jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matylda Anna Izert | Maria Klimecka | Anna Antosiewicz | Karol Wróblewski | Jakub Jozef Kowalski | Katarzyna Bandyra | Tomasz Goral | Sebastian Kmiecik | Remigiusz Serwa | Maria W. Górna
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-03-03)
Field: Chemistry — Spectroscopy

Targeted Protein Degradation in Escherichia coli - depletion of the essential GroEL using CLIPPERs New, universal tools for targeted protein degradation in bacteria can help to accelerate protein function studies and antimicrobial research. We have developed a new method for degrading bacterial proteins using plasmid-encoded degrader peptides which deliver target proteins for degradation by a highly conserved ClpXP protease. We demonstrated the mode of action of the degraders on a challenging essential target, GroEL. The studies in bacteria were complemented by in vitro binding and structural studies. Expression of degrader peptides resulted in a temperature-dependent growth inhibition and depletion of GroEL levels over time. The reduction of GroEL levels was accompanied by dramatic proteome alterations. The presented method offers a new alternative approach for regulating protein levels in bacteria without genomic modifications or tag fusions. Our studies demonstrate that ClpXP is an attractive protease for the future use in bacterial targeted protein degradation. GroEL ; Escherichia coli ; Proteome ; Protease ; Biology ; Bacteria ; Protein degradation ; Chaperonin ; Biochemistry ; Microbiology ; Protein folding ; Enzyme ; Gene ; Genetics Advanced Proteomics Techniques and Applications ; Protein Degradation and Inhibitors ; Ubiquitin and proteasome pathways
Authors: Philipp Guevorguian | Menua Bedrosian | Tigran Fahradyan | Gayane Chilingaryan | Hrant Khachatrian | Armen Aghajanyan
Source: arXiv (Cornell University) (preprint, 2024-07-26)
Field: Materials Science — Materials Chemistry

Small Molecule Optimization with Large Language Models Recent advancements in large language models have opened new possibilities for generative molecular drug design. We present Chemlactica and Chemma, two language models fine-tuned on a novel corpus of 110M molecules with computed properties, totaling 40B tokens. These models demonstrate strong performance in generating molecules with specified properties and predicting new molecular characteristics from limited samples. We introduce a novel optimization algorithm that leverages our language models to optimize molecules for arbitrary properties given limited access to a black box oracle. Our approach combines ideas from genetic algorithms, rejection sampling, and prompt optimization. It achieves state-of-the-art performance on multiple molecular optimization benchmarks, including an 8% improvement on Practical Molecular Optimization compared to previous methods. We publicly release the training corpus, the language models and the optimization algorithm. Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Caitlin E. Shields | Thomas Fellowes | Anna G. Slater | Andrew I. Cooper | Keith G. Andrews | Filip Szczypiński
Source: ChemRxiv (preprint, 2024-03-27)
Field: Chemistry — Organic Chemistry

Exploration of the polymorphic solid-state landscape of an amide-linked organic cage using computation and automation Organic cages can possess complex, functionalised internal cavities that make them promising candidates for synthetic enzyme mimics. Conformationally flexible but chemically robust structures are needed for adaptable guest binding and catalysis, but these rapidly exchanging systems are difficult to resolve in solution. Here, we use inexpensive calculations and high-throughput crystallisation experiments to identify accessible cage conformations for a recently reported organic cage by ‘locking’ them in the solid state. The conformers identified exhibit a range of distances between the carboxylic acid groups in the internal cavity, suggesting adaptability towards binding a wide array of target guest molecules. The complexity of the observed crystal structures goes beyond what is possible with state-of-the-art crystal structure prediction. Solid-state ; Adaptability ; Conformational isomerism ; Computation ; Cage ; Molecule ; Amide ; Crystal engineering ; Chemistry ; Chemical physics ; Crystallization ; Materials science ; Combinatorial chemistry ; Crystal structure ; Crystallography ; Topology (electrical circuits) ; Computer science ; Supramolecular chemistry ; Organic chemistry ; Algorithm ; Engineering ; Biology Supramolecular Chemistry and Complexes ; Chemical Synthesis and Analysis ; Supramolecular Self-Assembly in Materials
Authors: Alexander Azarov | Javier García‐Fernández | J. Zhao | Ru He | Ji‐Hyeon Park | Dae‐Woo Jeon | Øystein Prytz | Flyura Djurabekova | Andrej Kuznetsov
Source: arXiv (Cornell University) (preprint, 2024-09-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase glides and self-organization of atomically abrupt interfaces out of stochastic disorder in $α$-Ga$_{2}$O$_{3}$ Disorder-induced ordering and unprecedentedly high radiation tolerance in $γ$-phase of gallium oxide is a recent spectacular discovery at the intersection of the fundamental physics and electronic applications. Importantly, by far, these data were collected with initial samples in form of the thermodynamically stable $β$-phase of this material. Here, we investigate these phenomena starting instead from already metastable $α$-phase and explain radically new trend occurring in the system. We argue that in contrast to that in $β$-to-$γ$ disorder-induced transitions, the O sublattice in $α$-phase exhibits hexagonal close-packed structure, so that to activate $α$-to-$γ$ transformation significant structural rearrangements are required in both Ga and O sublattices. Moreover, consistently with theoretical predictions, $α$-to-$γ$ phase transformation requires accumulation of the substantial tensile strain to initiate otherwise impossible lattice glides. Thus, we explain the experimentally observed trends in term of the combination of disorder and strain governing the process. Finally, and perhaps most amazingly, we demonstrate atomically abrupt $α$/$γ$ interfaces paradoxically self-organized out of the stochastic disorder. Alpha (finance) ; Phase (matter) ; Materials science ; Condensed matter physics ; Crystallography ; Psychology ; Physics ; Chemistry ; Developmental psychology ; Quantum mechanics Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Yu.V. Pustovit | Ye.P. Lytveniuk
Source: Ukrainian Journal of Physics (article, 2024-02-06)
Field: Materials Science — Materials Chemistry

Neural-Network-Based Methods for ARPES Data Processing (Review Article) In recent years, many developed upgrades of angle-resolved photoemission spectroscopy (ARPES) have significantly increased the amount of the obtained data. In this article, we briefly review the methods of processing of ARPES spectra with the use of convolutional neural networks (CNNs). In addition, we have made a short checkup of the potential application of CNNs that outperforms the existing methods or gives the possibility to achieve previously unachievable results. Angle-resolved photoemission spectroscopy ; Convolutional neural network ; Artificial neural network ; Computer science ; Photoemission spectroscopy ; Spectral line ; Artificial intelligence ; Electronic structure ; Physics ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Spectroscopy and Laser Applications
Authors: Xiaorong Sun | Yiran Hu | Cuiling Liu | Shanzhe Zhang | Sining Yan | Xuecong Liu | Kun Zhao
Source: Preprints.org (preprint, 2024-04-03)
Field: Chemistry — Analytical Chemistry

Characterizing Edible Oils by Oblique-Incidence Reflectivity Difference Combined with Machine Learning Algorithms Due to the significant price difference, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models exceeded 95%. Experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils. Reflectivity ; Oblique case ; Incidence (geometry) ; Algorithm ; Computer science ; Artificial intelligence ; Machine learning ; Optics ; Physics Spectroscopy and Chemometric Analyses
Authors: Mengmeng Qiao | Guoyi Xia | Yang Xu | Tao Cui | Chenlong Fan | Yibo Li | Shaoyun Han | Jun Qian
Source: Journal of the Science of Food and Agriculture (article, 2024-03-23)
Field: Chemistry — Analytical Chemistry

Prediction of moisture content for a single maize kernel based on viscoelastic properties This study confirms the potential for accurate and fast detection of moisture content in single maize kernels using viscoelastic properties, which provides a novel approach for the detection of various components in cereals. © 2024 Society of Chemical Industry. Viscoelasticity ; Water content ; Calibration ; Mathematics ; Kernel (algebra) ; Partial least squares regression ; Relaxation (psychology) ; Graph ; Mean squared error ; Biological system ; Statistics ; Computer science ; Algorithm ; Materials science ; Engineering ; Composite material Spectroscopy and Chemometric Analyses ; Agricultural Engineering and Mechanization ; Meat and Animal Product Quality
Authors: Aleksei Makogon | Frédéric Kanoufi | Viacheslav Shkirskiy
Source: ChemRxiv (preprint, 2024-01-10)
Field: Materials Science — Materials Chemistry

Is Unsupervised Dimensionality Reduction Sufficient to Decode the Complexities of Electrochemical Impedance Spectra? As electrochemical research undergoes rapid technological progression, the acquisition of substantial amounts of electrochemical impedance spectra (EIS) becomes increasingly feasible. Yet, this advancement introduces intricate challenges in data processing, automation, and interpretation. This paper delves into the sufficiency of unsupervised machine learning (ML) and in particular dimensionality reduction methods in decoding EIS complexities, examining its strengths, limitations, and potential pathways for optimization. As we navigated the intricacies of non-linear dimensionality reduction, spotlighting t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) algorithms, a pattern emerged: these techniques excel at categorizing divergent impedance spectra but show limitations when faced with analogous circuit configurations, especially those substituting a capacitor with a constant phase element. This observation not only underscores a limitation but also accentuates that unsupervised ML approaches, alone, may not fully unravel the nuances of EIS spectra. In the concluding section of our manuscript, we discuss the implications of this finding from a practical standpoint, particularly for electrochemists seeking to apply these methods in their work. Dimensionality reduction ; Nonlinear dimensionality reduction ; Computer science ; Reduction (mathematics) ; Unsupervised learning ; Electrical impedance ; Decoding methods ; Dielectric spectroscopy ; Artificial intelligence ; Curse of dimensionality ; Projection (relational algebra) ; Pattern recognition (psychology) ; Algorithm ; Machine learning ; Electrochemistry ; Chemistry ; Mathematics ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Machine Learning and ELM ; Neural Networks and Applications
Authors: A. Valiev | И А Старовойтова | A.M. Suleymanov
Source: Bulletin of Belgorod State Technological University named after V G Shukhov (article, 2024-01-31)
Field: Materials Science — Materials Chemistry

ADHESION INTERACTION IN HYBRID COMPOSITE. RELATION OF ENERGY CHARACTERISTICS OF PHASES WITH STRENGTH Improvement of stiffness characteristics of polymer composites is conditioned by the development of hybrid composites (containing more than one type of reinforcing continuous fiber) with the provision of effective stress transfer from fibers to binder through the interface. The study of mechanisms of formation of adhesion interaction in hybrid polymer composite consists in purposeful change of energy characteristics of phases in order to achieve the optimal level of adhesion, affecting the strength of composites while providing technological and other factors, predicting their durability. In this work the relationship between the energy characteristics of phases and the strength of hybrid polymer composite was investigated. Microphotographs of wetting of fibers of different nature by liquids were obtained by the optical method, by which the edge angles of wetting were determined, tests were carried out to determine the bending strength of polymer composites. A method for predicting the flexural strength of hybrid polymer composites made by vacuum infusion method has been proposed and experimentally confirmed, which consists in linking the adhesive interaction of components, structural components and flexural strength by determining the change in their energy characteristics of the filler by wetting method. Materials science ; Composite material ; Flexural strength ; Composite number ; Wetting ; Polymer ; Adhesion ; Bending ; Durability ; Adhesive ; Fiber ; Layer (electronics) Machine Learning in Materials Science ; Industrial Engineering and Technologies ; Material Properties and Failure Mechanisms
Authors: Andreas H. Franz | Kendall S. Bromley | Ei T. Aung | Q. L. Stephen | Hana M. Rosenblatt | Amelia Watson
Source: Magnetic Resonance in Chemistry (article, 2024-10-16)
Field: Chemistry — Organic Chemistry

NMR Coupling Constants, Karplus Equations, and Adjusted MD Statistics: Detecting Diagnostic Torsion Angles for the Solution Geometry of 6‐[α‐ <scp>d</scp> ‐Mannopyranosyl]‐ <scp>d</scp> ‐Mannopyranose (Mannobiose) The quantitative solution conformations of 2-(hydroxymethyl)-tetrahydropyran, α-methyl-d-mannopyranoside, and 6-[α-d-mannopyranosyl]-d-mannopyranose (mannobiose) are described. Parametrized Karplus equations for redundant spin pairs across the terminal ω-torsion and the glycosidic ω-torsion for mannobiose are developed, including ω/θ-hypersurfaces for the terminal hydroxymethylene group. Experimental NMR data, algorithmic spectral simulation (clustered Hamiltonian method), molecular dynamics (MD) simulations (GLYCAM06), energy minimizations by DFT, and adjusted torsion angle populations weighted over the Karplus-type equations are used. We demonstrate that spectral simulation is a powerful tool in the refinement of initial J values obtained from static GAIO DFT calculations. We also show that only as few as one of multiple redundant torsions can be diagnostic for conformational analysis of the disaccharide. Chemistry ; Disaccharide ; Glycosidic bond ; Torsion (gastropod) ; Dihedral angle ; Hamiltonian (control theory) ; Computational chemistry ; Coupling constant ; Molecular dynamics ; Stereochemistry ; Molecule ; Physics ; Organic chemistry ; Hydrogen bond ; Quantum mechanics Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Protein Structure and Dynamics
Authors: Jan Leffler | Jan Kaska | Petr Kadlec | Pavel Prosr | Václav Šmídl | Pavel Trnka
Source: Polymers (article, 2024-07-16)
Field: Materials Science — Materials Chemistry

Hygrothermal Degradation of Epoxy Electrical Insulating Material—Testing and Mathematical Modeling The degradation of electrical insulating materials has been a subject of interest for decades as they are commonly applied in many fields of electrical engineering. Suitably modeling such a process is important since the known and well-described degradation process reveals the effect of ambient conditions, and this allows us to possibly estimate a material's remaining useful life. However, not many studies are dealing with the effect of the hygrothermal degradation of impregnating mono-component epoxy resins in the context of electrical engineering. Therefore, this study deals with this issue and discusses both the dielectric response (based on the measurement of relative permittivity, dissipation factor, and dielectric strength) and the mechanical response (based on measurements of tensile strength and Shore D hardness) to a hygrothermal degradation experiment. In addition, the results of thermal analyses are presented for the evaluation of the pristine specimen manufacturing process and possible post-curing processes. Furthermore, this study presents several methodologies for modeling the degradation process, including a novel methodology in this area based on Bayesian experimental design. As an outcome, mechanical parameters are proven to be specific in terms of the actual condition of the material and the Bayesian enhanced degradation model seems to be superior to the conventional evaluation methods in this particular study. Materials science ; Epoxy ; Degradation (telecommunications) ; Context (archaeology) ; Composite material ; Relative permittivity ; Curing (chemistry) ; Dissipation factor ; Dielectric ; Permittivity ; Computer science ; Mechanical engineering ; Process engineering ; Engineering High voltage insulation and dielectric phenomena ; Non-Destructive Testing Techniques ; Concrete Corrosion and Durability
Authors: Ali Khumaidi | Ridwan Raafi’udin | Nusa Setiani Triastuti
Source: Instrumentation Mesure Métrologie (article, 2024-12-24)
Field: Chemistry — Analytical Chemistry

Enhancing Ship Coating Quality Detection via Machine Learning-Optimized Visible Near-Infrared Spectroscopy This study proposes a portable Visible/Near-Infrared (Vis/NIR) spectroscopy-based approach to detect and evaluate the quality of ship coatings.Vis/NIR spectroscopy offers an accurate, non-destructive method for identifying coating conditions through spectral data acquisition, combined with machine learning analysis to improve detection performance.In this study, using a device with a wavelength of 410-940 nm, spectral transformations such as scatter correction, baseline correction, smoothing, and derivative were applied to improve data quality, followed by feature selection using PCA and IFS.SVM, Random Forest (RF), and LDA classification algorithms were then used to model spectral data.The coating quality consists of four classes, with 40 samples for each.The initial results of modeling without treatment were improved with an average accuracy of 83.90%.Then, applying the combination of Nippy and IFS significantly increases average accuracy results by 96.86%.Incorporating spectral transformation and feature selection methods can optimally utilize spectral information and improve the model's overall performance with an increase in accuracy of 12.96%. Infrared ; Coating ; Spectroscopy ; Infrared spectroscopy ; Quality (philosophy) ; Materials science ; Computer science ; Optics ; Chemistry ; Nanotechnology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Thermography and Photoacoustic Techniques
Authors: Ruiguang Pan | Chen Zhu
Source: arXiv (Cornell University) (preprint, 2024-05-06)
Field: Materials Science — Materials Chemistry

Linear correlations of Gibbs free energy for rare earth element oxide, hydroxide, chloride, fluoride, carbonate, and ferrite minerals and crystalline solids Rare Earth Elements (REE) are critical minerals (metals) for the transition from fossil fuels to renewable and clean energy. Accurate thermodynamic properties of REE minerals and other crystalline solids are crucial for geochemical modeling of the solubility, speciation, and transport of REE in ore formation, extraction, chemical processing, and recycling processes. However, the Gibbs free energies of formation (DGof, REEX) for these solids from different sources vary by 10s kJ/mol. We applied the Sverjensky linear free energy relationship (LFER) to evaluate their internal consistency and predict the unavailable DGof of the REE solids. By considering both the effects of ionic radius size and corresponding aqueous ion properties, the Sverjensky LFER allows estimates with much accuracy and precision. Here, rREEZ+ represents the Shannon-Prewitt ionic radii of REEZ+, and DGon, REEZ+ denotes the non-solvation contribution to the DGof of the aqueous REEZ+ ion. X represents the remainder of the compounds. In this study, the parameters aREEX, bREEX, and beta REEX were regressed from DGof compilations in the literature for 13 isostructural families. Based on these linear relationships, we recommend a set of internally consistent DGof, REEX for 119 end-members of REE oxides, hydroxides, chlorides, fluorides, carbonates, hydrous carbonates, and ferrites. These DGof, REEX are combined with experimental or predicted values of So, Vo, and Cpo from the literature and incorporated into a new SUPCRT database, which allows the calculations of thermodynamic properties to high P-T conditions (e.g., up to 1000 oC and 5 kb). Fluoride ; Hydroxide ; Carbonate ; Gibbs free energy ; Chloride ; Oxide ; Rare-earth element ; Ferrite (magnet) ; Materials science ; Mineralogy ; Inorganic chemistry ; Thermodynamics ; Rare earth ; Chemistry ; Metallurgy ; Composite material ; Physics Thermal and Kinetic Analysis ; Numerical methods in inverse problems ; Chemical Thermodynamics and Molecular Structure
Authors: Joshua L. Ryan | Gabriele C. Sosso | Stefan A. F. Bon
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Small molecule organic eutectics as candidates to replace plastics Legislative change and shifting consumer sentiment drive a need to replace polymers in certain products. Herein, we highlight that eutectic molecular glasses and liquids are promising but underutilized candidate materials. We formulate a series of hydrophobic eutectic molecular liquids and glasses by mixing their crystalline components. The eutectic composition of each mixture was determined by both differential scanning calorimetry (DSC) and UV-vis spectroscopic measurements, which were processed and analyzed using a trained partial least squares regression model. With product shelf-life in mind, the long-term stability (up to 14 months) of the amorphous materials towards crystallization was proven using powder X-ray diffraction (PXRD). Molecular dynamics (MD) simulations put forward potential design rules in terms of the physical stability of these glasses. Rheological properties were investigated from the perspective of processability. Low fragility indices were found for all liquids, aiding processability through glassblowing, fiber pulling, film formation and molding. We show that properties can be tailored by blending two different eutectic systems or simply adding a plasticizer. To demonstrate a potential application area, the 4-hydroxychalcone and bifonazole eutectic system was used as a matrix for controlled release studies of a model active ingredient. Eutectic system ; Organic molecules ; Molecule ; Mixing (physics) ; Materials science ; Polymer ; Chemical engineering ; Small molecule ; Polymer science ; Polymer chemistry ; Organic chemistry ; Chemistry ; Composite material ; Microstructure ; Engineering ; Physics Material Dynamics and Properties ; Ionic liquids properties and applications ; Phase Equilibria and Thermodynamics