jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xin-Zhuo Zhou | Chao-Chao Du | Gang Bi
Source: AIP Advances (article, 2023-05-01)
Field: Chemistry — Spectroscopy

Optical gain calculation and structure optimization of type-II GaAsSb/InAs/InGaAs quantum well heterostructures This paper reports an optimization scheme of type-II GaAsSb/InAs/InGaAs quantum wells using the six bands k · p method. By calculating the optical gain, the optimal structures of the quantum wells are obtained. We observed that without an electric field, the thickness values of symmetrical quantum wells designed by the AdaGrad algorithm are 2.97, 2.81, and 6.21 nm, respectively, and the gain intensity is 6489 cm−1 that is about 1600 cm−1 higher than the original empirical design scheme. The thickness of asymmetric quantum wells designed by the Adam algorithm is 5.56, 2.72, 1.88, 3.16, and 2.46 nm under the external electric field of 75 kV/cm, respectively, and the gain intensity is 5729 cm−1 that is about 2300 cm−1 higher than the original empirical design scheme. The optimized scheme effectively suppresses the gain attenuation caused by the electric field and is helpful to improve the performance of interband cascade lasers. Quantum well ; Heterojunction ; Electric field ; Cascade ; Attenuation ; Optoelectronics ; Materials science ; Quantum ; Laser ; Field (mathematics) ; Gallium arsenide ; Physics ; Optics ; Chemistry ; Mathematics ; Quantum mechanics Spectroscopy and Laser Applications ; Semiconductor Lasers and Optical Devices ; Semiconductor Quantum Structures and Devices
Authors: Stefan Hill | David Fitzek | Patrick Delfmann | Carl Corea
Source: arXiv (Cornell University) (preprint, 2023-06-30)
Field: Materials Science — Materials Chemistry

Inter-case Predictive Process Monitoring: A candidate for Quantum Machine Learning? Regardless of the domain, forecasting the future behaviour of a running process instance is a question of interest for decision makers, especially when multiple instances interact. Fostered by the recent advances in machine learning research, several methods have been proposed to predict the next activity, outcome or remaining time of a process automatically. Still, building a model with high predictive power requires both - intrinsic knowledge of how to extract meaningful features from the event log data and a model that captures complex patterns in data. This work builds upon the recent progress in inter-case Predictive Process Monitoring (PPM) and comprehensively benchmarks the impact of inter-case features on prediction accuracy. Moreover, it includes quantum machine learning models, which are expected to provide an advantage over classical models with a scaling amount of feature dimensions. The evaluation on real-world training data from the BPI challenge shows that the inter-case features provide a significant boost by more than four percent in accuracy and quantum algorithms are indeed competitive in a handful of feature configurations. Yet, as quantum hardware is still in its early stages of development, this paper critically discusses these findings in the light of runtime, noise and the risk to overfit on the training data. Finally, the implementation of an open-source plugin demonstrates the technical feasibility to connect a state-of-the-art workflow engine such as Camunda to an IBM quantum computing cloud service. Computer science ; Overfitting ; Workflow ; Machine learning ; Artificial intelligence ; IBM ; Process (computing) ; Cloud computing ; Feature (linguistics) ; Data mining ; Database Machine Learning in Materials Science ; Cloud Computing and Resource Management ; Data Stream Mining Techniques
Authors: Edgar C. Hornus | Martín A. Rodríguez
Source: Corrosion Reviews (article, 2023-01-25)
Field: Materials Science — Metals and Alloys

Effect of environmental variables and main alloying elements on the repassivation potential of Ni–Cr–Mo–(W) alloys 59 and 686 Abstract Chloride-induced crevice corrosion ( E R,CREV ) of alloys UNS N06059 and UNS N06686 was studied at different temperatures in 0.1, 1 and 10 M chloride solutions. Crevice corrosion occurred several degrees below the reported critical crevice temperatures obtained through standard immersion tests. The repassivation potential of the tested alloys as a function of temperature and chloride concentration was given by E R,CREV = ( A + BT)log[Cl − ] + CT + D for a range of environmental conditions. When temperature and chloride concentration increased E R,CREV showed a lesser dependence on the environmental variables. The repassivation potential of Ni–Cr–Mo–(W) alloys was described by a new proposed equation in terms of [Cl − ], T, Cr, Mo and W, alloys in wt%. The dependence of E R,CREV with the weight % of main alloying elements was 5–6 mV/%Cr, 17–18 mV/%Mo and ∼9 mV/%W, at 85 °C in chloride solutions. An optimal main alloying elements relationship was noted that maximizes the E R,CREV value. The optimal alloy ratio would be 1:3.3:1.65 for wt%Cr, wt%Mo and wt%W, the same factors as in the PRE equation. The optimal alloying ratio would be independent of the alloy composition since it is not a function of the content of main elements. Crevice corrosion ; Materials science ; Alloy ; Chloride ; Metallurgy ; Corrosion ; Analytical Chemistry (journal) ; Chemistry ; Environmental chemistry Hydrogen embrittlement and corrosion behaviors in metals ; High-Temperature Coating Behaviors ; Corrosion Behavior and Inhibition
Authors: Anfal Albalool
Source: International Journal of Medical Arts (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

Liquid-Liquid Phase Separation at the Surface of Dissolving Drug Salt Particles: Prediction This study aimed to show how the amount of liquid in a liquid mixture separates, and how it relates to the science of heat and energy.We did experiments to study the liquid phase separation, as well as the ability of drug-like molecules to dissolve in crystal form. The CLME equation was derived based on how heat and energy affect things, like how much substance can dissolve in a liquid, how strong the concentration is, and the temperature at which it melts [denoted as Tm]. The equation is [𝑙𝑜𝑔10 =𝑙𝑜𝑔10 −0.0095 [𝑇𝑚−310] for 310 K. The scientists tested 31 drugs by changing the pH or solvent of the substance and using lasers to see if there were any changes in how they looked. To ensure the material didn't form crystals within 10 seconds, I used a special microscope that utilizes polarized light to conduct some tests.The measured and calculated values showed a strong similarity [with a small error of 0. 40 log units]. The average error was 0. 32 log units. Dissolution ; Liquid liquid ; Chromatography ; Salt (chemistry) ; Liquid phase ; Phase (matter) ; Separation (statistics) ; Materials science ; Liquid–liquid extraction ; Chemistry ; Extraction (chemistry) ; Computer science ; Organic chemistry ; Thermodynamics ; Physics Crystallization and Solubility Studies ; Field-Flow Fractionation Techniques ; Drug Solubulity and Delivery Systems
Authors: Tobias Beck | Bernhard Gatternig | Antonio Delgado
Source: Heliyon (article, 2023-11-01)
Field: Chemistry — Analytical Chemistry

Neural network enhanced aging time measurements of diary product remaining with infrared spectroscopy The determination of the drying degree of food residues on surfaces is an important step before efficient cleaning can be achieved. To accomplish this goal, a rapid evaluation based on a neural network and non-invasive measurement technique is introduced. Two common starch-based products and various yogurts from different manufacturers are used as example contaminants to determine the aging time of dried food residue. Near-infrared spectroscopy serves as a modern and fast measurement technique for investigating food compositions. Two analysis methods were compared for processing the measured near-infrared spectral data. The raw data were analyzed using partial least squares regression in conjunction with necessary preprocessing steps. As an alternative method, three different types of neural networks are employed. The aim of this approach is to compensate for the filtering steps before regression, which are typically necessary for multivariate regression. The challenge is to measure three different types of food and obtain a reliable prediction of moisture content in order to draw conclusions about the drying time. The experiments have shown that simple flat neural networks have similar accuracy compared to conventional regression. The use of a convolutional layer in advance demonstrates a significant improvement in prediction compared to other neural networks and even manages to surpass the accuracy of PLS regression. A network with a convolutional layer can also compensate for the sometimes strong variations between food types. Partial least squares regression ; Artificial neural network ; Convolutional neural network ; Regression ; Computer science ; Preprocessor ; Multivariate statistics ; Artificial intelligence ; Regression analysis ; Biological system ; Pattern recognition (psychology) ; Mathematics ; Machine learning ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: Deqian Kong | Yuhao Huang | Jianwen Xie | Ying Wu
Source: arXiv (Cornell University) (preprint, 2023-10-05)
Field: Materials Science — Materials Chemistry

Molecule Design by Latent Prompt Transformer This paper proposes a latent prompt Transformer model for solving challenging optimization problems such as molecule design, where the goal is to find molecules with optimal values of a target chemical or biological property that can be computed by an existing software. Our proposed model consists of three components. (1) A latent vector whose prior distribution is modeled by a Unet transformation of a Gaussian white noise vector. (2) A molecule generation model that generates the string-based representation of molecule conditional on the latent vector in (1). We adopt the causal Transformer model that takes the latent vector in (1) as prompt. (3) A property prediction model that predicts the value of the target property of a molecule based on a non-linear regression on the latent vector in (1). We call the proposed model the latent prompt Transformer model. After initial training of the model on existing molecules and their property values, we then gradually shift the model distribution towards the region that supports desired values of the target property for the purpose of molecule design. Our experiments show that our proposed model achieves state of the art performances on several benchmark molecule design tasks. Computer science ; Transformer ; Algorithm ; Mathematics ; Applied mathematics ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Ilaria Gianani | Luis L. Sanchez Soto | Aaron Z. Goldberg | Marco Barbieri
Source: arXiv (Cornell University) (preprint, 2023-01-19)
Field: Chemistry — Analytical Chemistry

Efficient lineshape estimation by ghost spectroscopy Recovering the original spectral lineshapes from data obtained by instruments with extended transmission profiles is a basic tenet in spectroscopy. By using the moments of the measured lines as basic variables, we turn the problem into a linear inversion. However, when only a finite number of these moments are relevant, the rest of them act as nuisance parameters. These can be taken into account with a semiparametric model, which allows us to establish the ultimate bounds on the precision attainable in the estimation of the moments of interest. We experimentally confirm these limits with a simple ghost spectroscopy demonstration. Inversion (geology) ; Spectroscopy ; Simple (philosophy) ; Statistical physics ; Algorithm ; Mathematics ; Applied mathematics ; Physics ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Geochemistry and Geologic Mapping
Authors: Cormac Toher | Mackenzie Ridley | Kathleen Q. Tomko | David H. Olson | Stefano Curtarolo | Patrick E. Hopkins | Elizabeth J. Opila
Source: arXiv (Cornell University) (preprint, 2023-03-05)
Field: Materials Science — Ceramics and Composites

Design rules for the thermal and elastic properties of rare-earth disilicates Rare-earth silicates are the current standard material for use as environmental barrier coatings for SiC-based ceramic matrix composites as hot-section components in gas-turbine engines. Expanding the design space to all available rare-earth elements to facilitate optimizing functionality requires an understanding of systematic trends in $RE_2$Si$_2$O$_7$ properties. In this work, we combine first-principles calculations with experimental measurements of Young's modulus, coefficient of thermal expansion, and thermal conductivity for a range of different $RE_2$Si$_2$O$_7$ compositions and phases. Clear trends are observed in these properties as a function of the radius of the rare-earth cation. In the case of Young's modulus and thermal expansion, these trends also hold for multi-component systems; while the thermal conductivity of multi-component systems is noticeably lower, indicating the potential of such materials to also act as thermal barriers. These results provide design rules for developing new thermal and environmental barrier coatings with stiffness and thermal expansion engineered to match that of the substrate, while simultaneously having reduced thermal conductivity. Thermal expansion ; Thermal conductivity ; Materials science ; Thermal barrier coating ; Thermal ; Rare earth ; Ceramic ; Composite material ; Thermal conduction ; Thermodynamics ; Metallurgy ; Physics Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Thermal properties of materials
Authors: Małgorzata Kowalczyk
Source: Materials research proceedings (article, 2023-04-13)
Field: Materials Science — Materials Chemistry

Hardware implementation of Monte Carlo and RSM method for the optimization of cutting force during turning of NiTi shape memory alloy Abstract. This study was conducted to understand the exact turning of the NiTi shape memory alloy and consisted of four stages: experimental work, function modelling using RSM method, Monte Carlo method optimization and hardware implementation of Monte Carlo method . This article has the following main objectives: to develop a framework for solving machining optimization problems using the Monte Carlo method and hardware implementation of MC method. The solutions presented in this paper are important from the point of view of practical solutions related to the prediction and optimization of the cutting forces components Fc, Fp and Ff during turning of NiTi shape memory alloy. Nickel titanium ; Monte Carlo method ; Shape-memory alloy ; Computer science ; Machining ; Mechanical engineering ; Engineering ; Mathematics ; Artificial intelligence Shape Memory Alloy Transformations
Authors: Md Asif Bin Syed | Azmine Toushik Wasi | Imtiaz Ahmed
Source: arXiv (Cornell University) (preprint, 2023-09-23)
Field: Chemistry — Analytical Chemistry

ML Algorithm Synthesizing Domain Knowledge for Fungal Spores Concentration Prediction The pulp and paper manufacturing industry requires precise quality control to ensure pure, contaminant-free end products suitable for various applications. Fungal spore concentration is a crucial metric that affects paper usability, and current testing methods are labor-intensive with delayed results, hindering real-time control strategies. To address this, a machine learning algorithm utilizing time-series data and domain knowledge was proposed. The optimal model employed Ridge Regression achieving an MSE of 2.90 on training and validation data. This approach could lead to significant improvements in efficiency and sustainability by providing real-time predictions for fungal spore concentrations. This paper showcases a promising method for real-time fungal spore concentration prediction, enabling stringent quality control measures in the pulp-and-paper industry. Usability ; Computer science ; Algorithm ; Domain knowledge ; Metric (unit) ; Machine learning ; Pulp (tooth) ; Data mining ; Artificial intelligence ; Engineering ; Operations management Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding
Authors: Elias Moubarak | Seyed Mohamad Moosavi | Charithea Charalambous | Susana García | Berend Smit
Source: ChemRxiv (preprint, 2023-04-25)
Field: Chemistry — Inorganic Chemistry

A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms. For screening a large number of materials we also increasingly rely on isotherms predicted from molecular simulations. In particular, for such screening studies, it is important that the procedures to generate the data are accurate, reliable, and robust. In this work, we develop an efficient and automated workflow for a meticulous sampling of pure component isotherms. The workflow was tested on a set of metal-organic frameworks (MOFs) and proved to be reliable given different guest molecules. We show that the coupling of our workflow with the Clausius-Clapeyron relation saves CPU time, yet enables us to accurately predict pure component isotherms at the temperatures of interest, starting from a reference isotherm at a given temperature. We also show that one can accurately predict the CO2 and N2 mixture isotherms using Ideal Adsorbed Solution Theory (IAST). In particular, we show that IAST is a more reliable numerical tool to predict binary adsorption uptakes for a range of pressures, temperatures, and compositions, as it does not rely on the fitting of experimental data, which typically needs to be done with analytical models such as dual-site Langmuir (DSL). This makes IAST a more suitable and general technique to bridge the gap between adsorption (raw) data and process modelling. To demonstrate this point, we show that the ranking of materials, for a standard 3-step Temperature Swing Adsorption (TSA) process, can be significantly different depending on the thermodynamic method used to predict binary adsorption data. We show that for the design of processes that capture CO2 from low concentration (0.4%) streams, the commonly used methodology to predict mixture isotherms incorrectly assigns up to 33% of the materials as top-performing. Component (thermodynamics) ; Adsorption ; Computer science ; Workflow ; Ranking (information retrieval) ; Langmuir ; Process (computing) ; Process engineering ; Work (physics) ; Materials science ; Thermodynamics ; Chemistry ; Database ; Physical chemistry ; Physics ; Artificial intelligence ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Olga Soloveva | Sergei Solovev | Светлана Иванова | Anna Belyanina | Tatiana Kochkareva | Антон Синицын
Source: E3S Web of Conferences (article, 2023-01-01)
Field: Materials Science — General Materials Science

Mathematical modeling of the mass transfer of the drying process of a multilayer thermal insulation coating Polymer thermal insulation materials are widely used in modern industry and technological production of energy carriers. Thermal insulation with polymer coatings is one of the main ways to protect thermal equipment from temperature effects, corrosion, cavitation, erosion, and other influences, reducing the consumption of expensive materials. However, although polymeric materials can significantly reduce the cost of heat losses, their use is kept at a relatively low level. This is due to the low level of culture in the construction industry and the desire to save on projects, even at the expense of quality. The important issue of forming a reliable system “polymer sheet – adhesive film – environment” is given minimal attention, which, as a result, greatly affects the performance and efficiency of the operation of power facilities. In this paper, we studied the problem of mathematical modeling of the mass transfer of the process of drying a multilayer thermal insulation coating on a polymer basis. The proposed method for calculating the concentration and temperature fields allows for optimizing the drying process and improving the quality and reliability of the technological process. Coating ; Thermal insulation ; Process engineering ; Materials science ; Reliability (semiconductor) ; Process (computing) ; Adhesive ; Polymer ; Thermal ; Mass transfer ; Heat transfer ; Energy consumption ; Mechanical engineering ; Composite material ; Computer science ; Power (physics) ; Engineering ; Layer (electronics) ; Mechanics Material Properties and Applications ; Engineering Technology and Methodologies ; Industrial Engineering and Technologies
Authors: Nidhi Nidhi | Hardeep Singh | Samaresh Das | Tashi Nautiyal
Source: Physica Scripta (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

Role of morphology and composition in the transport properties of highly conducting CVD grown PtTe<sub>x</sub> Abstract Platinum telluride (PtTe x ), a metallic noble transition-metal dichalcogenide, has emerged as a central candidate for magnetic and optoelectronic applications. Recently, PtTe x has drawn great attention because of its large positive magnetoresistance and broadband photodetection owing to its astounding electron transport properties. Here, we report the role of morphology, texture, and composition in the transport properties of CVD-grown highly conducting PtTe x . Two different compositions of Te atoms, named tellurium stoichiometric (TS) and tellurium rich (TR), with different morphology have been obtained in the PtTe x films. The highest longitudinal conductivity was found to be ∼ 3.57 × 10 7 and 3.83 × 10 6 S m −1 at 200 K in the TR and TS-PtTe x samples, respectively. The maximum carrier density in TR and TS-PtTe x samples was found to be of the order of 10 22 and 10 21 cm −3 , respectively. Further, the negative magnetoresistance has also been found to be ∼ −2.2% at 300 K in the presence of an in-plane magnetic field of 9 T. Our results, reporting extremely high conductivity along with negative magnetoresistance, promise exciting applications of PtTe x for nanoelectronic devices. Materials science ; Magnetoresistance ; Tellurium ; Condensed matter physics ; Texture (cosmology) ; Conductivity ; Chalcogen ; Electrical resistivity and conductivity ; Magnetic field ; Crystallography ; Chemistry ; Physics ; Metallurgy ; Physical chemistry 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Chao Zhang | Hui Tang | Pan Chen | Hong Jiang | Huai-Jun Sun | Kai‐Ming Ho | Cai‐Zhuang Wang
Source: arXiv (Cornell University) (preprint, 2023-03-16)
Field: Materials Science — Materials Chemistry

Machine learning guided discovery of superconducting calcium borocarbides Pursuit of superconductivity in light-element systems at ambient pressure is of great experimental and theoretical interest. In this work, we combine a machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary Ca-B-C compounds. Three new layered borocarbides (stable CaBC5 and metastable Ca2BC11 and CaB3C3) are predicted to be phonon-mediated superconductors at ambient pressure. The hexagonal CaB3C3 possesses the highest Tc of 26.05 K among the three compounds. The σ-bonging bands around the Fermi level account for the large electron-phonon coupling (λ = 0.980) of hexagonal CaB3C3. The ML-guided approach opens up a way for greatly accelerating the discovery of new high-Tc superconductors. Superconductivity ; Calcium ; Computer science ; Materials science ; Physics ; Condensed matter physics ; Metallurgy Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Boron Compounds in Chemistry