jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yadgar I. Abdulkarim | Halgurd N. Awl | Fahmi F. Muhammadsharif | Mehmet Bakır | Fatih Özkan Alkurt | Olcay Altıntaş | Salah Raza Saeed | Mohamed Taouzari | Muharrem Karaaslan | Bhargav Appasani | Jian Dong
Source: Frontiers in Physics (article, 2022-08-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A vanadium dioxide-based metamaterial with quatrefoil and circle loaded structure on flexible polyamide substrate for terahertz applications In this study, a novel broadband metamaterial (MTM) absorber is proposed, which is based on vanadium dioxide (VO 2 ) resonators coated onto a flexible polyamide substrate. The VO 2 resonator is in the form of a quatrefoil and circle-loaded complementary square split ring resonator (CSSRR), which is fine tuned to operate in the terahertz range. The MTM design and simulation were carried out using Computer Simulation Technology (CST) software, by which different layouts were investigated to achieve the optimum structure. The results showed the presence of two pronounced peaks for the proposed MTM structure at 0.88 and 1.42 THz. The absorption peaks were found to be insensitive to the wave polarization and incident angle from 0° to 90° . The absorption mechanism of the MTM was investigated considering the insulating and metallic states of VO 2 . The results were in a good agreement with those calculated using high frequency simulated structure (HFSS) software. The flexible nature of the proposed MTM design makes it a viable candidate for non-planar and conformal geometry applications in stealth technology, and terahertz imaging. HFSS ; Terahertz radiation ; Metamaterial ; Materials science ; Polyamide ; Resonator ; Substrate (aquarium) ; Planar ; Optoelectronics ; Absorption (acoustics) ; Split-ring resonator ; Optics ; Computer science ; Composite material ; Telecommunications ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Radio Wave Propagation Studies
Authors: Nicholas Lewis | Yicheng Jin | Xiuyu Tang | Vidit Shah | Christina Doty | Bethany E. Matthews | Sarah Akers | Steven R. Spurgeon
Source: npj Computational Materials (article, 2022-12-12)
Field: Materials Science — Materials Chemistry

Forecasting of in situ electron energy loss spectroscopy Abstract Forecasting models are a central part of many control systems, where high-consequence decisions must be made on long latency control variables. These models are particularly relevant for emerging artificial intelligence (AI)-guided instrumentation, in which prescriptive knowledge is needed to guide autonomous decision-making. Here we describe the implementation of a long short-term memory model (LSTM) for forecasting in situ electron energy loss spectroscopy (EELS) data, one of the richest analytical probes of materials and chemical systems. We describe key considerations for data collection, preprocessing, training, validation, and benchmarking, showing how this approach can yield powerful predictive insight into order-disorder phase transitions. Finally, we comment on how such a model may integrate with emerging AI-guided instrumentation for powerful high-speed experimentation. Benchmarking ; Instrumentation (computer programming) ; Computer science ; Preprocessor ; Machine learning ; Electron energy loss spectroscopy ; Artificial intelligence ; Data pre-processing ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Jin Da Tan | Balamurugan Ramalingam | Swee Liang Wong | Jayce Jian Wei Cheng | Yee‐Fun Lim | Vijila Chellappan | Saif A. Khan | Jatin Kumar | Kedar Hippalgaonkar
Source: ChemRxiv (preprint, 2022-07-14)
Field: Materials Science — Materials Chemistry

Machine Learning Predicts Conversion and Molecular Weight Distributions in Computer Controlled Polymerization The skew and shape of the Molecular Weight Distribution (MWD) of polymers have a significant impact on polymer physical properties. Standard summary metrics statistically derived from the MWD only provide an incomplete picture of the polymer MWD. Machine learning (ML) methods coupled with high-throughput experimentation (HTE) could potentially allow for the prediction of the entire polymer MWD without information loss. In our work, we present a computer controlled HTE platform that is able to run up to 8 unique variable conditions for the free radical polymerizations of styrene. The segmented-flow HTE system was equipped with an inline Raman spectrometer and offline size exclusion chromatography (SEC) to obtain time dependent conversion and MWD respectively. In a supervised learning exercise, the gradient-boosted decision trees ML algorithm was used to predict monomer conversion from reagent concentrations and reaction time with high accuracy, using data obtained from the Raman spectra. A second ML algorithm uses the random forest regressor to predict entire MWDs of the synthesized polymer. Each algorithm can navigate the complexity of multiple parallel reactions occurring in a polymerization. The algorithm accurately predicts monomer conversion in the first case despite variations in the polymerization kinetic parameters over time. In the second case, we predict the polymer MWD where fine details such as the shape and skew of the MWD are captured without information loss. SHAP values were calculated to examine the dependence of the ML model output on the experimental conditions. A transfer learning approach was also used to enhance the predictive power of a Deep Neural Network (DNN) model in predicting batch polymerization MWDs. Overall, we demonstrate that the combination of HTE and ML provide a high level of predictive accuracy in determining polymerization outcomes, providing polymer chemists with the ability to target the synthesis of polymers with desired properties via the predicted conversion and MWD. Polymerization ; Polymer ; Monomer ; Computer science ; Molar mass distribution ; Size-exclusion chromatography ; Biological system ; Algorithm ; Materials science ; Chemistry ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Marek Florkowski
Source: IEEE Transactions on Transportation Electrification (article, 2022-06-23)
Field: Materials Science — Materials Chemistry

Effect of Magnetic Field on Partial Discharge Dynamics in Insulation Systems of Transportation Power Devices The globally observed trend toward electrically supplied devices is common in the transportation segment. Traditionally, the performance of electrical insulation has been assessed with respect to electric stresses and endurance. In all current-carrying devices, magnetic fields will also be superimposed on electric ones; therefore, the influence of a magnetic field’s exposure on partial discharge (PD) dynamics is an actual research topic. This problem is important in the transportation segment as well, where low- and medium-voltage levels are mostly used for supply and energy conversion (which are associated with high load currents). This article is focused on the influence of magnetic fields on the dynamics of PDs in the voids of insulation systems of transportation power devices and refers to both ac and dc cases. A rather weak magnetic field (80 mT) was established in order to detect and analyze early stages of PD behavior. The measurements of PDs that were carried out in a dedicated setup revealed their impact on PD intensity, which have been visualized in time-sequence diagrams and PD images. This detected effect is attributed to both the elongation of the charged particle trajectory and the enhancement of the electron energy due to acceleration. It has been shown that the impact of a magnetic field can be observed within a supply voltage frequency range of 20–400 Hz (which is typical for the transportation segment). The PD intensity was amplified in the above range at a magnetic field induction of 80 mT (even up to 50%). The effect of a magnetic field can be recognized as an additional modulation factor that influences the dynamics of PDs. Magnetic field ; Partial discharge ; Voltage ; Electric field ; Range (aeronautics) ; Electrical engineering ; Materials science ; Intensity (physics) ; Mechanics ; Nuclear magnetic resonance ; Physics ; Engineering ; Optics ; Composite material High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Power Transformer Diagnostics and Insulation
Authors: Marek Florkowski
Source: Scientific Reports (article, 2022-12-21)
Field: Materials Science — Materials Chemistry

Magnetic field modulated dynamics of partial discharges in defects of high voltage insulating materials This paper presents original measurement methodology and detection approach to determine the influence of the magnetic field on the partial discharge (PD) dynamics. The application areas refer to insulation systems of both grid and industrial network devices and emerging segments such as high-speed rail, electric vehicles or more electric aircrafts. Conventionally PD measurements are executed only in electric field, however the interaction of magnetic and electric fields influences the dynamics of PDs. The measurement technique allowed to detect quantitatively the effect of magnetic fields on PDs in two representative arrangements: in gaseous void in dielectric material and in corona point-plane setup. Measurements in both configurations have revealed amplification of PDs intensity. The quantitative comparison of PDs evolution in the magnetic field is a novel aspect shown in this paper. Combination of phase resolved images and time-sequence intensity diagrams obtained at magnetic field switching allowed to visualize and determine quantitatively this impact. This effect is attributed to the extension of the charged particle trajectory and the amplification of the electron energy due to acceleration. Thus, the investigated impact of a magnetic field can be perceived as an additional element that influences the PD dynamics. Electric field ; Magnetic field ; Partial discharge ; Materials science ; Dielectric ; Computational physics ; Voltage ; Nuclear magnetic resonance ; Physics ; Optoelectronics High voltage insulation and dielectric phenomena ; Plasma Diagnostics and Applications ; Aerosol Filtration and Electrostatic Precipitation
Authors: Priyesh Pappinisseri Puluckul | Maarten Weyn
Source: Sensors (article, 2022-06-23)
Field: Materials Science — Materials Chemistry

Battery-Less Environment Sensor Using Thermoelectric Energy Harvesting from Soil-Ambient Air Temperature Differences Energy harvesting is an effective technique for prolonging the lifetime of Internet of Things devices and Wireless Sensor Networks. In applications such as environmental sensing, which demands a deploy-and-forget architecture, energy harvesting is an unavoidable technology. Thermal energy is one of the most widely used sources for energy harvesting. A thermal energy harvester can convert a thermal gradient into electrical energy. Thus, the temperature difference between the soil and air could act as a vital source of energy for an environmental sensing device. In this paper, we present a proof-of-concept design of an environmental sensing node that harvests energy from soil temperature and uses the DASH7 communication protocol for connectivity. We evaluate the soil temperature and air temperature based on the data collected from two locations: one in Belgium and the other in Iceland. Using these datasets, we calculate the amount of energy that is producible from both of these sites. We further design power management and monitoring circuit and use a supercapacitor as the energy storage element, hence making it battery-less. Finally, we deploy the proof-of-concept prototype in the field and evaluate its performance. We demonstrate that the system can harvest, on average, 178.74 mJ and is enough to perform at least 5 DASH7 transmissions and 100 sensing tasks per day. Energy harvesting ; Battery (electricity) ; Wireless sensor network ; Energy (signal processing) ; Environmental science ; Computer science ; Energy storage ; Thermal energy ; Proof of concept ; Electrical engineering ; Wireless ; Electric potential energy ; Node (physics) ; Remote sensing ; Power (physics) ; Automotive engineering ; Telecommunications ; Computer network ; Engineering Advanced Thermoelectric Materials and Devices ; Energy Harvesting in Wireless Networks ; Innovative Energy Harvesting Technologies
Authors: Jinchang Yin | Haonan Wu | Jintao Zhang | Shuangshuang Wu | Hongting Zheng | Fuli Zhao | Yuanzhi Shao
Source: Chemistry of Materials (article, 2022-06-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantum Chemical Calculations and Machine Learning Predictions Innovate Synthesis for High-Performance Optical Gold Nanorods Understanding the optical properties of gold nanorods (GNRs) in the colloidal state is crucial to engineering them for versatile applications in many fields. Concomitant gold nanospheres (GNSs) are easily involved in GNR synthesis, incurring a negative effect on the GNR performance. To unravel the underlying mechanism, we constructed a GNR–GNS heterodimer to imitate their colloidal state and calculated the relevant optical and electronic properties through a quantum chemical approach. The calculations reveal that GNSs prevent certain charge-transfer excitations of adjacent GNRs by affecting the electronic structure and thereby the excitation behavior of the GNR. We synthesized 310 sets of GNR–GNS colloidal solutions with a seed-mediated growth method and then measured their absorption spectra to extract the datasets available for 11 machine learning algorithms. Among them, XGBoost had the best prediction accuracy of over 94%. A direct relevance from the initial synthesis parameters to the final optical properties of GNR–GNS colloids has been successfully identified by the machine learning approach, which could skip the cumbersome step-by-step procedure used for the conventional nanostructure characterization as well as optimize the batch GNR synthesis process with improved GNR performance simultaneously. Methodologically, such a three-in-one approach combining chemical synthesis, quantum chemical calculations, and machine learning predictions can be extended to other chemical synthetic studies, with methodological guidance to chemistry and materials science researchers. Nanorod ; Nanostructure ; Nanotechnology ; Materials science ; Colloidal gold ; Quantum dot ; Characterization (materials science) ; Computer science ; Nanoparticle Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Junji Zhang | Joshua Pagotto | Timothy T. Duignan
Source: Journal of Materials Chemistry A (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Towards predictive design of electrolyte solutions by accelerating <i>ab initio</i> simulation with neural networks Ab initio molecular dynamics can be massively accelerated using equivariant neural networks applicable to predict the properties of electrolyte solutions for predictive design in materials applications. Ab initio ; Electrolyte ; Artificial neural network ; Computer science ; Massively parallel ; Computational chemistry ; Materials science ; Chemistry ; Artificial intelligence ; Physical chemistry ; Parallel computing ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: Jeonghee Jo | Bumju Kwak | Byunghan Lee | Sungroh Yoon
Source: ACS Omega (article, 2022-01-27)
Field: Materials Science — Materials Chemistry

