jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chun-I Wang | Ignasius Joanito | Chang-Feng Lan | Chao‐Ping Hsu
Source: The Journal of Chemical Physics (article, 2020-12-04)
Field: Materials Science — Materials Chemistry

Artificial neural networks for predicting charge transfer coupling Quantum chemistry calculations have been very useful in providing many key detailed properties and enhancing our understanding of molecular systems. However, such calculation, especially with ab initio models, can be time-consuming. For example, in the prediction of charge-transfer properties, it is often necessary to work with an ensemble of different thermally populated structures. A possible alternative to such calculations is to use a machine-learning based approach. In this work, we show that the general prediction of electronic coupling, a property that is very sensitive to intermolecular degrees of freedom, can be obtained with artificial neural networks, with improved performance as compared to the popular kernel ridge regression method. We propose strategies for optimizing the learning rate and batch size, improving model performance, and further evaluating models to ensure that the physical signatures of charge-transfer coupling are well reproduced. We also address the effect of feature representation as well as statistical insights obtained from the loss function and the data structure. Our results pave the way for designing a general strategy for training such neural-network models for accurate prediction. Computer science ; Artificial neural network ; Degrees of freedom (physics and chemistry) ; Charge (physics) ; Coupling (piping) ; Artificial intelligence ; Kernel (algebra) ; Representation (politics) ; Machine learning ; Transfer of learning ; Ab initio ; Transfer function ; Feature (linguistics) ; Statistical physics ; Physics ; Materials science ; Mathematics ; Quantum mechanics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Yun Zhang | Xiaojie Xu
Source: Journal of Low Temperature Physics (article, 2020-11-10)
Field: Materials Science — Materials Chemistry

Fe-Based Superconducting Transition Temperature Modeling through Gaussian Process Regression Superconductivity ; Materials science ; Gaussian ; Statistical physics ; Condensed matter physics ; Lattice (music) ; Transition temperature ; Work (physics) ; Refrigeration ; Computer science ; Thermodynamics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Iron-based superconductors research ; Physics of Superconductivity and Magnetism
Authors: Shi Chen | Kazuyuki Demachi
Source: Applied Sciences (article, 2020-07-26)
Field: Materials Science — Materials Chemistry

A Vision-Based Approach for Ensuring Proper Use of Personal Protective Equipment (PPE) in Decommissioning of Fukushima Daiichi Nuclear Power Station Decommissioning of the Fukushima Daiichi nuclear power station (NPS) is challenging due to industrial and chemical hazards as well as radiological ones. The decommissioning workers in these sites are instructed to wear proper Personal Protective Equipment (PPE) for radiation protection. However, workers may not be able to accurately comply with safety regulations at decommissioning sites, even with prior education and training. In response to the difficulties of on-site PPE management, this paper presents a vision-based automated monitoring approach to help to facilitate the occupational safety monitoring task of decommissioning workers to ensure proper use of PPE by the combination of deep learning-based individual detection and object detection using geometric relationships analysis. The performance of the proposed approach was experimentally evaluated, and the experimental results demonstrate that the proposed approach is capable of identifying decommissioning workers’ improper use of PPE with high precision and recall rate while ensuring real-time performance to meet the industrial requirements. Nuclear decommissioning ; Personal protective equipment ; Nuclear power ; Radiological weapon ; Engineering ; Risk analysis (engineering) ; Construction engineering ; Waste management ; Business ; Medicine ; Coronavirus disease 2019 (COVID-19) Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies
Authors: Prabu Ravindran | Blaise J. Thompson | Richard Soares | Alex C. Wiedenhoeft
Source: Frontiers in Plant Science (article, 2020-07-10)
Field: Chemistry — Organic Chemistry

The XyloTron: Flexible, Open-Source, Image-Based Macroscopic Field Identification of Wood Products Forests, estimated to contain two thirds of the world's biodiversity, face existential threats due to illegal logging and land conversion. Efforts to combat illegal logging and to support sustainable value chains are hampered by a critical lack of affordable and scalable technologies for field-level inspection of wood and wood products. To meet this need we present the XyloTron, a complete, self-contained, multi-illumination, field-deployable, open-source platform for field imaging and identification of forest products at the macroscopic scale. The XyloTron platform integrates an imaging system built with off-the-shelf components, flexible illumination options with visible and UV light sources, software for camera control, and deep learning models for identification. We demonstrate the capabilities of the XyloTron platform with example applications for automatic wood and charcoal identification using visible light and human-mediated wood identification based on ultra-violet illumination and discuss applications in field imaging, metrology, and material characterization of other substrates. Identification (biology) ; Scalability ; Computer science ; Field (mathematics) ; Environmental science ; Software ; Remote sensing ; Logging ; Scale (ratio) ; Artificial intelligence ; Geology ; Database ; Cartography ; Ecology Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: Fang Liu | Chenru Duan | Heather J. Kulik
Source: The Journal of Physical Chemistry Letters (article, 2020-08-31)
Field: Materials Science — Materials Chemistry

Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening Despite its widespread use in chemical discovery, approximate density functional theory (DFT) is poorly suited to many targets, such as those containing open-shell, 3d transition metals that can be expected to have strong multireference (MR) character. For discovery workflows to be predictive, we need automated, low-cost methods that can distinguish the regions of chemical space where DFT should be applied from those where it should not. We curate more than 4800 open-shell transition-metal complexes up to hundreds of atoms in size from prior high-throughput DFT studies and evaluate affordable, finite-temperature DFT fractional occupation number (FON)-based MR diagnostics. We show that intuitive measures of strong correlation (i.e., the HOMO–LUMO gap) are not predictive of MR character as judged by FON-based diagnostics. Analysis of independently trained machine learning (ML) models to predict HOMO–LUMO gaps and FON-based diagnostics reveals differences in the metal and ligand sensitivity of the two quantities. We use our trained ML models to rapidly evaluate MR character over a space of ∼187000 theoretical complexes, identifying large-scale trends in spin-state-dependent MR character and finding small HOMO–LUMO gap complexes while ensuring low MR character. Character (mathematics) ; HOMO/LUMO ; Density functional theory ; Chemical space ; Correlation ; Computer science ; Artificial intelligence ; Throughput ; Parameter space ; Machine learning ; Physics ; Statistical physics ; Materials science ; Chemistry ; Computational chemistry ; Mathematics ; Quantum mechanics ; Drug discovery ; Molecule ; Statistics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Soumya Banerjee | Ek Adhikari | Pitambar Sapkota | Amal Sebastian | Sylwia Ptasińska
Source: Materials (review, 2020-06-30)
Field: Materials Science — Materials Chemistry

Atmospheric Pressure Plasma Deposition of TiO2: A Review Atmospheric pressure plasma (APP) deposition techniques are useful today because of their simplicity and their time and cost savings, particularly for growth of oxide films. Among the oxide materials, titanium dioxide (TiO<sub>2</sub>) has a wide range of applications in electronics, solar cells, and photocatalysis, which has made it an extremely popular research topic for decades. Here, we provide an overview of non-thermal APP deposition techniques for TiO<sub>2</sub> thin film, some historical background, and some very recent findings and developments. First, we define non-thermal plasma, and then we describe the advantages of APP deposition. In addition, we explain the importance of TiO<sub>2</sub> and then describe briefly the three deposition techniques used to date. We also compare the structural, electronic, and optical properties of TiO<sub>2</sub> films deposited by different APP methods. Lastly, we examine the status of current research related to the effects of such deposition parameters as plasma power, feed gas, bias voltage, gas flow rate, and substrate temperature on the deposition rate, crystal phase, and other film properties. The examples given cover the most common APP deposition techniques for TiO<sub>2</sub> growth to understand their advantages for specific applications. In addition, we discuss the important challenges that APP deposition is facing in this rapidly growing field. Deposition (geology) ; Materials science ; Nanotechnology ; Thin film ; Plasma ; Substrate (aquarium) ; Atmospheric-pressure plasma ; Optoelectronics ; Engineering physics ; Computer science ; Environmental science ; Engineering ; Physics ; Geology Catalytic Processes in Materials Science ; ZnO doping and properties ; Plasma Applications and Diagnostics
Authors: Xiaoli Liu | Ruchita Jani | Esther Orisakwe | Conrad Johnston | Piotr Chudziński | Ming Qu | Brian Norton | Niall Holmes | Jorge Kohanoff | Lorenzo Stella | Hongxi Yin | Kazuaki Yazawa
Source: Renewable and Sustainable Energy Reviews (article, 2020-11-12)
Field: Materials Science — Materials Chemistry

State of the art in composition, fabrication, characterization, and modeling methods of cement-based thermoelectric materials for low-temperature applications Thermoelectric effect ; Thermoelectric materials ; Renewable energy ; Cement ; Cementitious ; Materials science ; Electricity ; Thermoelectric generator ; Process engineering ; Environmental science ; Waste management ; Engineering ; Thermal conductivity ; Composite material ; Electrical engineering Advanced Thermoelectric Materials and Devices ; Smart Materials for Construction ; Thermal properties of materials
Authors: Sara Ferraris | Silvia Spriano | Alessandro Calogero Scalia | Andrea Cochis | Lia Rimondini | Iriczalli Cruz‐Maya | Vincenzo Guarino | Alessio Varesano | Claudia Vineis
Source: Polymers (review, 2020-12-03)
Field: Materials Science — Biomaterials

Topographical and Biomechanical Guidance of Electrospun Fibers for Biomedical Applications Electrospinning is gaining increasing interest in the biomedical field as an eco-friendly and economic technique for production of random and oriented polymeric fibers. The aim of this review was to give an overview of electrospinning potentialities in the production of fibers for biomedical applications with a focus on the possibility to combine biomechanical and topographical stimuli. In fact, selection of the polymer and the eventual surface modification of the fibers allow selection of the proper chemical/biological signal to be administered to the cells. Moreover, a proper design of fiber orientation, dimension, and topography can give the opportunity to drive cell growth also from a spatial standpoint. At this purpose, the review contains a first introduction on potentialities of electrospinning for the obtainment of random and oriented fibers both with synthetic and natural polymers. The biological phenomena which can be guided and promoted by fibers composition and topography are in depth investigated and discussed in the second section of the paper. Finally, the recent strategies developed in the scientific community for the realization of electrospun fibers and for their surface modification for biomedical application are presented and discussed in the last section. Electrospinning ; Nanotechnology ; Fiber ; Synthetic fiber ; Materials science ; Polymer ; Computer science ; Biochemical engineering ; Composite material ; Engineering Electrospun Nanofibers in Biomedical Applications ; Silk-based biomaterials and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Chanh-Nghiem Nguyen | Quoc-Thang Phan | Nhut-Thanh Tran | Masayuki Fukuzawa | Phuoc-Loc Nguyen | Chi-Ngon Nguyen
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Precise Sweetness Grading of Mangoes (Mangifera indica L.) Based on Random Forest Technique With Low-Cost Multispectral Sensors Before being exported, mangoes generally undergo rigorous external and internal quality inspection processes in which near-infrared (NIR) spectral approaches are favorable for grading purposes. A successful NIR-based grading system depends largely on high-quality spectral sensors and the reliability of the classifier. Motivated by the high economic impact of Cat Hoa Loc mangoes (Mangifera indica L.), we demonstrated that the sweetness of that mangoes could be precisely graded based on a random forest (RF) classifier in a three-phase approach with a low-cost Visible-Near infrared (VIS-NIR) multispectral sensor chipset. This approach is so-called RPR because RF, Partial Least Squares regression, and RF were respectively applied to consecutively determine the significant VIS-NIR responses, the good features as input variables, and the reliable RF classifier via our formulated discriminant index (DI). The experimental results confirmed that higher classification accuracy was achieved by using the extracted latent features rather than the raw VIS-NIR data. The DI was effectively used as a reliability measure to select the optimal classifier among those of identical training and testing accuracies of 100% and 82.1%, respectively. Performance comparison between the optimal RF classifier with a Support Vector Machines classifier and a multinomial logistic regression showed that the developed RF classifier was superior in various performance indices. Therefore, it is promising to extend the proposed approach to more complicated fruit grading problems with sufficient VIS-NIR datasets that are acquired from low-cost multispectral sensors. Multispectral image ; Computer science ; Random forest ; Classifier (UML) ; Pattern recognition (psychology) ; Artificial intelligence ; Preprocessor ; Support vector machine ; Mangifera ; Remote sensing ; Mathematics Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Smart Agriculture and AI
Authors: Maciej Guzik | Tomasz Witko | Alexander Steinbüchel | Magdalena Wojnarowska | Mariusz Sołtysik | Sławomir Wawak
Source: Frontiers in Bioengineering and Biotechnology (review, 2020-09-10)
Field: Materials Science — Biomaterials

What Has Been Trending in the Research of Polyhydroxyalkanoates? A Systematic Review Over the past decades, enormous progress has been achieved with regard to research on environmentally friendly polymers. One of the most prominent families of such biopolymers are bacterially synthesized polyhydroxyalkanoates (PHAs) that have been known since the 1920s. However, only as recent as the 1990s have extensive studies sprung out exponentially in this matter. Since then, different areas of exploration of these intriguing materials have been uncovered. However, no systematic review of undertaken efforts has been conducted so far. Therefore, we have performed an unbiased search of up-to-date literature to reveal trending topics in the research of PHAs over the past three decades by data mining of 2,227 publications. This allowed us to identify eight past and current trends in this area. Our study provides a comprehensive review of these trends and speculates where PHA research is heading. Polyhydroxyalkanoates ; Data science ; Computer science ; Biology ; Genetics biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Advanced Polymer Synthesis and Characterization
Authors: Sophia Mayr | Krzysztof B. Beć | Justyna Grabska | Verena Wiedemair | Verena Pürgy | Michael Popp | Günther K. Bonn | Christian W. Huck
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2020-12-13)
Field: Chemistry — Analytical Chemistry

Challenging handheld NIR spectrometers with moisture analysis in plant matrices: Performance of PLSR vs. GPR vs. ANN modelling The global demand for natural products grows rapidly, intensifying the request for the development of high-throughput, fast, non-invasive tools for quality control applicable on-site. Moisture content is one of the most important quality parameters of natural products. It determines their market suitability, stability and shelf life and should preferably be constantly monitored. Miniaturized near-infrared (NIR) spectroscopy is a powerful method for on-site analysis, potentially fulfilling this requirement. Here, a feasibility study for applicability and analytical performance of three miniaturized NIR spectrometers and two benchtop instruments was evaluated in that scenario. The case study involved 192 dried plant extracts composed of five different plants harvested in different countries at various times within two years. The reference analysis by Karl Fischer titration determined the water content in this sample set between 1.36% and 6.47%. For the spectroscopic analysis half of the samples were laced with a drying agent to comply with the industry standard. The performance of various calibration models for NIR analysis was evaluated on the basis of root-mean square error of prediction (RMSEP) determined for an independent test set. Partial least squares regression (PLSR), Gaussian process regression (GPR) and artificial neural network (ANN) models were constructed for the spectral sets from each instrument. GPR and ANN models performed superior for all samples measured by handheld spectrometers and for native ones analyzed by benchtop instruments. Moreover, the accuracy penalty when analyzing native samples was lower for GPR and ANN prediction as well. With GPR or ANN calibration, miniaturized spectrometers offered the prediction performance at the level of the benchtop instruments. Therefore, in this analytical application miniaturized spectrometers can be used on-site with no penalty to the performance vs. laboratory-based NIR analysis. Partial least squares regression ; Calibration ; Spectrometer ; Near-infrared spectroscopy ; Mean squared error ; Artificial neural network ; Water content ; Chemometrics ; Ground-penetrating radar ; Environmental science ; Remote sensing ; Computer science ; Biological system ; Process engineering ; Mathematics ; Artificial intelligence ; Statistics ; Engineering ; Machine learning ; Radar ; Optics ; Physics ; Telecommunications ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Pesticide Residue Analysis and Safety
Authors: Roland Tóbiás | Tibor Furtenbacher | Irén Simkó | Attila G. Császár | Meissa L. Diouf | Frank M. J. Cozijn | J. Staa | E. J. Salumbides | W. Ubachs
Source: Nature Communications (article, 2020-04-06)
Field: Chemistry — Spectroscopy

Spectroscopic-network-assisted precision spectroscopy and its application to water Frequency combs and cavity-enhanced optical techniques have revolutionized molecular spectroscopy: their combination allows recording saturated Doppler-free lines with ultrahigh precision. Network theory, based on the generalized Ritz principle, offers a powerful tool for the intelligent design and validation of such precision-spectroscopy experiments and the subsequent derivation of accurate energy differences. As a proof of concept, 156 carefully-selected near-infrared transitions are detected for H<sub>2</sub><sup>16</sup>O, a benchmark system of molecular spectroscopy, at kHz accuracy. These measurements, augmented with 28 extremely-accurate literature lines to ensure overall connectivity, allow the precise determination of the lowest ortho-H<sub>2</sub><sup>16</sup>O energy, now set at 23.794 361 22(25) cm<sup>-1</sup>, and 160 energy levels with similarly high accuracy. Based on the limited number of observed transitions, 1219 calibration-quality lines are obtained in a wide wavenumber interval, which can be used to improve spectroscopic databases and applied to frequency metrology, astrophysics, atmospheric sensing, and combustion chemistry. Spectroscopy ; Calibration ; Metrology ; Benchmark (surveying) ; Molecular spectroscopy ; Energy (signal processing) ; Accuracy and precision ; Computer science ; Set (abstract data type) ; Materials science ; Computational physics ; Analytical Chemistry (journal) ; Biological system ; Optics ; Chemistry ; Physics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Advanced Chemical Sensor Technologies
Authors: Kunal Sandip Garud | Jae‐Hyeong Seo | Chongpyo Cho | Moo‐Yeon Lee
Source: Symmetry (article, 2020-02-08)
Field: Materials Science — Materials Chemistry

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery The present study elaborates the suitability of the artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) to predict the thermal performances of the thermoelectric generator system for waste heat recovery. Six ANN models and seven ANFIS models are formulated by considering hot gas temperatures and voltage load conditions as the inputs to predict current, power, and thermal efficiency of the thermoelectric generator system for waste heat recovery. The ANN model with the back-propagation algorithm, the Levenberg–Marquardt variant, Tan-Sigmoidal transfer function and 25 number of hidden neurons is found to be an optimum model to accurately predict current, power and thermal efficiency. For current, power and thermal efficiency, the ANFIS model with pi-5 or gauss-5-membership function is recommended as the optimum model when the prediction accuracy is important while the ANFIS model with gbell-3-membership function is suggested as the optimum model when the prediction cost plays a crucial role along with the prediction accuracy. The proposed optimal ANN and ANFIS models present higher prediction accuracy than the coupled numerical approach. Adaptive neuro fuzzy inference system ; Neuro-fuzzy ; Artificial neural network ; Thermoelectric generator ; Computer science ; Waste heat recovery unit ; Thermal ; Power (physics) ; Control theory (sociology) ; Fuzzy logic ; Thermoelectric effect ; Fuzzy control system ; Engineering ; Artificial intelligence ; Mechanical engineering ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Environmental Education and Sustainability