-trained ANN is much more precise and accurate than the constitutive equations in predicting the hot flow behavior. Constitutive equation ; Materials science ; Flow stress ; Strain rate ; Crystal twinning ; Deformation (meteorology) ; Hyperbolic function ; Magnesium alloy ; Arrhenius equation ; Alloy ; Thermodynamics ; Mechanics ; Metallurgy ; Composite material ; Activation energy ; Mathematics ; Microstructure ; Mathematical analysis ; Physics Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Microstructure and mechanical properties
Authors: Pedram Ghamgosar | Federica Rigoni | Shujie You | Illia Dobryden | Mojtaba Gilzad Kohan | Anna Lucia Pellegrino | Isabella Concina | Nils Almqvist | Graziella Malandrino | Alberto Vomiero
Source: Nano Energy (article, 2018-06-21)
Field: Materials Science — Materials Chemistry

ZnO-Cu2O core-shell nanowires as stable and fast response photodetectors Materials science ; Metalorganic vapour phase epitaxy ; Nanowire ; Wurtzite crystal structure ; Photodetector ; Heterojunction ; Optoelectronics ; Chemical vapor deposition ; Band gap ; Copper oxide ; Oxide ; Ohmic contact ; Nanotechnology ; Layer (electronics) ; Epitaxy ZnO doping and properties ; Copper-based nanomaterials and applications ; Electronic and Structural Properties of Oxides
Authors: Connor W. Coley | Wengong Jin | Luke Rogers | Timothy F. Jamison | Tommi Jaakkola | William H. Green | Regina Barzilay | Klavs F. Jensen
Source: ChemRxiv (preprint, 2018-10-04)
Field: Materials Science — Materials Chemistry

A Graph-Convolutional Neural Network Model for the Prediction of Chemical Reactivity We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). The prediction task is factored into two stages comparable to manual expert approaches: considering possible sites of reactivity and evaluating their relative likelihoods. By training on hundreds of thousands of reaction precedents covering a broad range of reaction types from the patent literature, the neural model makes informed predictions of chemical reactivity. The model predicts the major product correctly over 85% of the time requiring around 100 ms per example, a significantly higher accuracy than achieved by previous machine learning approaches, and performs on par with expert chemists with years of formal training. We gain additional insight into predictions via the design of the neural model, revealing an understanding of chemistry qualitatively consistent with manual approaches. Reactivity (psychology) ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Convolutional neural network ; Task (project management) ; Graph ; Range (aeronautics) ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Mohamad Mahmoudi | R. Guardián | Brian Franco | Ji Ma | Raymundo Arróyave | İbrahim Karaman | Alaa Elwany
Source: Journal of Manufacturing Processes (article, 2018-09-20)
Field: Materials Science — Materials Chemistry

On the printability and transformation behavior of nickel-titanium shape memory alloys fabricated using laser powder-bed fusion additive manufacturing Materials science ; Nickel titanium ; Shape-memory alloy ; Fusion ; Design for manufacturability ; Transformation (genetics) ; Titanium alloy ; Metallurgy ; Composite material ; Mechanical engineering ; Alloy Shape Memory Alloy Transformations ; Additive Manufacturing Materials and Processes ; Additive Manufacturing and 3D Printing Technologies
Authors: Igor Sokolov | Maxim Dokukin | V. Kalaparthi | Miloš D. Miljković | Andrew Wang | John D. Seigne | Petros Grivas | Eugene Demidenko
Source: Proceedings of the National Academy of Sciences (article, 2018-12-03)
Field: Chemistry — Spectroscopy

Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: Detection of bladder cancer We report an approach in diagnostic imaging based on nanoscale-resolution scanning of surfaces of cells collected from body fluids using a recent modality of atomic force microscopy (AFM), subresonance tapping, and machine-leaning analysis. The surface parameters, which are typically used in engineering to describe surfaces, are used to classify cells. The method is applied to the detection of bladder cancer, which is one of the most common human malignancies and the most expensive cancer to treat. The frequent visual examinations of bladder (cytoscopy) required for follow-up are not only uncomfortable for the patient but a serious cost for the health care system. Our method addresses an unmet need in noninvasive and accurate detection of bladder cancer, which may eliminate unnecessary and expensive cystoscopies. The method, which evaluates cells collected from urine, shows 94% diagnostic accuracy when examining five cells per patient's urine sample. It is a statistically significant improvement (<i>P</i> < 0.05) in diagnostic accuracy compared with the currently used clinical standard, cystoscopy, as verified on 43 control and 25 bladder cancer patients. Medicine ; Bladder cancer ; Cancer ; Cancer detection ; Urethra ; Biopsy ; Pathology ; Diagnostic test ; Colorectal cancer ; Cervical cancer ; Urinary system ; Radiology ; Internal medicine ; Urology Advanced Proteomics Techniques and Applications ; Radiomics and Machine Learning in Medical Imaging ; Molecular Biology Techniques and Applications
Authors: Zonglin Chu | Yanxiao Han | Tong Bian | Soumen De | Petr Král | Rafał Klajn
Source: Journal of the American Chemical Society (article, 2018-12-31)
Field: Materials Science — Materials Chemistry

Supramolecular Control of Azobenzene Switching on Nanoparticles The reversible photoisomerization of azobenzene has been utilized to construct a plethora of systems in which optical, electronic, catalytic, and other properties can be controlled by light. However, owing to azobenzene's hydrophobic nature, most of these examples have been realized only in organic solvents, and systems operating in water are relatively scarce. Here, we show that by coadsorbing the inherently hydrophobic azobenzenes with water-solubilizing ligands on the same nanoparticulate platforms, it is possible to render them essentially water-soluble. To this end, we developed a modified nanoparticle functionalization procedure allowing us to precisely fine-tune the amount of azobenzene on the functionalized nanoparticles. Molecular dynamics simulations helped us to identify two distinct supramolecular architectures (depending on the length of the background ligand) on these nanoparticles, which can explain their excellent aqueous solubilities. Azobenzenes adsorbed on these water-soluble nanoparticles exhibit highly reversible photoisomerization upon exposure to UV and visible light. Importantly, the mixed-monolayer approach allowed us to systematically investigate how the background ligand affects the switching properties of azobenzene. We found that the nature of the background ligand has a profound effect on the kinetics of azobenzene switching. For example, a hydroxy-terminated background ligand is capable of accelerating the back-isomerization reaction by more than 6000-fold. These results pave the way toward the development of novel light-responsive nanomaterials operating in aqueous media and, in the long run, in biological environments. Azobenzene ; Photoisomerization ; Chemistry ; Nanoparticle ; Supramolecular chemistry ; Isomerization ; Molecular switch ; Photochemistry ; Ligand (biochemistry) ; Aqueous solution ; Monolayer ; Nanomaterials ; Nanotechnology ; Catalysis ; Molecule ; Organic chemistry ; Materials science Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Supramolecular Self-Assembly in Materials
Authors: Justin S. Smith | Benjamin Nebgen | R.I. Zubatyuk | Nicholas Lubbers | Christian Devereux | Kipton Barros | Sergei Tretiak | Olexandr Isayev | Adrián E. Roitberg
Source: ChemRxiv (preprint, 2018-07-06)
Field: Materials Science — Materials Chemistry

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning &lt;p&gt;Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology and chemistry, and billions of times faster&lt;i&gt;&lt;/i&gt;than CCSD(T)/CBS calculations. &lt;/p&gt; Scalability ; Thermochemistry ; Computer science ; Coupled cluster ; Transferability ; Artificial neural network ; Quantum chemical ; Computational science ; Quantum chemistry ; Artificial intelligence ; Transfer of learning ; Theoretical chemistry ; Quantum ; Chemistry ; Machine learning ; Physics ; Physical chemistry ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Janis Timoshenko | Andris Anspoks | Arturs Cintiņš | Alexei Kuzmin | J. Purāns | Anatoly I. Frenkel
Source: Physical Review Letters (article, 2018-05-31)
Field: Materials Science — Materials Chemistry

Neural Network Approach for Characterizing Structural Transformations by X-Ray Absorption Fine Structure Spectroscopy The knowledge of the coordination environment around various atomic species in many functional materials provides a key for explaining their properties and working mechanisms. Many structural motifs and their transformations are difficult to detect and quantify in the process of work (operando conditions), due to their local nature, small changes, low dimensionality of the material, and/or extreme conditions. Here we use an artificial neural network approach to extract the information on the local structure and its in situ changes directly from the x-ray absorption fine structure spectra. We illustrate this capability by extracting the radial distribution function (RDF) of atoms in ferritic and austenitic phases of bulk iron across the temperature-induced transition. Integration of RDFs allows us to quantify the changes in the iron coordination and material density, and to observe the transition from a body-centered to a face-centered cubic arrangement of iron atoms. This method is attractive for a broad range of materials and experimental conditions. Curse of dimensionality ; Materials science ; Chemical physics ; X-ray absorption spectroscopy ; Spectroscopy ; Absorption spectroscopy ; Radial distribution function ; Absorption (acoustics) ; Work (physics) ; X-ray absorption fine structure ; Transition metal ; Grid ; Biological system ; Nanotechnology ; Computer science ; Molecular dynamics ; Chemistry ; Physics ; Computational chemistry ; Optics ; Artificial intelligence ; Thermodynamics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Sandra Signorella | Claudia Palopoli | Gabriela N. Ledesma
Source: Coordination Chemistry Reviews (article, 2018-03-28)
Field: Chemistry — Inorganic Chemistry

Rationally designed mimics of antioxidant manganoenzymes: Role of structural features in the quest for catalysts with catalase and superoxide dismutase activity Chemistry ; Superoxide dismutase ; Catalase ; Hydrogen peroxide ; Superoxide ; Reactive oxygen species ; Antioxidant ; Catalysis ; Redox ; Oxidative stress ; Biochemistry ; Enzyme ; Organic chemistry Metal-Catalyzed Oxygenation Mechanisms ; Electrochemical Analysis and Applications ; Metal complexes synthesis and properties
Authors: Martha Ramesh | Martha Purna Chander Rao | Sambandam Anandan | H. Nagaraja
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2018-03-14)
Field: Materials Science — Polymers and Plastics

Adsorption and photocatalytic properties of NiO nanoparticles synthesized via a thermal decomposition process Materials science ; Photocatalysis ; Thermal decomposition ; Adsorption ; Non-blocking I/O ; Chemical engineering ; Nanoparticle ; Decomposition ; Process (computing) ; Nanotechnology ; Catalysis ; Organic chemistry Transition Metal Oxide Nanomaterials ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Hans‐Jürgen Butt | Rüdiger Berger | W. Steffen | Doris Vollmer | Stefan A. L. Weber
Source: Langmuir (article, 2018-08-15)
Field: Materials Science — Surfaces, Coatings and Films

Adaptive Wetting—Adaptation in Wetting Many surfaces reversibly change their structure and interfacial energy upon being in contact with a liquid. Such surfaces adapt to a specific liquid. We propose the first order kinetic model to describe dynamic contact angles of such adaptive surfaces. The model is general and does not refer to a particular adaptation process. The aim of the proposed model is to provide a quantitative description of adaptive wetting and to link changes in contact angles to microscopic adaptation processes. By introducing exponentially relaxing interfacial energies and applying Young's equation locally, we predict a change of advancing and receding contact angles depending on the velocity of the contact line. Even for perfectly homogeneous and smooth surfaces, a dynamic contact angle hysteresis is obtained. As possible adaptations, we discuss changes and reconstruction of polymer surfaces or monolayers, diffusion and swelling, adsorption of surfactants, replacement of contaminants, reorientation of liquid molecules, or formation of an electric double layer. Wetting ; Contact angle ; Hysteresis ; Wetting transition ; Chemical physics ; Monolayer ; Diffusion ; Materials science ; Surface energy ; Adaptation (eye) ; Kinetic energy ; Adsorption ; Nanotechnology ; Chemistry ; Mechanics ; Thermodynamics ; Composite material ; Optics ; Physical chemistry ; Classical mechanics ; Physics ; Condensed matter physics Surface Modification and Superhydrophobicity ; Polymer Surface Interaction Studies ; Block Copolymer Self-Assembly
Authors: Kyoungdoc Kim | Logan Ward | Jiangang He | Amar Krishna | Ankit Agrawal | Chris Wolverton
Source: Physical Review Materials (article, 2018-12-04)
Field: Materials Science — Materials Chemistry

Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds Discovering novel, multicomponent crystalline materials is a complex task owing to the large space of feasible structures. Here we demonstrate a method to significantly accelerate materials discovery by using a machine learning (ML) model trained on density functional theory (DFT) data from the Open Quantum Materials Database (OQMD). Our ML model predicts the stability of a material based on its crystal structure and chemical composition, and we illustrate the effectiveness of the method by application to finding new quaternary Heusler (QH) compounds. Our ML-based approach can find new stable materials at a rate 30 times faster than undirected searches and we use it to predict 55 previously unknown, stable QH compounds. We find the accuracy of our ML model is higher when trained using the diversity of crystal structures available in the OQMD than when training on well-curated datasets which contain only a single family of crystal structures (i.e., QHs). The advantage of using diverse training data shows how large datasets, such as OQMD, are particularly valuable for materials discovery and that we need not train separate ML models to predict each different family of crystal structures. Compared to other proposed ML approaches, we find that our method performs best for small $(&lt;{10}^{3})$ and large $(&gt;{10}^{5})$ training set sizes. The excellent flexibility and accuracy of the approach presented here can be easily generalized to other types of crystals. Flexibility (engineering) ; Materials science ; Stability (learning theory) ; Crystal structure prediction ; Chemical space ; Set (abstract data type) ; Characterization (materials science) ; Crystal (programming language) ; Machine learning ; Throughput ; Task (project management) ; Crystal structure ; Artificial intelligence ; Computer science ; Algorithm ; Nanotechnology ; Drug discovery ; Mathematics ; Crystallography ; Bioinformatics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; 2D Materials and Applications
Authors: Kristian Kersting
Source: Frontiers in Big Data (article, 2018-11-19)
Field: Materials Science — Materials Chemistry

Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines Machine learning (ML) and artificial intelligence (AI) are becoming dominant problem-solving techniques in many areas of research and industry, not least because of the recent successes of deep learning (DL). However, the equation AI=ML=DL, as recently suggested in the news, blogs, and media, falls too short. These fields share the same fundamental hypotheses: computation is a useful way to model intelligent behavior in machines. What kind of computation and how to program it? This is not the right question. Computation neither rules out search, logical, and probabilistic techniques, nor (deep) (un)supervised and reinforcement learning methods, among others, as computational models do include all of them. They complement each other, and the next breakthrough lies not only in pushing each of them but also in combining them. Psychology ; Cognitive science ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Anomaly Detection Techniques and Applications ; Data Stream Mining Techniques
Authors: Ondrej Dyck | Songkil Kim | Elisa Jimenez‐Izal | Anastassia N. Alexandrova | Sergei V. Kalinin | Stephen Jesse
Source: Small (article, 2018-08-26)
Field: Materials Science — Surfaces, Coatings and Films

Building Structures Atom by Atom via Electron Beam Manipulation Building materials from the atom up is the pinnacle of materials fabrication. Until recently the only platform that offered single-atom manipulation was scanning tunneling microscopy. Here controlled manipulation and assembly of a few atom structures are demonstrated by bringing together single atoms using a scanning transmission electron microscope. An atomically focused electron beam is used to introduce Si substitutional defects and defect clusters in graphene with spatial control of a few nanometers and enable controlled motion of Si atoms. The Si substitutional defects are then further manipulated to form dimers, trimers, and more complex structures. The dynamics of a beam-induced atomic-scale chemical process is captured in a time-series of images at atomic resolution. These studies suggest that control of the e-beam-induced local processes offers the next step toward atom-by-atom nanofabrication, providing an enabling tool for the study of atomic-scale chemistry in 2D materials and fabrication of predefined structures and defects with atomic specificity. Atom (system on chip) ; Atomic physics ; Electron ; Materials science ; Cathode ray ; Physics ; Nuclear physics ; Computer science Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Guangrong Meng | Lazaros T. Kakalis | Steven P. Nolan | Michal Szostak
Source: Tetrahedron Letters (article, 2018-12-26)
Field: Chemistry — Organic Chemistry

A simple 1H NMR method for determining the σ-donor properties of N-heterocyclic carbenes Chemistry ; Carbene ; Steric effects ; Catalysis ; Transition metal ; Reactivity (psychology) ; Ligand (biochemistry) ; Combinatorial chemistry ; Computational chemistry ; Organic chemistry N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Catalytic Cross-Coupling Reactions ; Synthetic Organic Chemistry Methods
Authors: Hongxiang Zong | Ghanshyam Pilania | Xiangdong Ding | Graeme J. Ackland | Turab Lookman
Source: npj Computational Materials (article, 2018-08-20)
Field: Materials Science — Materials Chemistry

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning Abstract Atomic simulations provide an effective means to understand the underlying physics of structural phase transformations. However, this remains a challenge for certain allotropic metals due to the failure of classical interatomic potentials to represent the multitude of bonding. Based on machine-learning (ML) techniques, we develop a hybrid method in which interatomic potentials describing martensitic transformations can be learned with a high degree of fidelity from ab initio molecular dynamics simulations (AIMD). Using zirconium as a model system, for which an adequate semiempirical potential describing the phase transformation process is lacking, we demonstrate the feasibility and effectiveness of our approach. Specifically, the ML-AIMD interatomic potential correctly captures the energetics and structural transformation properties of zirconium as compared to experimental and density-functional data for phonons, elastic constants, as well as stacking fault energies. Molecular dynamics simulations successfully reproduce the transformation mechanisms and reasonably map out the pressure–temperature phase diagram of zirconium. Zirconium ; Molecular dynamics ; Interatomic potential ; Diffusionless transformation ; Stacking fault ; Materials science ; Phase diagram ; Phase (matter) ; Transformation (genetics) ; Martensite ; Ab initio ; Density functional theory ; Chemical physics ; Statistical physics ; Thermodynamics ; Computational chemistry ; Chemistry ; Physics ; Dislocation ; Microstructure ; Metallurgy Machine Learning in Materials Science ; Nuclear Materials and Properties ; X-ray Diffraction in Crystallography