Flexible Dual-Branched Message-Passing Neural Network for a Molecular Property Prediction A molecule is a complex of heterogeneous components, and the spatial arrangements of these components determine the whole molecular properties and characteristics. With the advent of deep learning in computational chemistry, several studies have focused on how to predict molecular properties based on molecular configurations. MA message-passing neural network provides an effective framework for capturing molecular geometric features with the perspective of a molecule as a graph. However, most of these studies assumed that all heterogeneous molecular features, such as atomic charge, bond length, or other geometric features, always contribute equivalently to the target prediction, regardless of the task type. In this study, we propose a dual-branched neural network for molecular property prediction based on both the message-passing framework and standard multilayer perceptron neural networks. Our model learns heterogeneous molecular features with different scales, which are trained flexibly according to each prediction target. In addition, we introduce a discrete branch to learn single-atom features without local aggregation, apart from message-passing steps. We verify that this novel structure can improve the model performance. The proposed model outperforms other recent models with sparser representations. Our experimental results indicate that, in the chemical property prediction tasks, the diverse chemical nature of targets should be carefully considered for both model performance and generalizability. Finally, we provide the intuitive analysis between the experimental results and the chemical meaning of the target. Computer science ; Artificial neural network ; Message passing ; Molecular graph ; Property (philosophy) ; Generalizability theory ; Molecular communication ; Dual (grammatical number) ; Artificial intelligence ; Task (project management) ; Graph ; Theoretical computer science ; Machine learning ; Algorithm ; Channel (broadcasting) ; Mathematics ; Distributed computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Abdelazim M. Mebed | Hasnain Mehdi Jafri | Ahmad Hakamy | Alaa M. Abd‐Elnaiem | Muhammad Sulaman | Sayed Elshahat
Source: International Journal of Quantum Chemistry (article, 2022-08-06)
Field: Materials Science — Materials Chemistry

Multidimensional modeling assisted mining of <scp>GDB17</scp> chemical database: A search for polymer donors for organic solar cells and machine learning assisted performance prediction Abstract Indeed, the designing of efficient material for organic solar cells is challenging and time‐consuming task. In this work, we have proposed a design pipeline to screen efficient polymer donors. Machine learning based models are trained to predict the power conversion efficiency (PCE). Structural descriptors are used as input to train machine learning models. The data of organic molecules is extracted from Photovoltaics database. Best buildings blocks are selected on the basis of similarity analysis using PBT7‐Th and MP6 as reference (standard). New polymers are designed using building blocks having high similarity. The PCE is predicted with the help of high‐performance machine learning model. Top three candidates are studied by molecular dynamics (MD) simulations. Packing behavior of polymer donors and their blend with fullerene acceptor is studied using radial distribution function (RDF). Density functional theory analysis is also performed on selected polymers. Electrostatic potential analysis has indicated the highly polar behavior that can result higher charge generation. Our proposed pipeline has ability to design and screen the polymer donors, as well as can predict their PCE without any experimentation. More importantly computational cost is marginal. Organic solar cell ; Pipeline (software) ; Computer science ; Similarity (geometry) ; Artificial intelligence ; Polymer ; Machine learning ; Materials science Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Qi Yuan | Filip Szczypiński | Kim E. Jelfs
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Explainable graph neural networks for organic cages The development of accurate and explicable machine learning models to predict the properties of topologically complex systems is a challenge in materials science. Porous organic cages, a class of polycyclic molecular materials, have potential application in molecular separations, catalysis and encapsulation. For most applications of porous organic cages, having a permanent internal cavity in the absence of solvent, a property termed "shape persistence" is critical. Here, we report the development of Graph Neural Networks (GNNs) to predict the shape persistence of organic cages. Graph neural networks are a class of neural networks where the data, in our case that of organic cages, are represented by graphs. The performance of the GNN models was measured against a previously reported computational database of organic cages formed through a range of [4 + 6] reactions with a variety of reaction chemistries. The reported GNNs have an improved prediction accuracy and transferability compared to random forest predictions. Apart from the improvement in predictive power, we explored the explicability of the GNNs by computing the integrated gradient of the GNN input. The contribution of monomers and molecular fragments to the shape persistence of the organic cages could be quantitatively evaluated with integrated gradients. With the added explicability of the GNNs, it was possible not only to accurately predict the property of organic materials, but also to interpret the predictions of the deep learning models and provide structural insights for the discovery of future materials. Artificial neural network ; Computer science ; Graph ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes
Authors: Quan Zhang | Ru Bai | Bo Peng | Zhen Wang | Yangyi Liu
Source: Micron (article, 2022-12-29)
Field: Materials Science — Materials Chemistry

FFT pattern recognition of crystal HRTEM image with deep learning High-resolution transmission electron microscopy ; Fast Fourier transform ; Computer science ; Artificial intelligence ; Image processing ; Computer vision ; Optics ; Fourier transform ; Materials science ; Pattern recognition (psychology) ; Diffraction ; Physics ; Algorithm ; Image (mathematics) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography