jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Utkarsh Sinha | Birol Dindoruk | Mohamed Y. Soliman
Source: SPE Journal (article, 2022-04-04)
Field: Chemistry — Analytical Chemistry

Physics Augmented Correlations and Machine Learning Methods to Accurately Calculate Dead Oil Viscosity Based on the Available Inputs Summary Viscosity is one of the key parameters that is necessary to describe the fluid flow and pressure propagation in porous media as well as flow in pipes and various types of conduits. In this study, we have developed two different approaches to be able to estimate the viscosity seamlessly from a fraction to a million centipoise range. Our first approach was based on a trainable explicit expression that uses a reference viscosity measured at any temperature. This is a very common situation as the viscosity may have already been measured at reservoir temperature. Using the reference viscosity concept for many data sets, we also demonstrated the validity and tuneability of the proposed approach to predict the viscosities for the entire temperature range of interest. Our second method is developed for the cases that we did not have any viscosity measurements or no valid viscosity measurements. For such circumstances, we have utilized the extreme gradient boost (XGB) method algorithm (XGB Method) (Friedman 2001; Chen et al. 2015) using leave-one-out cross-validation (LOOCV) (Shao 1993), to be able to estimate the viscosity for the entire range of viscosities that are encountered for crudes. Some of the details of hybrid methods and other regression/machine learning methods and the role of the asphaltenes are already discussed by Sinha et al. (2019, 2020). The current approach uses a richer data set than any of the published papers with very limited input parameters: reservoir temperature, API gravity, and molecular weight, and the tuneability of the models is constrained with the molecular weight of the crudes. Viscosity ; Range (aeronautics) ; API gravity ; Set (abstract data type) ; Flow (mathematics) ; Computer science ; Machine learning ; Algorithm ; Mathematics ; Mechanics ; Thermodynamics ; Petroleum engineering ; Materials science ; Geology ; Physics ; Crude oil Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Miguel Ángel Delgado Noguera | Borja Millán | Arturo Aquino | José Manuel Andújar
Source: Agronomy (article, 2022-04-19)
Field: Chemistry — Analytical Chemistry

Methodology for Olive Fruit Quality Assessment by Means of a Low-Cost Multispectral Device The standard methods for determining the quality of olives involve chemical methods that are time-consuming and expensive. These limitations lead growers to homogeneous harvesting based on subjective criteria such as intuition and visual decisions. In recent times, precision agriculture techniques for fruit quality assessment, such as spectroscopy, have been introduced. However, they require expensive equipment, which limit their use to olive mills. This work presents a complete methodology based on a new low-cost multispectral sensor for assessing quality parameters of intact olive fruits. A set of 507 olive samples were analyzed with the proposed device. After data pre-processing, artificial neural network (ANN) models were trained using the 18 reflectance signals acquired by the sensor as input and three olive quality indicators (moisture, acidity, and fat content) as targets. The responses of the ANN models were promising, reaching coefficient-of-determination values of 0.78, 0.86, and 0.62 for fruit moisture, acidity, and fat content, respectively. These results show the suitability of the proposed device for assessing the quality status of intact olive fruits. Its performance, along with its low cost and ease of use, paves the way for the implementation of an olive fruit quality appraisal system that is more affordable for olive growers. Multispectral image ; Agricultural engineering ; Reflectivity ; Artificial neural network ; Environmental science ; Quality (philosophy) ; Computer science ; Agriculture ; Water content ; Artificial intelligence ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Feyza Candan | Yuri Markushin | Gulnihal Ozbay
Source: Plants (article, 2022-06-14)
Field: Materials Science — Materials Chemistry

Uptake and Presence Evaluation of Nanoparticles in Cicer arietinum L. by Infrared Spectroscopy and Machine Learning Techniques The aim of this work was to study the applicability of infrared spectroscopy combined with machine learning techniques to evaluate the uptake and distribution of gold nanoparticles (AuNPs) and single-walled carbon nanotubes (CNTs) in <i>Cicer arietinum</i> L. (chickpea). Obtained spectral data revealed that the uptake of AuNPs and CNTs by the <i>C. arietinum</i> seedlings' root resulted in the accumulation of AuNPs and CNTs at stem and leaf parts, which consequently led to the heterogeneous distribution of nanoparticles. principal component analysis and support vector machine classification were applied to assess its usefulness for evaluating the results obtained using the attenuated total reflectance-Fourier transform infrared spectroscopy method of <i>C. arietinum</i> plant grown at different conditions. Specific wavenumbers that could classify the different nanoparticle constituents of <i>C. arietinum</i> plant extracts according to their ATR-FTIR spectra were identified within three specific regions: 450-503 cm<sup>-1</sup>, 750-870 cm<sup>-1</sup>, and 1022-1218 cm<sup>-1</sup>, based on larger PCA loadings of <i>C. arietinum</i> ATR-FTIR spectra with distinct spectral differences between samples of interest. The current work paves a path to the future fabrication strategies for AuNPs and single-walled CNTs via plant-based routes and highlights the diversity of the applications of these materials in bio-nanotechnology. These results indicate the importance of family-plant selection, choice of methods, and pathways for the efficient biomolecule delivery, drug cargo, and optimal conditions in the wide spectrum of bioapplications. Fourier transform infrared spectroscopy ; Biomolecule ; Attenuated total reflection ; Nanoparticle ; Colloidal gold ; Spectroscopy ; Materials science ; Infrared spectroscopy ; Carbon nanotube ; Principal component analysis ; Infrared ; Nanotechnology ; Analytical Chemistry (journal) ; Chemistry ; Chemical engineering ; Computer science ; Chromatography ; Artificial intelligence ; Optics ; Organic chemistry Nanoparticles: synthesis and applications ; Genetic and Environmental Crop Studies ; Medicinal Plant Research
Authors: YanYing Zhang | Tao Shen | Zhi‐Tian Zuo | Yuanzhong Wang
Source: Frontiers in Plant Science (article, 2022-11-02)
Field: Chemistry — Analytical Chemistry

ResNet and MaxEnt modeling for quality assessment of Wolfiporia cocos based on FT-NIR fingerprints As a fungus with both medicinal and edible value, <i>Wolfiporia cocos</i> (F. A. Wolf) Ryvarden & Gilb. has drawn more public attention. Chemical components' content fluctuates in wild and cultivated <i>W. cocos</i>, whereas the accumulation ability of chemical components in different parts is different. In order to perform a quality assessment of <i>W. cocos</i>, we proposed a comprehensive method which was mainly realized by Fourier transform near-infrared (FT-NIR) spectroscopy and ultra-fast liquid chromatography (UFLC). A qualitative analysis means was built a residual convolutional neural network (ResNet) to recognize synchronous two-dimensional correlation spectroscopy (2DCOS) images. It can rapidly identify samples from wild and cultivated <i>W. cocos</i> in different parts. As a quantitative analysis method, UFLC was used to determine the contents of three triterpene acids in 547 samples. The results showed that a simultaneous qualitative and quantitative strategy could accurately evaluate the quality of <i>W. cocos</i>. The accuracy of ResNet models combined synchronous FT-NIR 2DCOS in identifying wild and cultivated <i>W. cocos</i> in different parts was as high as 100%. The contents of three triterpene acids in Poriae Cutis were higher than that in Poria, and the one with wild Poriae Cutis was the highest. In addition, the suitable habitat plays a crucial role in the quality of <i>W. cocos</i>. The maximum entropy (MaxEnt) model is a common method to predict the suitable habitat area for <i>W. cocos</i> under the current climate. Through the results, we found that suitable habitats were mostly situated in Yunnan Province of China, which accounted for approximately 49% of the total suitable habitat area of China. The research results not only pave the way for the rational planting in Yunnan Province of China and resource utilization of <i>W. cocos</i>, but also provide a basis for quality assessment of medicinal fungi. Principal component analysis ; Botany ; Biology ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Cas van der Oord | Matthias Sachs | David Kovacs | Christoph Ortner | Gábor Cśanyi
Source: Research Square (preprint, 2022-11-18)
Field: Materials Science — Materials Chemistry

Hyperactive learning for data-driven interatomic potentials Abstract Data-driven interatomic potentials have emerged as a powerful class of surrogate models for ab initio potential energy surfaces that are able to reliably predict macroscopic properties with experimental accuracy. In generating accurate and transferable potentials the most time-consuming and arguably most important task is generating the training set, which still requires significant expert user input. To accelerate this process, this work presents hyperactive learning (HAL), a framework for formulating an accelerated sampling algorithm specifically for the task of training database generation. The key idea is to start from a physically motivated sampler (e.g., molecular dynamics) and add a biasing term that drives the system towards high uncertainty and thus to unseen training configurations. Building on this framework, general protocols for building training databases for alloys and polymers leveraging the HAL framework will be presented. For alloys, ACE potentials for AlSi10 are created by fitting to a minimal HAL-generated database containing 88 configurations (32 atoms each) with fast evaluation times of &lt;100 μs/atom/cpu-core. These potentials are demonstrated to predict the melting temperature with excellent accuracy. For polymers, a HAL database is built using ACE, able to determine the density of a long polyethylene glycol (PEG) polymer formed of 200 monomer units with experimental accuracy by only fitting to small isolated PEG polymers with sizes ranging from 2 to 32. Interatomic potential ; Computer science ; Psychology ; Physics ; Molecular dynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Francisco Colmenero | Vicente Timón
Source: Applied Sciences (article, 2022-10-15)
Field: Chemistry — Inorganic Chemistry

ZIF-75 under Pressure: Negative Linear Compressibility and Pressure-Induced Instability The behavior of the crystal structure of the zeolitic imidazolate framework ZIF-75 under pressure was studied by means of periodic density functional theory methods. Experimentally, it was shown that this material is tetragonal, space group I41/a at room temperature. However, according to the calculations, at zero temperature this material is monoclinic, space group C2/c. Irrespective of the symmetry of the material, the results show that ZIF-75 exhibits a negative linear compressibility effect and is unstable under relatively small applied pressures of the order of 0.1 GPa. Compressibility ; Monoclinic crystal system ; Tetragonal crystal system ; Materials science ; Instability ; Thermodynamics ; Condensed matter physics ; Crystal structure ; Crystallography ; Chemistry ; Mechanics ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Organic and Molecular Conductors Research
Authors: Leonid Komissarov | Toon Verstraelen
Source: Scientific Data (article, 2022-02-22)
Field: Chemistry — Inorganic Chemistry

Zeo-1, a computational data set of zeolite structures Fast, empirical potentials are gaining increased popularity in the computational fields of materials science, physics and chemistry. With it, there is a rising demand for high-quality reference data for the training and validation of such models. In contrast to research that is mainly focused on small organic molecules, this work presents a data set of geometry-optimized bulk phase zeolite structures. Covering a majority of framework types from the Database of Zeolite Structures, this set includes over thirty thousand geometries. Calculated properties include system energies, nuclear gradients and stress tensors at each point, making the data suitable for model development, validation or referencing applications focused on periodic silica systems. Zeolite ; Set (abstract data type) ; Computer science ; Data set ; Work (physics) ; Point (geometry) ; Quality (philosophy) ; Computational science ; Data mining ; Algorithm ; Chemistry ; Physics ; Geometry ; Mathematics ; Artificial intelligence ; Thermodynamics Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Zhigeng Lin | Baoying Huang | Lufeng Ouyang | Liyao Zheng
Source: Molecules (review, 2022-06-02)
Field: Chemistry — Organic Chemistry

Synthesis of Cyclic Fragrances via Transformations of Alkenes, Alkynes and Enynes: Strategies and Recent Progress With increasing demand for customized commodities and the greater insight and understanding of olfaction, the synthesis of fragrances with diverse structures and odor characters has become a core task. Recent progress in organic synthesis and catalysis enables the rapid construction of carbocycles and heterocycles from readily available unsaturated molecular building blocks, with increased selectivity, atom economy, sustainability and product diversity. In this review, synthetic methods for creating cyclic fragrances, including both natural and synthetic ones, will be discussed, with a focus on the key transformations of alkenes, alkynes, dienes and enynes. Several strategies will be discussed, including cycloaddition, catalytic cyclization, ring-closing metathesis, intramolecular addition, and rearrangement reactions. Representative examples and the featured olfactory investigations will be highlighted, along with some perspectives on future developments in this area. Atom economy ; Organic synthesis ; Chemistry ; Metathesis ; Intramolecular force ; Catalysis ; Combinatorial chemistry ; Biochemical engineering ; Organic chemistry ; Engineering Synthesis of Indole Derivatives ; Sulfur-Based Synthesis Techniques ; Radical Photochemical Reactions
Authors: Andrei A. Tereshchenko | D. M. Pashkov | Alexander A. Guda | С. А. Гуда | Yury V. Rusalev | А. В. Солдатов
Source: Molecules (article, 2022-01-06)
Field: Materials Science — Materials Chemistry

Adsorption Sites on Pd Nanoparticles Unraveled by Machine-Learning Potential with Adaptive Sampling Catalytic properties of noble-metal nanoparticles (NPs) are largely determined by their surface morphology. The latter is probed by surface-sensitive spectroscopic techniques in different spectra regions. A fast and precise computational approach enabling the prediction of surface-adsorbate interaction would help the reliable description and interpretation of experimental data. In this work, we applied Machine Learning (ML) algorithms for the task of adsorption-energy approximation for CO on Pd nanoclusters. Due to a high dependency of binding energy from the nature of the adsorbing site and its local coordination, we tested several structural descriptors for the ML algorithm, including mean Pd-C distances, coordination numbers (CN) and generalized coordination numbers (GCN), radial distribution functions (RDF), and angular distribution functions (ADF). To avoid overtraining and to probe the most relevant positions above the metal surface, we utilized the adaptive sampling methodology for guiding the ab initio Density Functional Theory (DFT) calculations. The support vector machines (SVM) and Extra Trees algorithms provided the best approximation quality and mean absolute error in energy prediction up to 0.12 eV. Based on the developed potential, we constructed an energy-surface 3D map for the whole Pd<sub>55</sub> nanocluster and extended it to new geometries, Pd<sub>79</sub>, and Pd<sub>85</sub>, not implemented in the training sample. The methodology can be easily extended to adsorption energies onto mono- and bimetallic NPs at an affordable computational cost and accuracy. Density functional theory ; Support vector machine ; Nanoclusters ; Bimetallic strip ; Adsorption ; Computer science ; Ab initio ; Nanoparticle ; Materials science ; Chemistry ; Artificial intelligence ; Computational chemistry ; Nanotechnology ; Metal ; Physical chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; X-ray Diffraction in Crystallography
Authors: Wei Wang | Yingwei Liu | Zheng Wang | Ge‐Fei Hao | Baoan Song
Source: Chemical Science (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

The way to AI-controlled synthesis: how far do we need to go? Chemical synthesis always plays an irreplaceable role in chemical, materials, and pharmacological fields. Meanwhile, artificial intelligence (AI) is causing a rapid technological revolution in many fields by replacing manual chemical synthesis and has exhibited a much more economical and time-efficient manner. However, the rate-determining step of AI-controlled synthesis systems is rarely mentioned, which makes it difficult to apply them in general laboratories. Here, the history of developing AI-aided synthesis has been overviewed and summarized. We propose that the hardware of AI-controlled synthesis systems should be more adaptive to execute reactions with different phase reagents and under different reaction conditions, and the software of AI-controlled synthesis systems should have richer kinds of reaction prediction modules. An updated system will better address more different kinds of syntheses. Our viewpoint could help scientists advance the revolution that combines AI and synthesis to achieve more progress in complicated systems. Computer science ; Architecture ; Artificial intelligence ; Computer architecture ; History ; Archaeology Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Ferroelectric and Negative Capacitance Devices
Authors: Harry Jang | James Louis–Jean | Bradley C. Childs | Kiel Holliday | Dallas D. Reilly | Matthew Athon | Kenneth R. Czerwinski | David W. Hatchett | Frédéric Poineau
Source: Royal Society Open Science (article, 2022-03-01)
Field: Chemistry — Inorganic Chemistry

Synthetic diversity in the preparation of metallic uranium Uranium metal is associated with several aspects of nuclear technology; it is used as fuel for research and power reactors, targets for medical isotope productions, explosive for nuclear weapons and precursors in synthetic chemistry. The study of uranium metal at the laboratory scale presents the opportunity to evaluate metallic nuclear fuels, develop new methods for metallic spent fuel reprocessing and advance the science relevant to nuclear forensics and medical isotope production. Since its first isolation in 1841, from the reaction of uranium chloride and potassium metal, uranium metal has been prepared by solid-state reactions and in solution by electrochemical, chemical and radiochemical methods. The present review summarizes the methods outlined above and describes the chemistry associated with each preparation. Uranium ; Depleted uranium ; Nuclear fuel ; Spent nuclear fuel ; Enriched uranium ; Radiochemistry ; Nuclear power ; Nuclear material ; Metal ; Chemistry ; Nuclear chemistry ; Materials science ; Metallurgy ; Nuclear physics ; Physics Radioactive element chemistry and processing ; Nuclear Materials and Properties ; Extraction and Separation Processes
Authors: Lukas Thum | Wiebke Riedel | Nataša Milojević | Chengyue Guan | Annette Trunschke | Klaus‐Peter Dinse | Thomas Risse | Reinhard Schomäcker | Robert Schlögl
Source: Frontiers in Chemistry (article, 2022-02-11)
Field: Materials Science — Materials Chemistry

Transition-Metal-Doping of CaO as Catalyst for the OCM Reaction, a Reality Check In this study, first-row transition metal-doped calcium oxide materials (Mn, Ni, Cr, Co., and Zn) were synthesized, characterized, and tested for the OCM reaction. Doped carbonate precursors were prepared by a co-precipitation method. The synthesis parameters were optimized to yield materials with a pure calcite phase, which was verified by XRD. EPR measurements on the doped CaO materials indicate a successful substitution of Ca<sup>2+</sup> with transition metal ions in the CaO lattice. The materials were tested for their performance in the OCM reaction, where a beneficial effect towards selectivity and activity effect could be observed for Mn, Ni, and Zn-doped samples, where the selectivity of Co- and Cr-doped CaO was strongly reduced. The optimum doping concentration could be identified in the range of 0.04-0.10 atom%, showing the strongest decrease in the apparent activation energy, as well as the maximum increase in selectivity. Catalysis ; Doping ; Transition metal ; Materials science ; Chemical engineering ; Chemistry ; Nanotechnology ; Organic chemistry ; Engineering ; Optoelectronics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Susanne M. Fischer | Petra Kaschnitz | Christian Slugovc
Source: Catalysis Science & Technology (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Tris(2,4,6-trimethoxyphenyl)phosphine – a Lewis base able to compete with phosphazene bases in catalysing oxa-Michael reactions The performance of the fairly airstable and commercially available “Lewis base beast” TTMPP in catalysing oxa-Michael reactions and the control of its activity by dilution and solvent choice are disclosed. Phosphazene ; Lewis acids and bases ; Phosphine ; Michael reaction ; Tris ; Base (topology) ; Chemistry ; Catalysis ; Organic chemistry ; Polymer chemistry ; Medicinal chemistry ; Polymer ; Biochemistry Asymmetric Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis ; Coordination Chemistry and Organometallics
Authors: Ayesha Akter | John Pietras | Srikanth Gopalan
Source: International Journal of Hydrogen Energy (article, 2022-08-27)
Field: Materials Science — Materials Chemistry

Heavily neodymium doped ceria as an effective barrier layer in solid oxide electrochemical cells Barrier layer ; Electrolyte ; Dielectric spectroscopy ; Electrode ; Materials science ; Oxide ; Neodymium ; Electrochemistry ; Doping ; Oxygen ; Inorganic chemistry ; Analytical Chemistry (journal) ; Layer (electronics) ; Chemical engineering ; Chemistry ; Nanotechnology ; Optoelectronics ; Physical chemistry ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials