jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matea Gavran | Željka Ujević Andrijić | Nenad Bolf | Nikola Rimac | Josip Sacher | Damir Šahnić
Source: Processes (article, 2023-12-16)
Field: Chemistry — Analytical Chemistry

Development of a Calibration Model for Real-Time Solute Concentration Monitoring during Crystallization of Ceritinib Using Raman Spectroscopy and In-Line Process Microscopy Raman spectroscopy is a useful tool for polymorphic form-monitoring during the crystallization process. However, its application to solute concentration estimation in two-phase systems like crystallization is rare, as the Raman signal is influenced by various changing factors in the crystallization process. The development of a robust calibration model that covers all variations is complex and represents a major challenge for the implementation of Raman spectroscopy for in-line monitoring and control of the solution crystallization process. This paper describes the development of a Raman-based calibration model for estimating the solute concentration of the active pharmaceutical ingredient ceritinib. Several different calibration approaches were tested, which included both temperature and spectra of clear solutions and slurries/suspensions. It was found that the concentration of the ceritinib solution could not be accurately predicted when suspended crystals were present. To overcome this challenge, the approach was enhanced by including additional variables related to crystal size and solid concentration obtained via in-line process microscopy (chord-length distribution percentiles D10, D50 and D90) and turbidity. Partial least squares regression (PLSR) and artificial neural network (ANN) models were developed and compared based on root mean square error (RMSE). ANN models estimated the solute concentration with high accuracy, with the prediction error not exceeding 1% of the nominal solute concentration. Crystallization ; Process analytical technology ; Raman spectroscopy ; Partial least squares regression ; Calibration ; Mean squared error ; Analytical Chemistry (journal) ; Materials science ; Biological system ; Chemistry ; Chromatography ; Chemical engineering ; Mathematics ; Optics ; Machine learning ; Computer science ; Physics Spectroscopy and Chemometric Analyses ; Crystallization and Solubility Studies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Yuan Niu | Yun Wang | Minjuan He | Wenhao He | Zhen-hua Zhao | Zhibin Lu
Source: Ceramics International (article, 2023-05-04)
Field: Materials Science — Materials Chemistry

Calculation and prediction of sliding energy barriers by first-principles combined with machine learning Materials science ; Tribology ; Mean squared error ; Charge (physics) ; Energy (signal processing) ; Eigenvalues and eigenvectors ; Lattice (music) ; Mean squared prediction error ; Algorithm ; Machine learning ; Computer science ; Composite material ; Mathematics ; Statistics ; Physics Thermal properties of materials ; 2D Materials and Applications ; Adhesion, Friction, and Surface Interactions
Authors: Filip Buric | Sandra Viknander | Xiaozhi Fu | Oliver Lemke | Jan Zrimec | Łukasz Szyrwiel | Michael Mueleder | Markus Ralser | Aleksej Zelezniak
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-10-04)
Field: Chemistry — Spectroscopy

The amino acid sequence determines protein abundance through its conformational stability and reduced synthesis cost Abstract Understanding what drives protein abundance is essential to biology, medicine, and biotechnology. Driven by evolutionary selection, the amino acid sequence is tailored to meet the required abundance of proteomes, underscoring the intricate relationship between sequence and functional demand. Yet, the specific role of amino acid sequences in determining proteome abundance remains elusive. Here, we demonstrate that the amino acid sequence predicts abundance by shaping a protein’s conformational stability. We show that increasing the abundance provides metabolic cost benefits, underscoring the evolutionary advantage of maintaining a highly abundant and stable proteome. Specifically, using a deep learning model (BERT), we predict 56% of protein abundance variation in Saccharomyces cerevisiae solely based on amino acid sequence. The model reveals latent factors linking sequence features to protein stability. To probe these relationships, we introduce MGEM (Mutation Guided by an Embedded Manifold), a methodology for guiding protein abundance through sequence modifications. We find that mutations increasing abundance significantly alter protein polarity and hydrophobicity, underscoring a connection between protein stability and abundance. Through molecular dynamics simulations and in vivo experiments in yeast, we confirm that abundance-enhancing mutations result in longer-lasting and more stable protein expression. Importantly, these sequence changes also reduce metabolic costs of protein synthesis, elucidating the evolutionary advantage of cost-effective, high-abundance, stable proteomes. Our findings support the role of amino acid sequence as a pivotal determinant of protein abundance and stability, revealing an evolutionary optimization for metabolic efficiency. Abundance (ecology) ; Biology ; Proteome ; Peptide sequence ; Amino acid ; Computational biology ; Protein sequencing ; Sequence (biology) ; Saccharomyces cerevisiae ; Genetics ; Biochemistry ; Yeast ; Gene ; Ecology Advanced Proteomics Techniques and Applications ; Microbial Metabolic Engineering and Bioproduction ; Protein Structure and Dynamics
Authors: Daniele Melideo | Lorenzo Ferrari | Paolo Taddei Pardelli
Source: Journal of Physics Conference Series (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Preliminary analysis of refilling cold-adsorbed hydrogen tanks Abstract The effective storage of hydrogen is a critical challenge that needs to be overcome for it to become a widely used and clean energy source. Various methods exist for storing hydrogen, including compression at high pressures, liquefaction through extreme cooling (i.e. -253 °C), and storage with chemical compounds. Each method has its own advantages and disadvantages. MAST3RBoost (Maturing the Production Standards of Ultraporous Structures for High Density Hydrogen Storage Bank Operating on Swinging Temperatures and Low Compression) is a European funded Project aiming to establish a reliable benchmark for cold-adsorbed H2 storage (CAH2) at low compression levels (100 bar or below). This is achieved through the development of advanced ultraporous materials suitable for mobility applications, such as hydrogen-powered vehicles used in road, railway, air, and water transportation. The MAST3RBoost Project utilizes cutting-edge materials, including Activated Carbons (ACs) and high-density MOFs (Metal-organic Frameworks), which are enhanced by Machine Learning techniques. By harnessing these materials, the project seeks to create a groundbreaking path towards meeting industry goals. The project aims to develop the world’s first adsorption-based demonstrator at a significant kg-scale. To support the design of the storage tank, the project employs Computational Fluid Dynamics (CFD) software, which allows for numerical investigations. In this paper, a preliminary analysis of the tank refilling process is presented, with a focus on the impact of the effect of the tank and hydrogen temperatures on quantity of hydrogen adsorbed. Hydrogen storage ; Liquefaction ; Hydrogen ; Compressed hydrogen ; Storage tank ; Adsorption ; Process engineering ; Environmental science ; Mechanical engineering ; Computer science ; Materials science ; Engineering ; Chemistry Hydrogen Storage and Materials ; Spacecraft and Cryogenic Technologies ; Hybrid Renewable Energy Systems
Authors: Ali Roozbahani | Maryam Salahinejad | Vanik Gholipour
Source: Environmental Technology (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

An exploratory in <i>N</i> -doped carbon dots as green fluorescence probes for Hg(II) ions detection Carbon dots (CDs), as a fascinating carbon nanomaterial, have important applications in various fields due to their unique properties. The physical and chemical properties of CDs can be fine-tuned using heteroatom doping and surface functionalisation. Here, we synthesised N-doped carbon dots (N-CDs) by reacting Citric acid, which serve as the carbon core, with twenty amino acids under microwave irradiation. The fluorescence quenching of each amino acid doped CDs by Hg(II) ions was experimentally measured. Then the effect of the molecular features and chemical properties of amino acids on the fluorescence quenching of N-CDs by Hg(II) ions was investigated by using the quantitative structure-property relationship (QSPR) method. Applying different machine learning techniques including correlation-based and ReliefF algorithm feature selection approaches to choose relevant descriptors, multi-linear regression, and support vector machine to construct QSPR model, some reliable and predictive models were developed. Based on the variables used throughout the final QSPR models, hydrophobic interactions, in addition to hydrogen bonding interactions, can be considered a major factor governing the photoluminescence behaviour of different N-CDs quenched by Hg(II) ions. N-CDs derived from amino acids bearing larger hydrophobic surfaces show greater fluorescence quenching, indicating that a greater capacity to interact with Hg(II) metal ions resulting in further fluorescence quenching. Fluorescence ; Ion ; Carbon fibers ; Doping ; Chemistry ; Analytical Chemistry (journal) ; Nanotechnology ; Environmental chemistry ; Materials science ; Nuclear chemistry ; Optoelectronics ; Physics ; Optics ; Organic chemistry Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Shaswat Mohanty | SangHyuk Yoo | Keon Wook Kang | Wei Cai
Source: Computer Physics Communications (article, 2023-03-13)
Field: Materials Science — Materials Chemistry

Evaluating the transferability of machine-learned force fields for material property modeling Force field (fiction) ; Computer science ; Machine learning ; Benchmarking ; Artificial intelligence ; Transferability ; Field (mathematics) ; Molecular dynamics ; Test set ; Statistical physics ; Algorithm ; Physics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: M. Franke | Selsela Arsala | Frozan Tahiry | Simon-Olivier Gingras | Arun Sharma
Source: Data in Brief (article, 2023-12-06)
Field: Chemistry — Analytical Chemistry

Curated dataset of asphaltene structures Asphaltenes, a distinct class of molecules found in crude oil, exhibit insolubility in nonpolar solvents like n-heptane but are soluble in aromatic solvents such as toluene and benzene. Understanding asphaltenes is crucial in the petroleum industry due to their detrimental effects on oil processing, resulting in significant economic losses and production disruptions. While no singular structure defines asphaltenes, two major molecular architectures, namely archipelago and continental models, have gained wide acceptance for their consistency with various experimental investigations and subsequent use in computational studies. The archipelago model comprises two or more polyaromatic hydrocarbon entities interconnected via aliphatic side chains. In contrast, the island or continental model features a unified polyaromatic hydrocarbon moiety with 4 to 10 fused aromatic rings, averaging around 7 rings. To establish a comprehensive collection, we meticulously curated over 250 asphaltene structures derived from previous experimental and computational studies in this field. Our curation process involved an extensive literature survey, conversion of figures from publications into molecular structure files, careful verification of conversion accuracy, and structure editing to ensure alignment with molecular formulas. Our database provides digital structure files and optimized geometries for both predominant structural motifs. The optimization procedure commenced with the PM6 semi-empirical method, followed by further optimization utilizing density functional theory employing the B3LYP functional and the 6-31+G(d,p) basis set. Furthermore, we compiled a range of structural and electronic features for these molecules, serving as a valuable foundation for employing machine learning algorithms to investigate asphaltenes. This work provides a ready to use structural database of asphaltenes and sets the stage for future research endeavours in this domain. Asphaltene ; Computer science ; Toluene ; Chemistry ; Organic chemistry Petroleum Processing and Analysis ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Mohd Amin bin Abd Majid | Love Goel | Abhinav Saxena | Ashish Kumar Srivastava | Gyanendra Kumar Singh | Rajesh Verma | Javed Khan Bhutto | Hany S. Hussein
Source: Coatings (article, 2023-04-27)
Field: Materials Science — Metals and Alloys

Firefly Algorithm and Neural Network Employment for Dilution Analysis of Super Duplex Stainless Steel Clads over AISI 1020 Steel Using Gas Tungsten Arc Process Traditional low-carbon steels provide the strength needed to satisfy industrial demands. Low-carbon steel’s poor corrosion resistance is one of its main drawbacks. Due to this restriction, corrosion-resistant materials such as super duplex stainless steels are frequently used for cladding onto the surface of low-carbon steel. The cladded surface possesses superior chloride stress corrosion cracking resistance, pitting and crevice corrosion resistance, yield strength, ductility, and impact toughness. Mild steel with measurements of 300 × 300 × 12 mm has been selected as the substrate material, and super duplex stainless steel (S32950) with an electrode diameter of 2.4 mm has been selected as the filler wire. Gas Tungsten Arc (GTA) cladding was performed onto the surface of low-carbon steel for experimentation. The clad was deposited in the flat position with four different currents, i.e., 120 A, 130 A, 140 A, and 150 A. Cladding was performed on different layers of each current level, i.e., single layer, double layers, and triple layers. After experimenting and overviewing the outcomes, it can be concluded that the optimum input parameters would be a 3-layered clad at a 140 A current level. Cladding of the super duplex stainless steel over mild steel improves the corrosive properties. The percentage ratio of reactivation current density to activation current density (Ir/Ia%) improves from 29% (mild steel) to 4.1% at the top layer and 11.9% at the intermediate layer. The microhardness of the clad decreases with an increase in both the current level and the number of layers. Microhardness varies between 191–248 at the clad, 170–189 at the HAZ, and 143–153 at the substrate for a 1 kgf load. Dilution refers to the change in the cladding alloy composition due to the mixing of the molten matrix. The composition of the clad changes under a high dilution, resulting in a decrease in the mechanical as well as corrosion properties of the clad. However, if the dilution is too small, the bond between the substrate and the clad is poor. Therefore, dilution is one of the most important process control parameters and the key to obtaining high-quality cladding. Thus, the dilution effect is also analyzed on all three clad layers deposited at various current levels using the firefly algorithm (FA) and artificial neural network (ANN). It is observed that dilution levels are found to be more approachable to the experimental setup data with FA in comparison to ANN for various current levels. Materials science ; Cladding (metalworking) ; Metallurgy ; Corrosion ; Carbon steel ; Tungsten ; Pitting corrosion ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; High Temperature Alloys and Creep
Authors: Maya Shelke | Nihar M. Ranjan | Palak Jha | Aditi B Chavan | Kanchan Thakare | Sweta Pandey
Source:  (article, 2023-06-14)
Field: Chemistry — Analytical Chemistry

Detection of Artificially Ripened Fruits using Machine Learning The process of fruit ripening occurs naturally and is triggered by ethylene, a natural substance found in fruits. Unfortunately, this process is irreversible, causing the fruit to spoil quickly and resulting in losses for vendors. To speed up the process and increase profits, some use a chemical called calcium carbide (CaC2). However, this chemical can be harmful to human health, causing negative effects such as unsteadiness, neuralgia, memory loss, and seizures. It is difficult to detect artificially ripened fruit with the naked eye, and laboratory testing is costly and complex. To address this issue, machine learning is being used. By leveraging image processing techniques, an Android application is developed to analyze digital images of fruits and determine whether they have been artificially ripened. The goal of this system is to improve fruit quality control and protect consumers from potentially harmful substances. Calcium carbide ; Computer science ; Ripening ; Android application ; Process (computing) ; Artificial intelligence ; Android (operating system) ; Chemistry ; Food science ; Operating system Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Currency Recognition and Detection
Authors: Conrad Szczuka | Rüdiger‐A. Eichel | Josef Granwehr
Source: RSC Advances (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Gauging the importance of structural parameters for hyperfine coupling constants in organic radicals The identification of fundamental relationships between atomic configuration and electronic structure typically requires experimental empiricism or systematic theoretical studies. Here, we provide an alternative statistical approach to gauge the importance of structure parameters, <i>i.e.</i>, bond lengths, bond angles, and dihedral angles, for hyperfine coupling constants in organic radicals. Hyperfine coupling constants describe electron-nuclear interactions defined by the electronic structure and are experimentally measurable, for example, by electron paramagnetic resonance spectroscopy. Importance quantifiers are computed with the machine learning algorithm <i>neighborhood components analysis</i> using molecular dynamics trajectory snapshots. Atomic-electronic structure relationships are visualized in matrices correlating structure parameters with coupling constants of all magnetic nuclei. Qualitatively, the results reproduce common hyperfine coupling models. Tools to use the presented procedure for other radicals/paramagnetic species or other atomic structure-dependent parameters are provided. Hyperfine coupling ; Radical ; Hyperfine structure ; Coupling constant ; Coupling (piping) ; Chemistry ; Computational chemistry ; Atomic physics ; Chemical physics ; Materials science ; Physics ; Quantum mechanics ; Organic chemistry ; Metallurgy Free Radicals and Antioxidants ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Xueqiong Fu | Yuhan Mao | Ning Wang | Gang Li | Rong Sun | Jibao Lu
Source: Powder Technology (article, 2023-05-19)
Field: Materials Science — Polymers and Plastics

Efficient numerical and ANN models for optimization of filler gradation of particulate-filled composites Gradation ; Composite material ; Ternary operation ; Filler (materials) ; Materials science ; Sphere packing ; Composite number ; Viscosity ; Particulates ; Discrete element method ; Mechanics ; Computer science ; Chemistry ; Physics Polymer crystallization and properties ; Advanced Sensor and Energy Harvesting Materials ; Electronic Packaging and Soldering Technologies
Authors: Shashank Saxena | Jan-Hendrik Bastek | Miguel Spínola | Prateek Gupta | Dennis M. Kochmann
Source: Mechanics of Materials (article, 2023-05-05)
Field: Materials Science — Materials Chemistry

GNN-assisted phase space integration with application to atomistics Overcoming the time scale limitations of atomistics can be achieved by switching from the state-space representation of Molecular Dynamics (MD) to a statistical-mechanics-based representation in phase space, where approximations such as maximum-entropy or Gaussian phase packets (GPP) evolve the atomistic ensemble in a time-coarsened fashion. In practice, this requires the computation of expensive high-dimensional integrals over all of phase space of an atomistic ensemble. This, in turn, is commonly accomplished efficiently by low-order numerical quadrature. We show that numerical quadrature in this context, unfortunately, comes with a set of inherent problems, which corrupt the accuracy of simulations—especially when dealing with crystal lattices with imperfections. As a remedy, we demonstrate that Graph Neural Networks, trained on Monte-Carlo data, can serve as a replacement for commonly used numerical quadrature rules, overcoming their deficiencies and significantly improving the accuracy. This is showcased by three benchmarks: the thermal expansion of copper, the martensitic phase transition of iron, and the energy of grain boundaries. We illustrate the benefits of the proposed technique over classically used third- and fifth-order Gaussian quadrature, highlight the impact on time-coarsened atomistic predictions, and discuss the computational efficiency. The latter is of general importance when performing frequent evaluation of phase space or other high-dimensional integrals, which is why the proposed framework promises applications beyond the scope of atomistics. Phase space ; Gaussian quadrature ; Statistical physics ; Numerical integration ; Gaussian ; Computer science ; Computation ; Quadrature (astronomy) ; Monte Carlo method ; Thermodynamic integration ; Applied mathematics ; Algorithm ; Mathematics ; Physics ; Nyström method ; Mathematical analysis ; Energy (signal processing) Machine Learning in Materials Science ; Protein Structure and Dynamics ; X-ray Diffraction in Crystallography
Authors: Tom Kirstein | Lukas Petrich | Ravi Raj Purohit Purushottam Raj Purohit | Jean‐Sébastien Micha | Volker Schmidt
Source: Materials (article, 2023-04-26)
Field: Materials Science — Materials Chemistry

CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation Laue microdiffraction is an X-ray diffraction technique that allows for the non-destructive acquisition of spatial maps of crystallographic orientation and the strain state of (poly)crystalline specimens. To do so, diffraction patterns, consisting of thousands of Laue spots, are collected and analyzed at each location of the spatial maps. Each spot of these so-called Laue patterns has to be accurately characterized with respect to its position, size and shape for subsequent analyses including indexing and strain analysis. In the present paper, several approaches for estimating these descriptors that have been proposed in the literature, such as methods based on image moments or function fitting, are reviewed. However, with the increasing size and quantity of Laue image data measured at synchrotron sources, some datasets become unfeasible in terms of computational requirements. Moreover, for irregular Laue spots resulting, e.g., from overlaps and extended crystal defects, the exact shape and, more importantly, the position are ill-defined. To tackle these shortcomings, a procedure using convolutional neural networks is presented, allowing for a significant acceleration of the characterization of Laue spots, while simultaneously estimating the quality of a Laue spot for further analyses. When tested on unseen Laue spots, this approach led to an acceleration of 77 times using a GPU while maintaining high levels of accuracy. Diffraction ; Position (finance) ; Spots ; Synchrotron ; Crystal (programming language) ; X-ray crystallography ; Orientation (vector space) ; Optics ; Computer science ; Convolutional neural network ; Artificial intelligence ; Materials science ; Algorithm ; Physics ; Mathematics ; Geometry ; Chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Mineral Processing and Grinding