jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthew Turner | Nicholas Langellier | Rachel Bainbridge | Dan Walters | Srujan Meesala | Thomas M. Babinec | Pauli Kehayias | Amir Yacoby | Evelyn L. Hu | Marko Lončar | Ronald L. Walsworth | Edlyn V. Levine
Source: arXiv (Cornell University) (article, 2020-04-07)
Field: Materials Science — Materials Chemistry

Magnetic Field Fingerprinting of Integrated Circuit Activity with a Quantum Diamond Microscope Current density distributions in active integrated circuits (ICs) result in patterns of magnetic fields that contain structural and functional information about the IC. Magnetic fields pass through standard materials used by the semiconductor industry and provide a powerful means to fingerprint IC activity for security and failure analysis applications. Here, we demonstrate high spatial resolution, wide field-of-view, vector magnetic field imaging of static (DC) magnetic field emanations from an IC in different active states using a Quantum Diamond Microscope (QDM). The QDM employs a dense layer of fluorescent nitrogen-vacancy (NV) quantum defects near the surface of a transparent diamond substrate placed on the IC to image magnetic fields. We show that QDM imaging achieves simultaneous $\sim10$ $μ$m resolution of all three vector magnetic field components over the 3.7 mm $\times$ 3.7 mm field-of-view of the diamond. We study activity arising from spatially-dependent current flow in both intact and decapsulated field-programmable gate arrays (FPGAs); and find that QDM images can determine pre-programmed IC active states with high fidelity using machine-learning classification methods. Integrated circuit ; Magnetic field ; Diamond ; Microscope ; Materials science ; Quantum ; Field (mathematics) ; Optoelectronics ; Nanotechnology ; Computer science ; Physics ; Optics ; Mathematics Diamond and Carbon-based Materials Research ; Integrated Circuits and Semiconductor Failure Analysis ; Advanced Surface Polishing Techniques
Authors: S.A. Korchagin | Yuri Vasilievich Klinaev | Д. В. Терин | Sergey Petrovich Romanchuk
Source:  (article, 2020-09-24)
Field: Materials Science — Materials Chemistry

Modeling the Dielectric Constant of Silicon-Based Nanocomposites Using Machine Learning In this work, we solve the problem of predicting the dielectric constant of silicon-based nanocomposites using machine learning methods. Mathematical models and programs have been developed to predict the electrophysical properties of new materials, as well as to select the optimal composition. The results obtained will be useful for faster and cheaper creation of new functional composites. Dielectric ; Nanocomposite ; Constant (computer programming) ; Silicon ; Materials science ; Work (physics) ; Computer science ; High-κ dielectric ; Composite material ; Electronic engineering ; Mechanical engineering ; Engineering physics ; Optoelectronics ; Engineering Carbon Nanotubes in Composites ; Silicon Nanostructures and Photoluminescence ; Semiconductor materials and interfaces
Authors: Cyril ALİYEGBENOMA | Mercy OZAKPOLOR
Source: International Journal of Engineering and Innovative Research (article, 2020-10-22)
Field: Materials Science — Polymers and Plastics

OPTIMISATION OF INJECTION MOULDED POLYPROPYLENE SAWDUST COMPOSITE USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORKS This study focuses on the optimisation of the injection moulded Polypropylene -Sawdust (PP-sawdust) composite. The PP material and sawdust were mixed together to form a homogenous mixture with various percentage composition by volume as recommended by the design of experiments using the central composite design (CCD). The two screw plunger injection moulding machine was used to produce Polypropylene-Sawdust (PP-Sawdust) composite at various temperature. The produced composites were evaluated for their mechanical properties which included tensile strength, proof stress, percentage elongation and flexural strength. The response surface methodology (RSM) and artificial neural networks (ANN) were used to determine the effect of the interaction of temperature, material type and percentage by volume of material on the mechanical properties of the produced PP-sawdust composite. The models were validated using coefficient of determination (R2). The models were validated using coefficient of determination (R2). The coefficient of determination (R2) obtained ranged from 0.9435 (94.357%) to 0.9988 (99.88%) which indicates that a substantial good fit was achieved by the model developed. A desirability of 0.952 was obtained which shows the adequacy of the model terms The optimization results for PP-Sawdust composites shows that the tensile strength, proof stress, flexural strength and flexural modulus were maximized with values of 31.90 MPa, 41.94 MPa, 88.22 MPa and 2.72 GPa respectively obtained at barrel temperature of 224.65 oC and polymer level of 45.56% while percentage elongation and average deflection were minimized with values of 74.12% and 6.46 cm respectively Sawdust ; Polypropylene ; Composite material ; Ultimate tensile strength ; Materials science ; Flexural strength ; Composite number ; Flexural modulus ; Response surface methodology ; Izod impact strength test ; Central composite design ; Pulp and paper industry ; Chromatography ; Chemistry Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Asphalt Pavement Performance Evaluation
Authors: Huichao Lv | Dayong Tian
Source: Chinese Journal of Chemical Engineering (article, 2020-09-25)
Field: Chemistry — Spectroscopy

Designing and optimizing a parallel neural network model for predicting the solubility of diosgenin in n-alkanols Diosgenin ; Solubility ; Artificial neural network ; Computer science ; Chemistry ; Process engineering ; Artificial intelligence ; Organic chemistry ; Engineering Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Chemical and Physical Properties in Aqueous Solutions
Authors: S. Meenakshi Sundaram | G.D. Janaki Ram | Murugaiyan Amirthalingam
Source: Welding in the World (article, 2020-10-07)
Field: Materials Science — Materials Chemistry

Development of shielded metal arc welding electrodes to achieve carbide-free bainitic weld microstructures Materials science ; Shielded metal arc welding ; Bainite ; Carbide ; Metallurgy ; Microstructure ; Welding ; Submerged arc welding ; Austenite ; Ferrite (magnet) ; Alloy ; Arc welding ; Electrode ; Composite material ; Gas metal arc welding Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Advanced materials and composites
Authors: Sergey Gusarov | Stanislav R. Stoyanov | Samira Siahrostami
Source: The Journal of Physical Chemistry (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Development of Fukui Function Based Descriptors for a Machine Learning Study of CO₂ Reduction Developing novel methods that capture chemical properties quickly and with reasonable accuracy has emerged as an attractive way to replace time-consuming density functional theory (DFT) calculations. In this study, we propose a new type of machine learning (ML) enhanced descriptors based on the Fukui function (FF) projected onto the Connolly surface. The FF contains information about the local system’s response to the perturbation and could be used as a descriptor of the chemical properties of a surfaces. We show that the FF, augmented by a general characteristic of the electronic structure of the surface, such as a work function, is well correlated to the mapped adsorption energy of CO. Therefore, this combination might replace the computationally expensive mapping of the adsorption energy of small molecules as an indicator of catalytic activity. Potential extensions of the proposed methodology are briefly discussed. Chemistry ; Fukui function ; Adsorption ; Density functional theory ; Molecule ; Perturbation theory (quantum mechanics) ; Function (biology) ; Reduction (mathematics) ; Surface (topology) ; Biological system ; Energy (signal processing) ; Computational chemistry ; Chemical physics ; Statistical physics ; Catalysis ; Physical chemistry ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Complex Network Analysis Techniques ; CO2 Reduction Techniques and Catalysts
Authors: Syamsul Rizal | Nor Kumalasari Caecar Pratiwi | Nur Ibrahim | Hurianti Vidya | Sofia Saidah | R Yunendah Nur Fuadah
Source: JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING (article, 2020-02-29)
Field: Chemistry — Analytical Chemistry

TEA LEAVES GMB SERIES CLASIFFICATION USING CONVOLUTIONAL NEURAL NETWORK This study classifies GMB series tea leaves by using a convolutional neural network as a classification system. GMB series tea are the superior tea seeds in Indonesia. Gambung series, namely: GMB 1 to GMB 11, are planting material seeds that have been recommended by the Ministry of Agriculture. The potential of these tea series yield of 4,000 - 5,800 kg / ha of dried tea. The morphological similarity level of GMB 1 to GMB 11 is very high, because many elders from the clones are from the same crossing parents. During this time, the process of identifying GMB clones 1 through GMB 11 is done manually using the visual eye of an experts at PPTK Gambung. These experts are limited to be able to identify each tea series. This process is susceptible to errors in the reading of clone types, and is very dependent on the presence of the experts. If an error occurs in the process of identifying the type of clone, it will interfere with the nursery process. Errors in the selection of recommended clones will harm the process of a long period of time, because the economic age of tea plants can reach until 50 years. The potential loss of production due to misuse of plant material can reach 1,200 kg / ha per year. Against the background of these problems, it is very necessary to have a system to identify the GMB series clone. Continuous studies has been conducted to build an automation system for the identification and classification of GMB series tea clones. The system is designed using the Convolutional Neural Network (CNN) method. The results obtained from this system output in the form of accuracy with a value of 85%. clone (Java method) ; Computer science ; Biology ; Gene Spectroscopy and Chemometric Analyses ; Food and Agricultural Sciences ; GABA and Rice Research
Authors: Wojciech Płaziński | Anita Płazińska | Agnieszka Brzyska
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Efficient sampling of high-energy states by machine learning force fields Regarding their application in the field of molecular sciences, machine learning (ML) methods are capable of combining the high accuracy of ab initio potentials with an efficiency closer to that of classical molecular mechanics. By relying on the reference data (e.g., atomic configurations and corresponding energies), the ML algorithms can reconstruct the potential energy surface for simple molecular systems, which may subsequently serve as a computationally inexpensive force field. The accuracy of such an ML force field is highly dependent on the character of the dataset that was used for its training. In this work, we show that omitting the high-energy states, which results from following the Boltzmann distribution, may lead to a catastrophic loss of accuracy in certain regions of the configurational phase space. To overcome this challenge, we have proposed an alternative solution for generating the ML input data. The most essential step is the biased subsampling of the configurations, aimed at increasing the population of hardly accessible states, usually located on energy barriers. The applicability of the proposed procedure is demonstrated on the example of conformational rearrangements in the two flexible, heterocyclic molecules. This approach provides an essential component required to obtain the ML force fields, accurate within the whole configurational phase space of the system. Sampling (signal processing) ; Force field (fiction) ; Energy (signal processing) ; Field (mathematics) ; Computer science ; Artificial intelligence ; Engineering physics ; Physics ; Mathematics ; Telecommunications ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Timotheos Savva | Maurizio Albano
Source:  (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

Development of a Deep Learning Software for Visual Analysis of High Voltage Insulators Silicone rubber insulators have been favoured over conventional insulators to be adopted on high voltage power systems due to their hydrophobic properties and better performance in polluted environments. However, dry-bands and discharges can still be initiated on the polluted surfaces of polymeric insulators causing surface degradation and lifetime reduction. This paper presents a new procedure that identifies and assesses location and frequency of electrical stresses such as discharges and partial arcs on high voltage insulator surface analyzing visual recordings. The procedure is based on image analysis and deep learning techniques to be fully automatic and to minimize user intervention. Also, a MATLAB GUI was developed to provide a fast and user-friendly control of all steps of the analysis and its results. Silicone rubber ; Insulator (electricity) ; High voltage ; Computer science ; Software ; MATLAB ; Voltage ; Materials science ; Degradation (telecommunications) ; Electrical engineering ; Optoelectronics ; Engineering ; Composite material ; Telecommunications High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Infrastructure Maintenance and Monitoring
Authors: Ali Raza | Árni Sturluson | Cory M. Simon | Xiaoli Z. Fern
Source: ChemRxiv (preprint, 2020-05-14)
Field: Chemistry — Inorganic Chemistry

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available. Partial charge ; Computer science ; Charge (physics) ; Artificial neural network ; Fidelity ; Computation ; Constraint (computer-aided design) ; Set (abstract data type) ; Chemical physics ; Nanotechnology ; Materials science ; Chemistry ; Physics ; Algorithm ; Artificial intelligence ; Quantum mechanics ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Meng Zhan | Sheliang Wang | Lizhen Zhang | Zhongfei Chen
Source: Journal of Materials in Civil Engineering (article, 2020-02-25)
Field: Materials Science — Materials Chemistry

Experimental Evaluation of Smart Composite Device with Shape Memory Alloy and Piezoelectric Materials for Energy Dissipation Based on the superelasticity of shape memory alloy (SMA) and the electrodeformation of a piezoelectric transition (PZT) ceramic, a novel SMA/PZT composite control device (SPCCD) was designed and its energy dissipation performance and neural network constitutive model investigated. The composite control device is composed of a variable friction unit that contains four rectangular PZT actuators and an SMA unit that includes energy-dissipation and resettable wires. The friction force can be adjusted in real time by applying the voltage to the PZT actuators and the SMA wires can dissipate energy via hysteresis and provide a reset force. This composite control device’s different components participate in energy dissipation at different seismic intensities. Electro-mechanical tests are conducted to evaluate the performance of the SPCCD with different displacement amplitudes, loading/unloading frequencies, and excitation voltages. Correspondingly, the force-displacement curves are acquired, and the influences of the lap hysteretic energy dissipation, equivalent stiffness, and equivalent damping ratio on the energy dissipation capacity of the SPCCD are analyzed. Then, the excitation voltage and loading history are considered as neuronal input to establish a back-propagation (BP) neural network model of the SPCCD. The experimental results show that the hysteresis curves of the SPCCD are approximately symmetrical and the loading/unloading frequency has little effect on the mechanical properties. The maximum control force of the SPCCD linearly increases with an increase in the excitation voltage, and its semiactive PZT unit has a large adjustable range for energy dissipation. Thus, the SPCCD is reasonably designed. Moreover, the BP network constitutive model is able to accurately track the output of the SPCCD, providing an effective means for the establishment and application of the constitutive model to SMA-based composite control devices. Dissipation ; Materials science ; Shape-memory alloy ; Hysteresis ; Actuator ; Piezoelectricity ; Voltage ; Stiffness ; SMA* ; Pseudoelasticity ; Smart material ; Composite material ; Structural engineering ; Electrical engineering ; Engineering Shape Memory Alloy Transformations ; Structural Engineering and Vibration Analysis ; Vibration Control and Rheological Fluids
Authors: Özlem Kışlal Saraçoğlu | Mecit Orhan Uludağ | Esma Kaya Özdemir | İsmail Tuncer Değim
Source: Brazilian Journal of Pharmaceutical Sciences (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Development of controlled release dexketoprofen tablets and prediction of drug release using Artificial Neural Network (ANN) modelling Dexketoprofen trometamol (DT) is an active S (+) enantiomer of ketoprofen, and a non-steroidal antiinflammatory agent. DT has a short biological half-life and the dosing interval is quite short when there is a need to maintain the desirable effect for longer time periods. Consequently, a controlled release DT tablet was designed for oral administration aiming to minimize the number of doses and the possible side effects. Calculations of the parameters for controlled release DT tablets were shown clearly. Controlled release matrix-type tablet formulations were prepared using hydroxypropyl methylcellulose (HPMC) (low and high viscosity), Eudragit RS and Carbopol, and the effects of different polymers on DT release from the tablet formulations were investigated. The dissolution rate profiles were compared and analyzed kinetically. An Artificial Neural Network (ANN) model was developed to predict drug release and a successful model was obtained. Subsequently, an optimum formulation was selected and evaluated in terms of its analgesic and anti-inflammatory activity. Although the developed controlled release tablets did not have an initial dose, they were found to be as effective as commercially available tablets on the market. Dissolution and in vivo studies have shown that the prepared tablets were able to release DT for longer time periods, making the tablets more effective, convenient and more tolerable. Artificial neural network ; Chemistry ; Drug ; Pharmacology ; Computer science ; Artificial intelligence ; Medicine Analytical Methods in Pharmaceuticals ; Drug Solubulity and Delivery Systems ; Computational Drug Discovery Methods
Authors: Sangwon Lee | Baekjun Kim | Jihan Kim
Source: ChemRxiv (preprint, 2020-05-20)
Field: Chemistry — Inorganic Chemistry

Discovery of Record-Breaking Metal-Organic Frameworks for Methane Storage using Evolutionary Algorithm and Machine Learning In the past decade, there has been a rise in a number of computational screening works to facilitate finding optimal metal-organic frameworks (MOF) for variety of different applications. Unfortunately, most of these screening works are limited to its initial set of materials and result in brute-force type of a screening approach. In this work, we present a systematic strategy that can find materials with desired property from an extremely diverse and large MOF set of over 100 trillion possible MOFs using machine learning and evolutionary algorithm. It is demonstrated that our algorithm can discover 964 MOFs with methane working capacity over 200 cm3 cm−3 and 96 MOFs with methane working capacity over 208 cm3 cm−3, which is the current world record. We believe that this methodology can facilitate a new type of a screening approach that takes advantage of the modular nature in MOFs, and can readily be extended to other important applications as well. Computer science ; Modular design ; Metal-organic framework ; Set (abstract data type) ; Methane ; Variety (cybernetics) ; Evolutionary algorithm ; Property (philosophy) ; Algorithm ; Machine learning ; Artificial intelligence ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Catalytic Processes in Materials Science
Authors: K. Srinivasa Rao | Murali Krishna Talari | Rajendra Kumar Pedapudi | Sai Charan Akula
Source: International Journal of Advance Research and Innovation (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Classification of Rice Grains using Machine Learning Techniques Classification of rice grains is important for human beings as it directly impacts the human health. Hence there is a great need to measure the quality of rice grains and identifying the adulteration and analysing the grains manually is more time consuming and complicated process and having more chances of errors with the subjectivity of the human perception. In order to achieve the uniform standard quality and precision, machine learning techniques are evolved.Rice quality is nothing but the combination of physical and chemical characteristics grain size, shape and colour are some physical characteristics. This paper obtained all physical features and classification of rice grains using SVM and CNN. By implementing these two and comparing both SVM and CNN outputs, identifyingwhich technique will perform classification efficiently. Support vector machine ; Computer science ; Artificial intelligence ; Machine learning ; Process (computing) ; Pattern recognition (psychology) ; Quality (philosophy) ; Human health Spectroscopy and Chemometric Analyses ; GABA and Rice Research