jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Henrike Loeffler | Anika Jonitz‐Heincke | Kirsten Peters | Brigitte Mueller‐Hilke | Tomas Fiedler | Rainer Bader | Annett Klinder
Source: Materials (article, 2020-03-05)
Field: Materials Science — Materials Chemistry

Comparison of Inflammatory Effects in THP-1 Monocytes and Macrophages after Exposure to Metal Ions Monocytes and macrophages are the first barrier of the innate immune system, which interact with abrasion and corrosion products, leading to the release of proinflammatory mediators and free reactive molecules. The aim of this study was to understand inflammation-relevant changes in monocytes and macrophages after exposure to corrosion products. To do this, the THP-1 cell line was used to analyze the effects of metal ions simultaneously in monocytes and differentiated macrophages. Cells were stimulated with several concentrations of metal salts (CoCl<sub>2</sub>, NiCl<sub>2</sub>, CrCl<sub>3</sub> × 6H<sub>2</sub>O) to analyze viability, gene expression, protein release and ROS production. Untreated cells served as negative controls. While exposure to Cr(3+) did not influence cell viability in both cell types, the highest concentration (500 µM) of Co(2+) and Ni(2+) showed cytotoxic effects mirrored by significantly reduced metabolism, cell number and a concomitant increase of ROS. The release of IL-1β, IL-8, MCP-1 and M-CSF proteins was mainly affected in macrophages after metal ion exposure (100 µM), indicating a higher impact on pro-inflammatory activity. Our results prove that monocytes and macrophages react very sensitively to corrosion products. High concentrations of bivalent ions lead to cell death, while lower concentrations trigger the release of inflammatory mediators, mainly in macrophages. THP1 cell line ; Proinflammatory cytokine ; Viability assay ; Inflammation ; Monocyte ; Macrophage ; Chemistry ; Immune system ; Cytotoxic T cell ; Innate immune system ; Cell culture ; Metal ; Cell biology ; Cell ; Immunology ; Biochemistry ; Biology ; In vitro Corrosion Behavior and Inhibition ; Nanoparticles: synthesis and applications ; S100 Proteins and Annexins
Authors: Daigo Ohki | Michihiro Hirata | Takehiro Tani | Kazushi Kanoda | Akito Kobayashi
Source: Physical Review Research (article, 2020-09-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chiral excitonic instability of two-dimensional tilted Dirac cones This work explores excitonic pairing instability in Zeeman-split two- dimensional Dirac cones with titled valleys, considering self-energy corrections via renormalization-group technique and a ladder-type vertex. The authors demonstrate how the pairing is affected by in-plane magnetic field and small carrier doping near charge neutrality. Pairing ; Instability ; Magnetic field ; Dirac (video compression format) ; Field (mathematics) ; Work (physics) Organic and Molecular Conductors Research ; Topological Materials and Phenomena ; Strong Light-Matter Interactions
Authors: Ana Vallejo Vitaller | Ueli Angst | Bernhard Elsener
Source: Geothermal Energy (article, 2020-03-19)
Field: Materials Science — Materials Chemistry

Laboratory tests simulating corrosion in geothermal power plants: influence of service conditions Abstract One of the main challenges associated with the operation and maintenance of binary geothermal power plants is the degradation of construction materials. In this sense, it is crucial to apply appropriate preventive maintenance in critical components (such as the wellheads, heat exchangers, or pipes), while reducing shutdown times. Based on electrochemical measurements performed in an autoclave corrosion testing setup, we studied the corrosion mechanism of API L80 steel grade as a function of operational and/or maintenance procedures. We used a test fluid representative for a site in Switzerland, but the main observations made may be applicable in a wider context. We found that changes in the fluid temperature (from 200 to 100 °C) or temporary oxygen ingress significantly influenced the corrosion behavior of this carbon steel and increased its corrosion rate (from approx. 20 µm/year to &gt; 120 µm/year). After a few days, the corrosion rate was found to decrease and stabilize around values of 50–70 µm/year, as a result of a porous corrosion product layer formed on the metal surface (approx. 250 µm thick). Electrochemical impedance spectroscopy indicated an increase in capacitance of the double layer over time, most likely due to an increase in the effective surface area of the steel sample, as a consequence of surface roughening due to corrosion. The results from this study may be implemented in the design and operation of future power plants in Switzerland and elsewhere to ensure reliable and cost-effective energy production from geothermal resources. Corrosion ; Geothermal gradient ; Autoclave ; Context (archaeology) ; Materials science ; Carbon steel ; Dielectric spectroscopy ; Geothermal power ; Environmental science ; Metallurgy ; Service life ; Heat exchanger ; Geothermal energy ; Nuclear engineering ; Electrochemistry ; Composite material ; Engineering ; Geology ; Mechanical engineering ; Chemistry ; Electrode Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Phatiphat Thounthong | Matheepot Phattanasak | Damien Guilbert | Noureddine Takorabet | Serge Pierfederici | Babak Nahid‐Mobarakeh | Nicu Bizon | Poom Kumam
Source: Energies (article, 2020-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Differential Flatness Based-Control Strategy of a Two-Port Bidirectional Supercapacitor Converter for Hydrogen Mobility Applications This article is focused on an original control approach applied to a transportation system that includes a polymer electrolyte membrane fuel cell (PEMFC) as the main energy source and supercapacitors (SC) as the energy storage backup. To interface the SC with the DC bus of the embedded network, a two-port bidirectional DC-DC converter was used. To control the system and ensure its stability, a reduced-order mathematical model of the network was developed through a nonlinear control approach employing a differential flatness algorithm, which is an attractive and efficient solution to make the system stable by overcoming the dynamic issues generally met in the power electronics networks of transportation systems. The design and tuning of the system control were not linked with the equilibrium point at which the interactions between the PEMFC main source, the SC energy storage device, and the loads are taken into consideration by the proposed control law. Besides this, high dynamics in the load power rejection were accomplished, which is the main contribution of this article. To verify the effectiveness of the developed control law, a small-scale experimental test rig was realized in the laboratory and the control laws were implemented in a dSPACE 1103 controller board. The experimental tests were performed with a 1 kW PEMFC source and a 250 F 32 V SC module as an energy storage backup. Lastly, the performances of the proposed control strategy were validated based on real experimental results measured during driving cycles, including motoring mode, ride-though, and regenerative braking mode. Backup ; Flatness (cosmology) ; Energy storage ; Supercapacitor ; Automotive engineering ; Engineering ; Control theory (sociology) ; Renewable energy ; Computer science ; Control engineering ; Power (physics) ; Electrical engineering ; Control (management) ; Mechanical engineering Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Advanced Battery Technologies Research
Authors: Massimiliano Lupo Pasini | Ying Wai Li | Junqi Yin | Jiaxin Zhang | Kipton Barros | Markus Eisenbach
Source: Journal of Physics Condensed Matter (article, 2020-11-17)
Field: Materials Science — Materials Chemistry

Fast and stable deep-learning predictions of material properties for solid solution alloys** We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between different physical properties in alloy systems to improve the prediction accuracy of neural network (NN) models. We use multitasking NN models to simultaneously predict the total energy, charge density and magnetic moment. These physical properties mutually serve as constraints during the training of the multitasking NN, resulting in more reliable DL models because multiple physics properties are correctly learned by a single model. Two binary alloys, copper-gold (CuAu) and iron-platinum (FePt), were studied. Our results show that once the multitasking NN's are trained, they can estimate the material properties for a specific configuration hundreds of times faster than first-principles density functional theory calculations while retaining comparable accuracy. We used a simple measure based on the root-mean-squared errors to quantify the quality of the NN models, and found that the inclusion of charge density and magnetic moment as physical constraints leads to more stable models that exhibit improved accuracy and reduced uncertainty for the energy predictions. Material properties ; Binary number ; Measure (data warehouse) ; Moment (physics) ; Human multitasking ; Charge (physics) ; Quality (philosophy) ; Magnetic moment Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Model Reduction and Neural Networks
Authors: Sogol Lotfi | Ziyan Zhang | Gayatri Viswanathan | Kaitlyn Fortenberry | Aria Mansouri Tehrani | Jakoah Brgoch
Source: ChemRxiv (preprint, 2020-02-07)
Field: Materials Science — Materials Chemistry

Targeting Productive Composition Space Through Machine-Learning Directed Inorganic Synthesis This work presents an approach to aid the discovery of novel inorganic solids by highlighting regions of underexplored, yet likely productive composition space using machine learning. A support vector regression (SVR) algorithm was constructed first to determine a compound’s formation energy (∆𝐸𝑓,SVR) based solely on chemical composition using data from 313,965 high-throughput density functional theory calculations. The resulting predicted formation energies (r2 = 0.94; MAE = 85 meV/atom) were then used to construct zero-kelvin convex hull diagrams and identify compositions immediately on the hull, as well as +50 meV above the convex hull to capture potential compounds that are considered energetically unfavorable but that are still experimentally accessible. Using this methodology, four ternary composition diagrams, Y−Ag−Tr (Tr = B, Al, Ga, In), were explored owing to the diversity of chemistries as a function of triel element to provide experimental validation for the predictions. A particularly promising but unexplored region in the Y−Ag−In diagram was identified, and the ensuing solid-state high-temperature synthesis produced YAg0.65In1.35, which has not been reported. First-principle calculations were finally used to determine the ordering of Ag and In as well as confirm the crystal structure solution. Our combination of machine learning, inorganic synthesis, and computational modeling describes a new avenue where data-centric models and computation play a critical role in supporting the experimental examination of unexplored phase diagrams. Convex hull ; Ternary operation ; Chemical space ; Ternary plot ; Support vector machine ; Composition (language) ; Algorithm ; Diagram ; Space (punctuation) ; Machine learning ; Chemistry ; Regular polygon ; Computer science ; Materials science ; Mathematics ; Geometry ; Drug discovery Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Sangqi Xiong | Xin Li | Xiangwei Wu | Jin Yu | Oleg I. Gorbatov | Igor Di Marco | Paul R. C. Kent | Weiwei Sun
Source: Computational Materials Science (article, 2020-06-19)
Field: Materials Science — Materials Chemistry

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus Materials science ; Binary number ; Stability (learning theory) ; Density functional theory ; Modulus ; Workflow ; Bulk modulus ; Binary tree ; Elastic modulus ; Thermodynamics ; Machine learning ; Computer science ; Composite material ; Algorithm ; Mathematics ; Computational chemistry ; Physics ; Chemistry Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Intermetallics and Advanced Alloy Properties
Authors: Yue Liu | Bingxue Yu | Zhiwen Liu | David E. Beck | Kaiyang Zeng
Source: ACS Applied Materials & Interfaces (article, 2020-02-03)
Field: Materials Science — Materials Chemistry

High-Speed Piezoresponse Force Microscopy and Machine Learning Approaches for Dynamic Domain Growth in Ferroelectric Materials Domain dynamics has been one of the hottest research topics for ferroelectric materials in order to understand the ferroelectric mechanisms and to develop the related applications. By using high-speed piezoresponse force microscopy (HSPFM), it is possible to observe the dynamic domain evolution in an ultrashort time increment. This paper combines the HSPFM experiments and machine learning to study the domain growth under a weak AC field in ferroelectric materials. Here, the Bayesian optimized support vector machine is employed to classify the switching domain and stable domain. The results indicate that the machine learning classifier is capable of discerning the switching area. In addition, the domain associated characteristics, such as domain pinning and domain wall pinning, can also be observed and analyzed by combining experiments and machine learning. The machine learning approach can fast and deeply extract the complicated features related to free energy from the multidimensional signals obtained by HSPFM. Piezoresponse force microscopy ; Ferroelectricity ; Materials science ; Domain (mathematical analysis) ; Artificial intelligence ; Machine learning ; Computer science ; Classifier (UML) ; Nanotechnology ; Optoelectronics ; Dielectric Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Ultrasonics and Acoustic Wave Propagation
Authors: Igor Ruvinov | Nima Zamani | Y. Zhou | Mohammad Ibraheem Khan
Source: Sensors and Actuators A Physical (article, 2020-07-02)
Field: Materials Science — Materials Chemistry

Novel laser processed shape memory alloy actuator design with an embedded strain gauge sensor using dual resistance measurements. Part II: Recurrent neural network-based position and force estimation Actuator ; Shape-memory alloy ; Position (finance) ; Strain gauge ; SMA* ; Artificial neural network ; Computer science ; Artificial intelligence ; Robotics ; Control theory (sociology) ; Work (physics) ; Dual (grammatical number) ; Mechanical engineering ; Materials science ; Robot ; Structural engineering ; Engineering ; Algorithm ; Control (management) Shape Memory Alloy Transformations
Authors: Ariel Boucheikhchoukh | Jules Thibault | Clémence Fauteux‐Lefebvre
Source: The Canadian Journal of Chemical Engineering (article, 2020-04-09)
Field: Materials Science — Materials Chemistry

Catalyst design using artificial intelligence: <scp>SO<sub>2</sub></scp> to <scp>SO<sub>3</sub></scp> case study Abstract Catalyst design is key to the improvement of chemical process efficiency. The required work for the development of new catalysts can be supported through the proper application of artificial intelligence to identify optimal compositions. A generic methodology for the application of machine learning to catalysis research is therefore outlined in this work. The catalytic oxidation of SO 2 was used to exemplify the first iteration of this methodology. 1784 data points from 31 published papers were compiled into a databank. The inlet SO 2 concentration ranged from 0 to 66 mol%. An artificial neural network (ANN) was trained on the databank in order to predict SO 2 conversion based on the catalyst composition and the reactor operating conditions (temperature, pressure, catalyst mass: volumetric flowrate ratio ( w/v ), and feed composition). The model achieved a root‐mean‐square error of 6.6%. A preliminary screening step identified 3:1 V‐Mg/SiO 2 catalysts as exhibiting high conversion at 648 K. A multi‐objective optimization was then performed on a single catalyst to identify solutions exhibiting high conversion and high productivity at 648 K while minimizing the catalyst cost. The optimal solution was predicted to be a 2.9 wt% V/0.2 wt% Mg/SiO 2 catalyst operating at a w/v of 7.49 kg‐cat · s/m 3 STP, achieving 100% SO 2 conversion with a material cost among the bottom third of cost values. Artificial intelligence can then be employed to extract useful knowledge from published catalytic data and orient future search for novel catalyst development. Catalysis ; Artificial neural network ; Chemistry ; Work (physics) ; Chemical engineering ; Process engineering ; Computer science ; Engineering ; Materials science ; Mechanical engineering ; Artificial intelligence ; Organic chemistry Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Gas Sensing Nanomaterials and Sensors
Authors: Tiantian Hu | Hui Song | Tao Jiang | Shaobo Li
Source: Symmetry (article, 2020-11-17)
Field: Materials Science — Materials Chemistry

Learning Representations of Inorganic Materials from Generative Adversarial Networks The two most important aspects of material research using deep learning (DL) or machine learning (ML) are the characteristics of materials data and learning algorithms, where the proper characterization of materials data is essential for generating accurate models. At present, the characterization of materials based on the molecular composition includes some methods based on feature engineering, such as Magpie and One-hot. Although these characterization methods have achieved significant results in materials research, these methods based on feature engineering cannot guarantee the integrity of materials characterization. One possible approach is to learn the materials characterization via neural networks using the chemical knowledge and implicit composition rules shown in large-scale known materials. This article chooses an adversarial method to learn the composition of atoms using the Generative Adversarial Network (GAN), which makes sense for data symmetry. The total loss value of the discriminator on the test set is reduced from 4.1e13 to 0.3194, indicating that the designed GAN network can well capture the combination of atoms in real materials. We then use the trained discriminator weights for material characterization and predict bandgap, formation energy, critical temperature (Tc) of superconductors on the Open Quantum Materials Database (OQMD), Materials Project (MP), and SuperCond datasets. Experiments show that when using the same predictive model, our proposed method performs better than One-hot and Magpie. This article provides an effective method for characterizing materials based on molecular composition in addition to Magpie, One-hot, etc. In addition, the generator learned in this study generates hypothetical materials with the same distribution as known materials, and these hypotheses can be used as a source for new material discovery. Discriminator ; Characterization (materials science) ; Computer science ; Generator (circuit theory) ; Generative grammar ; Feature (linguistics) ; Artificial intelligence ; Set (abstract data type) ; Artificial neural network ; Feature engineering ; Deep learning ; Machine learning ; Materials science ; Nanotechnology ; Programming language ; Power (physics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Yun Zhang | Ling Wang | Xinqiao Wang | Chengyun Zhang | Jiamin Ge | Jing Tang | An Su | Hongliang Duan
Source: ChemRxiv (preprint, 2020-12-17)
Field: Materials Science — Materials Chemistry

Data Augmentation and Transfer Learning Strategies for Reaction Prediction in Low Chemical Data Regimes &lt;p&gt;&lt;b&gt;Abstract:&lt;/b&gt; Effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery. Despite the outstanding capability of deep learning in retrosynthesis and forward synthesis, predictions based on small chemical datasets generally result in low accuracy due to an insufficiency of reaction examples. Here, we introduce a new state art of method, which integrates transfer learning with transformer model to predict the outcomes of the Baeyer-Villiger reaction which is a representative small dataset reaction. The results demonstrate that introducing transfer learning strategy markedly improves the top-1 accuracy of the transformer-transfer learning model (81.8%) over that of the transformer-baseline model (58.4%). Moreover, we further introduce data augmentation to the input reaction SMILES, which allows for better performance and improves the accuracy of the transformer-transfer learning model (86.7%). In summary, both transfer learning and data augmentation methods significantly improve the predictive performance of transformer model, which are powerful methods used in chemistry field to eliminate the restriction of limited training data.&lt;/p&gt; Transformer ; Transfer of learning ; Computer science ; Retrosynthetic analysis ; Artificial intelligence ; Deep learning ; Machine learning ; Training set ; Chemistry ; Engineering ; Stereochemistry Machine Learning in Materials Science
Authors: Prasanna V. Balachandran
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2020-03-05)
Field: Materials Science — Materials Chemistry

Data-driven design of B20 alloys with targeted magnetic properties guided by machine learning and density functional theory Abstract Materials science ; Density functional theory ; Nanotechnology ; Computational chemistry Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Ruoxi Sun | Hanjun Dai | Li Li | Steven Kearnes | Bo Dai
Source: arXiv (Cornell University) (preprint, 2020-07-14)
Field: Materials Science — Materials Chemistry

Energy-based View of Retrosynthesis Retrosynthesis -- the process of identifying a set of reactants to synthesize a target molecule -- is of vital importance to material design and drug discovery. Existing machine learning approaches based on language models and graph neural networks have achieved encouraging results. In this paper, we propose a framework that unifies sequence- and graph-based methods as energy-based models (EBMs) with different energy functions. This unified perspective provides critical insights about EBM variants through a comprehensive assessment of performance. Additionally, we present a novel dual variant within the framework that performs consistent training over Bayesian forward- and backward-prediction by constraining the agreement between the two directions. This model improves state-of-the-art performance by 9.6% for template-free approaches where the reaction type is unknown. Computer science ; Retrosynthetic analysis ; Graph ; Perspective (graphical) ; Artificial intelligence ; Process (computing) ; Set (abstract data type) ; Machine learning ; Theoretical computer science ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics