jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. A. Mamun | Shuozhi Xu | Xiangguo Li | Yanqing Su
Source: Physica Scripta (article, 2023-08-30)
Field: Materials Science — Materials Chemistry

Comparing interatomic potentials in calculating basic structural parameters and Peierls stress in tungsten-based random binary alloys Abstract The field of machine learning-based interatomic potentials (ML-IAPs) has seen increasing development in recent years. In this work, we compare three widely used ML-IAPs–the moment tensor potential (MTP), the spectral neighbor analysis potential (SNAP), and the tabulated Gaussian approximation potential (tabGAP)with a conventional non-ML-IAP, the embedded atom method (EAM) potential. We evaluated these potentials on the basis of their accuracy and efficiency in determining basic structural parameters and Peierls stress under equivalent conditions. Three tungsten (W)-based alloys (Mo-W, Nb-W, and Ta-W) are considered, and their lattice parameter, formation energy, elastic tensor, and Peierls stress of edge dislocation are calculated. Compared with DFT results, MTP demonstrates the highest accuracy in predicting the lattice parameter and the best computational efficiency among the three ML-IAPs, while tabGAP accurately predicts two independent elastic constants, C 11 and C 12 . Despite being the slowest, SNAP shows the highest accuracy in predicting the third independent elastic constant C 44 and its Peierls stress value is comparable to that based on MTP. Materials science ; Tungsten ; Gaussian ; Lattice constant ; Interatomic potential ; Atom (system on chip) ; Dislocation ; Peierls stress ; Lattice (music) ; Work (physics) ; Stress (linguistics) ; Cauchy stress tensor ; Condensed matter physics ; Thermodynamics ; Physics ; Molecular physics ; Quantum mechanics ; Molecular dynamics ; Diffraction Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Boron and Carbon Nanomaterials Research
Authors: Sergei F. Vyboishchikov
Source: Journal of Chemical Information and Modeling (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

Dense Neural Network for Calculating Solvation Free Energies from Electronegativity-Equalization Atomic Charges I propose a dense Neural Network for evaluation of solvation free energies Δ<i>G°</i><sub>solv</sub> for molecules and ions in water and nonaqueous solvents, Easy Solvation Energy with Electronegativity Equalization charges and Dense Neural Network (ESE-EE-DNN). As input features, it uses the Conductor-like Screening Model (COSMO) electrostatic energy, atomic cavity surface areas, total cavity volume, and induced surface charges. For the COSMO calculation, electronegativity-equalization atomic charges are employed. ESE-EE-DNN exhibits fairly high accuracy, similar or even superior to that of mainstream density functional theory-based methods. For neutral solutes in water, polar protic, polar aprotic, and nonpolar solvents, ESE-EE-DNN yields a root-mean-square error (RMSE) of 1.25, 1.36, 0.70, and 0.71 kcal/mol, respectively. ESE-EE-DNN is particularly advantageous for ionic solutes, with an RMSE of 2.82 and 1.42 kcal/mol for aqueous and nonaqueous ion solutions, correspondingly. ESE-EE-DNN is very efficient due to a fast evaluation of the electronegativity-equalization charges. Solvation ; Electronegativity ; Chemistry ; Implicit solvation ; Artificial neural network ; Ion ; Ionic bonding ; Aqueous solution ; Equalization (audio) ; Polar ; Physical chemistry ; Organic chemistry ; Machine learning ; Physics ; Algorithm ; Quantum mechanics ; Mathematics ; Computer science Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods
Authors: Carlos Alberto Teixeira | Amilton Moreira de Oliveira | Íris Medeiros | Leandro Wang Hantao
Source: Fuel (article, 2023-10-31)
Field: Chemistry — Analytical Chemistry

Unified multi-parameter predictive modeling of crude oil and its distilled fractions through Artificial Neural Networks Diesel fuel ; Chemistry ; Volatility (finance) ; Artificial neural network ; Sample preparation ; Monoisotopic mass ; Process engineering ; Environmental science ; Analytical Chemistry (journal) ; Chromatography ; Mass spectrometry ; Computer science ; Mathematics ; Artificial intelligence ; Organic chemistry Petroleum Processing and Analysis ; Spectroscopy and Chemometric Analyses ; Hydrocarbon exploration and reservoir analysis
Authors: Ahmed Belaadi | Abdelaziz Lekrine | Messaouda Boumaaza | Hassan Alshahrani | Mostefa Bourchak | Khalid A. Juhany | Fouad Damiri | Indran Suyambulingam | Suchart Siengchin
Source: Advances in Materials and Processing Technologies (article, 2023-04-05)
Field: Materials Science — Polymers and Plastics

Water Uptake of HDPE Reinforced with <i>Washingtonia</i> Fibre Biocomposites: Mathematical Modelling using Artificial Neural Network, Response Surface Methodology and Genetic Algorithm To research the mechanisms of diffusion and water uptake kinetics biocomposites, various HDPE materials reinforced by varying quantities of Washingtonia filifera (WF) fibres (10 to 30 wt%) were submerged at room temperature in distilled water. To optimise the immersion time and WF fibre content in HDPE/WF biocomposite water uptake, an artificial neural network, the response surface methodology and the genetic algorithm were employed. In this research, the CCD model of RSM was utilised to carry out test design, modelling, and optimisation. It was found that the way water absorbs liquids follows the Fickian diffusion mode. The outcomes demonstrate that the incorporation of WF fibres into the HDPE matrix decreased the diffusivity. The ANOVA determined the relative significance of each variable and demonstrated the model’s validity by demonstrating a high correlation between the observed data. Moreover, the results showed that the ANN models have training, test and validation correlation coefficients for water absorption prediction of 0.9955, 0.9915 and 0.9999, respectively. RSM-GA revealed that a 10% fibre content and a one-hour immersion duration produced the lowest levels of absorption. Additionally, a model that is very suitable for predicting biocomposites water uptake and is applicable to a variety of industrial applications is developed. Response surface methodology ; Absorption of water ; Materials science ; High-density polyethylene ; Distilled water ; Biocomposite ; Thermal diffusivity ; Composite material ; Artificial neural network ; Biological system ; Algorithm ; Mathematics ; Composite number ; Machine learning ; Computer science ; Polyethylene ; Chemistry ; Chromatography ; Thermodynamics ; Statistics Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Electrospun Nanofibers in Biomedical Applications
Authors: Yael Gutiérrez | Stefano Dicorato | Anna P. Ovvyan | Frank Brückerhoff‐Plückelmann | Josef Resl | Maria M. Giangregorio | Kurt Hingerl | Christoph Cobet | Manuela Schiek | Matthias Duwe | Peter Thiesen | Wolfram H. P. Pernice | María Losurdo
Source: Advanced Optical Materials (article, 2023-08-17)
Field: Materials Science — Materials Chemistry

Layered Gallium Monosulfide as Phase‐Change Material for Reconfigurable Nanophotonic Components On‐Chip Abstract The demand for information processing at ultrahigh speed with large data transmission capacity is continuously rising. Necessary building blocks for on‐chip photonic integrated circuits (PICs) are reconfigurable integrated low‐loss high‐speed modulators and switches. Phase change materials (PCMs) provide unique opportunities for integration into PICs. Here, the investigation of layered gallium monosulfide (GaS) as a novel low‐loss PCM from infrared to optical frequencies is pioneered, with high index contrast (Δ n ≈0.5) at the optical telecommunication band. The GaS bandgap switches from 1.5 ± 0.2 eV for the amorphous state to 2.1 ± 0.1 eV for the crystalline state. It is demonstrated that the reversible GaS amorphous‐to‐crystalline phase transition can be operated thermally and by picosecond green (532 nm) laser irradiation. The design of a reconfigurable integrated optical modulator on‐chip based on Mach‐Zehnder Interferometers (MZI) with the GaS PCM cell deposited on one of the arms for application is presented at the telecommunication wavelength of λ = 1310 nm, where the standard single mode optical fiber exhibits zero chromatic dispersion, and at λ = 1550 nm, where a minimum optical loss of 0.22 dB km −1 is obtained. This opens the route to applications such as reconfigurable modulators, beam steering using phase modulation, and photonic neural networks. Materials science ; Optoelectronics ; Optical switch ; Photonics ; Amorphous solid ; Optical modulator ; Optics ; Nanophotonics ; Optical communication ; Photonic integrated circuit ; Picosecond ; Phase modulation ; Laser ; Phase noise Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Optical Network Technologies
Authors: Zhanjun Qiu | Yanxiao Hu | Ding Li | Tao Hu | Hong Xiao | Chunbao Feng | Dengfeng Li
Source: Chinese Physics B (article, 2023-02-08)
Field: Materials Science — Materials Chemistry

Thermal transport properties of two-dimensional boron dichalcogenides from a first-principles and machine learning approach The investigation of thermal transport is crucial to the thermal management of modern electronic devices. To obtain the thermal conductivity through solution of the Boltzmann transport equation, calculation of the anharmonic interatomic force constants has a high computational cost based on the current method of single-point density functional theory force calculation. The recent suggested machine learning interatomic potentials (MLIPs) method can avoid these huge computational demands. In this work, we study the thermal conductivity of two-dimensional MoS 2 -like hexagonal boron dichalcogenides (H-B 2 VI 2 ; VI = S, Se, Te) with a combination of MLIPs and the phonon Boltzmann transport equation. The room-temperature thermal conductivity of H-B 2 S 2 can reach up to 336 W⋅m −1 ⋅K −1 , obviously larger than that of H-B 2 Se 2 and H-B 2 Te 2 . This is mainly due to the difference in phonon group velocity. By substituting the different chalcogen elements in the second sublayer, H-B 2 VIVI ′ have lower thermal conductivity than H-B 2 VI 2 . The room-temperature thermal conductivity of B 2 STe is only 11% of that of H-B 2 S 2 . This can be explained by comparing phonon group velocity and phonon relaxation time. The MLIP method is proved to be an efficient method for studying the thermal conductivity of materials, and H-B 2 S 2 -based nanodevices have excellent thermal conduction. Thermal conductivity ; Boltzmann equation ; Phonon ; Thermal conduction ; Materials science ; Condensed matter physics ; Density functional theory ; Mean free path ; Thermal ; Thermodynamics ; Physics ; Quantum mechanics ; Scattering ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Eyüp Bilgi | David A. Winkler | Ceyda Öksel Karakuş
Source: Journal of drug targeting (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

Identifying factors controlling cellular uptake of gold nanoparticles by machine learning There is strong interest to improve the therapeutic potential of gold nanoparticles (GNPs) while ensuring their safe development. The utility of GNPs in medicine requires a molecular-level understanding of how GNPs interact with biological systems. Despite considerable research efforts devoted to monitoring the internalisation of GNPs, there is still insufficient understanding of the factors responsible for the variability in GNP uptake in different cell types. Data-driven models are useful for identifying the sources of this variability. Here, we trained multiple machine learning models on 2077 data points for 193 individual nanoparticles from 59 independent studies to predict cellular uptake level of GNPs and compared different algorithms for their efficacies of prediction. The five ensemble learners (Xgboost, random forest, bootstrap aggregation, gradient boosting, light gradient boosting machine) made the best predictions of GNP uptake, accounting for 80-90% of the variance in the test data. The models identified particle size, zeta potential, GNP concentration and exposure duration as the most important drivers of cellular uptake. We expect this proof-of-concept study will foster the more effective use of accumulated cellular uptake data for GNPs and minimise any methodological bias in individual studies that may lead to under- or over-estimation of cellular internalisation rates. Colloidal gold ; Nanoparticle ; Nanotechnology ; Computer science ; Chemistry ; Artificial intelligence ; Materials science Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanoparticle-Based Drug Delivery
Authors: Theofrida Julius Maginga | Pierre Bakunzibake | Emmanuel Masabo | Deogracious Protas Massawe | Promise Ricardo Agbedanu | Jimmy Nsenga
Source: Smart Agricultural Technology (article, 2023-05-29)
Field: Chemistry — Analytical Chemistry

Design and implementation of IoT sensors for nonvisual symptoms detection on maize inoculated with Exserohilum turcicum Diseases on maize crops are highly caused by chronic and emerging pathogens that results in stagnant growth in the plant system. Several initiatives have been adopted to manage disease on crops which include new cultivation practices, genetic engineering, plant breeding and chemical control which have only proven to perform better on laboratory-based approaches. Meanwhile, small holder farmers can hardly afford such intervention mechanisms because they are costly and require highly skilled labor. With the advancement of technologies in Internet of Things (IoT) and different artificial intelligence models, non-visual signs of disease are being explored and experimented in this work for nonvisual early disease detection purposes. Volatile Organic Compounds (VOCs), Ultrasound, Nitrogen, Phosphorous, Potassium (NPK) fertilizer are profiled on control maize and inoculated maize with Exserohilum turcicum fungus to generate time series data. Dataset generated are preprocessed, analyzed, and visualized using pandas and matplotlib python tools. Machine Learning algorithms have been inferenced on the dataset; Statsmodel for trends and seasonality detection and Pruned Exact Linear Time (PELT) for change point detection. Analysis of data on the implemented Internet of Things technology in this experiment has achieved nonvisual detection of Northern Leaf Blight (NLB) disease on maize within four days post inoculation from monitored Volatile Organic Compounds and ultrasound emission. Exserohilum ; Blight ; Agronomy ; Biotechnology ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Charles Le Losq | Paolo A. Sossi
Source: Frontiers in Earth Science (article, 2023-03-20)
Field: Materials Science — Ceramics and Composites

Atomic structure and physical properties of peridotite glasses at 1 bar Earth’s mantle, whose bulk composition is broadly peridotitic, likely experienced periods of extensive melting in its early history that formed magma oceans and led to its differentiation and formation of an atmosphere. However, the physical behaviour of magma oceans is poorly understood, as the high liquidus temperatures and rapid quench rates required to preserve peridotite liquids as glasses have so far limited their investigation. In order to better characterize the atomic structure and estimate the physical properties of such glasses, we examined the Raman spectra of quenched peridotite melts, equilibrated at 1900 °C ± 50 °C at ambient pressure under different oxygen fugacities ( f O 2 ), from 1.9 log units below to 6.0 log units above the Iron-Wüstite buffer. Fitting the spectra with Gaussian components assigned to different molecular entities (Q-species) permits extraction of the mean state of polymerisation of the glass. We find that the proportions of Q 1 (0.36–0.32), Q 2 (0.50–0.43), and Q 3 (0.16–0.23) vary with Fe 3+ /Fe TOT (Fe TOT = Fe 2+ + Fe 3+ ), where increasing Fe 3+ /Fe TOT produces an increase in Q 3 at the expense of Q 2 at near-constant Q 1 . To account for the offset between Raman-derived NBO/T (2.06–2.27) with those determined by assuming Fe 2+ exists entirely as a network modifier and Fe 3+ a network former (2.10–2.44), ∼2/3 of the ferric iron and ∼90% of the ferrous iron in peridotite glasses must behave as network modifiers. We employ a deep neural network model, trained to predict alkali and alkaline-earth aluminosilicate melts properties, to observe how small variations in the atomic structure of peridotite-like melts affect their viscosity. For Fe-free peridotite-like melts, the model yields a viscosity of ∼ −1.75 log Pa s at 2000 °C, similar to experimental determinations for iron-bearing peridotite melts. The model predicts that changes in the peridotite melt atomic structure with Fe 3+ /Fe TOT yield variations in melt viscosity lower than 0.1 log Pa s, barely affecting the Rayleigh number. Therefore, at the high temperatures typical of magma oceans, at least at 1 bar, small changes in melt structure from variations in oxidation state are unlikely to affect magma ocean fluid dynamics. Peridotite ; Liquidus ; Raman spectroscopy ; Mineral redox buffer ; Analytical Chemistry (journal) ; Ferrous ; Mantle (geology) ; Materials science ; Mineralogy ; Wüstite ; FERRIC IRON ; Geology ; Chemistry ; Oxide ; Alloy ; Metallurgy ; Geochemistry ; Physics Glass properties and applications ; Geological and Geochemical Analysis ; Mineralogy and Gemology Studies
Authors: Xuhao Wan | Zhaofu Zhang | Anyang Wang | Jinhao Su | Wenjun Zhou | John Robertson | Peng Yuan | Yu Zheng | Yuzheng Guo
Source: Journal of Colloid and Interface Science (article, 2023-06-02)
Field: Chemistry — Inorganic Chemistry

Deep-learning-assisted theoretical insights into the compatibility of environment friendly insulation medium with metal surface of power equipment Compatibility (geochemistry) ; Sulfur hexafluoride ; Materials science ; Greenhouse gas ; Chemical engineering ; Nanotechnology ; Mechanical engineering ; Process engineering ; Chemistry ; Composite material ; Engineering ; Organic chemistry Inorganic Fluorides and Related Compounds ; High voltage insulation and dielectric phenomena ; Surface Modification and Superhydrophobicity
Authors: Yinyu Wei | Yi Sun | Shuailong Jia | Yan Pan | Chaomei Xiong | Meiling Qi | Chenxi Wang | Zhifeng Du | Hongliang Jiang
Source: Journal of Chromatography B (article, 2023-06-07)
Field: Chemistry — Spectroscopy

Identification of endogenous carbonyl steroids in human serum by chemical derivatization, hydrogen/deuterium exchange mass spectrometry and the quantitative structure-retention relationship Chemistry ; Derivatization ; Hydrogen–deuterium exchange ; Chromatography ; Mass spectrometry ; Steroid ; Fragmentation (computing) ; Deuterium ; Hormone ; Biochemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Chemical Reactions and Isotopes
Authors: Chris Sparling | Alice Ruget | Lewis Ireland | Νικολέτα Κωτσίνα | O. Ghafur | Jonathan Leach | Dave Townsend
Source: The Journal of Chemical Physics (article, 2023-12-01)
Field: Chemistry — Spectroscopy

The importance of molecular axis alignment and symmetry-breaking in photoelectron elliptical dichroism Photoelectron angular distributions (PADs) produced from the photoionization of chiral molecules using elliptically polarized light exhibit a forward/backward asymmetry with respect to the optical propagation direction. By recording these distributions using the velocity-map imaging (VMI) technique, the resulting photoelectron elliptical dichroism (PEELD) has previously been demonstrated as a promising spectroscopic tool for studying chiral molecules in the gas phase. The use of elliptically polarized laser pulses, however, produces PADs (and consequently, PEELD distributions) that do not exhibit cylindrical symmetry about the propagation axis. This leads to significant limitations and challenges when employing conventional VMI acquisition and data processing strategies. Using novel photoelectron image analysis methods based around Hankel transform reconstruction tomography and machine learning, however, we have quantified-for the first time-significant symmetry-breaking contributions to PEELD signals that are of a comparable magnitude to the symmetric terms in the multiphoton ionization of (1R,4R)-(+)- and (1S,4S)-(-)-camphor. This contradicts any assumptions that symmetry-breaking can be ignored when reconstructing VMI data. Furthermore, these same symmetry-breaking terms are expected to appear in any experiment where circular and linear laser fields are used together. This ionization scheme is particularly relevant for investigating dynamics in chiral molecules, but it is not limited to them. Developing a full understanding of these terms and the role they play in the photoionization of chiral molecules is of clear importance if the potential of PEELD and related effects for future practical applications is to be fully realized. Photoionization ; Ionization ; Physics ; Asymmetry ; Dichroism ; Symmetry (geometry) ; Elliptical polarization ; Circular dichroism ; Symmetry breaking ; Atomic physics ; Molecular physics ; Optics ; Chemistry ; Laser ; Quantum mechanics ; Linear polarization ; Ion ; Geometry ; Crystallography Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Mass Spectrometry Techniques and Applications
Authors: Sungwon Kim | Juhwan Noh | Taewon Jin | Jaewan Lee | Yousung Jung
Source: npj Computational Materials (article, 2023-08-11)
Field: Materials Science — Materials Chemistry

A structure translation model for crystal compounds Abstract High-throughput virtual screening for crystals aims to discover new materials by evaluating the property of every virtual candidate in the database exhaustively. During this process, the major computational bottleneck is the costly structural relaxation of each hypothetical material on the large-scale dataset using density functional theory (DFT) calculations. Here, we present a generative domain translation framework that maps the unrelaxed structural domains to the relaxed domains, enabling data-driven structural translations. The model predicts the materials formation energy with a small mean absolute error without DFT relaxations, and furthermore can produce the atomic coordinates consistent with the DFT relaxed structures. The utility of the proposed concept is not restricted to the structural domains, and we expect that it can be extended to translate the domain of easy-to-compute properties into the domain of more difficult properties. Translation (biology) ; Bottleneck ; Computer science ; Domain (mathematical analysis) ; Density functional theory ; Relaxation (psychology) ; Process (computing) ; Algorithm ; Property (philosophy) ; Virtual screening ; Theoretical computer science ; Statistical physics ; Molecular dynamics ; Physics ; Computational chemistry ; Mathematics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions