jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stephanie Felten | Cyndi Qixin He | Mark Weisel | Michael Shevlin | Marion H. Emmert
Source: ChemRxiv (preprint, 2022-09-20)
Field: Chemistry — Organic Chemistry

Accessing diverse azole carboxylic acid building blocks via mild C-H carboxylation: Parallel, one-pot amide couplings and ML-guided substrate scope design This manuscript describes a mild, functional group tolerant, and metal-free C-H carboxylation that enables direct access to azole-2-carboxylic acids, followed by amide couplings in one pot. This sequence accesses a large variety of azole-2-amides, demonstrating the significant expansion of the accessible chemical space, as compared to previously known methodologies. Key to the described reactivity is the use of silyl triflate reagents, which serve as reaction mediators in C-H deprotonation and stabilizers of (otherwise unstable) azole carboxylic acid intermediates. A diverse azole substrate scope designed via ma-chine learning-guided analysis demonstrates the broad utility of the sequence. DFT calculations provide insights into the role of silyl triflates in the reaction mechanism. Transferrable applications of the protocol are successfully established: (i) A low pressure (CO2 balloon) option for synthesizing azole-2-carboxylic acids without the need for high-pressure equipment; (ii) the use of 13CO2 for the synthesis of labeled compounds; and (iii) isocyanates as alternative electrophiles for direct C-H amidation. Fundamentally, the reported protocol expands the use of heterocycle C-H functionalization from late-stage functionalization applications towards its use in library synthesis. It provides general access to densely functionalized azole-2-carboxylic acid building blocks and demonstrates their one-pot use in diversifying amide couplings. Carboxylation ; Azole ; Chemistry ; Silylation ; Amide ; Carboxylic acid ; Deprotonation ; Combinatorial chemistry ; Electrophile ; Substrate (aquarium) ; Reagent ; Trifluoromethanesulfonate ; Reactivity (psychology) ; Organic chemistry ; Catalysis Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Anna Badura | Jerzy Krysiński | Alicja Nowaczyk | Marta Poćwiardowska-Głąb | Adam Buciński
Source: Research Square (preprint, 2022-12-01)
Field: Chemistry — Organic Chemistry

Application of artificial neural networks for predicting imidazole derivatives antimicrobial activity against Enterococcus faecalis Abstract The paper discusses artificial neural networks (ANNs) as a useful deep learning (DL) method to assist researchers in the search for new therapeutic and disinfectant substances. Two ANN models have been designed to predict the biological activity of the compounds based on their physicochemical properties and their structure. The said activity was tested against Enterococcus faecalis bacteria on a series of 140 imidazole derivatives. The regression model designed, predicted the minimum growth inhibitory concentration of E. faecalis (regression model: training data R = 0.91; test data R = 0.91; validation data R = 0.97). The classification model, on the other hand, divided the tested compounds into active or inactive against the tested microorganism predictive (classification accuracy: 92.86%). The exponential demand for new compounds in the pharmaceutical industry, requires alternative experimental methods to reduce the time and cost of development. Therefore, this paper proposes ANN as an alternative to standard techniques for predicting complex biological phenomena. Enterococcus faecalis ; Artificial neural network ; Antimicrobial ; Biochemical engineering ; Regression ; Artificial intelligence ; Imidazole ; Machine learning ; Computer science ; Biological system ; Quantitative structure–activity relationship ; Data mining ; Mathematics ; Chemistry ; Engineering ; Bacteria ; Biology ; Statistics ; Stereochemistry ; Organic chemistry Antimicrobial agents and applications ; Advanced Chemical Sensor Technologies ; Bacterial biofilms and quorum sensing
Authors: Yuanqi Du | Xian Liu | Shengchao Liu | Jieyu Zhang | Bolei Zhou
Source: ChemRxiv (preprint, 2022-06-23)
Field: Materials Science — Materials Chemistry

ChemSpacE: Toward Steerable and Interpretable Chemical Space Exploration Discovering new structures in the chemical space is a long-standing challenge and has important applications to various fields such as chemistry, material science, and drug discovery. Deep generative models have been used in de novo molecule design to embed molecules in a meaningful latent space and then sample new molecules from it. However, the steerability and interpretability of the learned latent space remain much less explored. In this paper, we introduce a new task named molecule manipulation, which aims to align the molecular properties of the generated molecule and its latent activation in order to achieve interactive molecule editing. Then we develop a method called Chemical Space Explorer (ChemSpacE), which identifies and traverses interpretable directions in the latent space that align with molecular structures and property changes. Specifically, ChemSpacE leverages the properties of the learned latent space by generative models and utilizes linear models to identify such directions and thus is highly efficient in terms of training/inference time, data, and the number of oracle calls. Experiments show that ChemSpacE can efficiently steer the latent spaces of multiple state-of-the-art molecule generative models for interactive molecule discovery. Chemical space ; Interpretability ; Space (punctuation) ; Generative model ; Computer science ; Inference ; Oracle ; Generative grammar ; Artificial intelligence ; Machine learning ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research
Authors: Sinem Erden Gülebağlan
Source: Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi (article, 2022-03-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Insights into the Prediction Structural, Electronic, Optic, Elastic, and Phonon Properties of Half-Heusler Compound LiAgSe via Density Functional Theory The structural, electronic, optic, elastic and dynamic features of LiAgSe half-Heusler structure are studied by using first principle calculations. LiAgSe half-Heusler compound is examined with the Generalized Gradient Approximation using the Density Functional Theory. The Quantum Espresso simulation program is preferred to investigate its structural, electronic and dynamic features. The ABINIT simulation program is preferred to investigate its elastic and optic properties. The electronic band structure graph of the LiAgSe crystal formed as a result of the calculation shows that this crystal has a semi-metallic structure. Optic properties such as, complex dielectric constant, extinction coefficient, reflectivity, for the volume of LiAgSe are calculated and plotted. In this study, elastic constants, Poisson's ratio and Debye Temperature values of LiAgSe half-Heusler crystal are determined. Apart from these, phonon dispersion curve graph is obtained. It has been calculated that the LiAgSe half-Heusler crystal is not dynamically stable in the ground state. However, when applied a pressure under nearly 16.396 GPa the crystal becomes stable. Phonon ; Debye model ; Condensed matter physics ; Materials science ; Density functional theory ; Crystal (programming language) ; Debye ; Electronic band structure ; Electronic structure ; Physics ; Quantum mechanics Heusler alloys: electronic and magnetic properties ; 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Alireza Moradzadeh | Hananeh Oliaei | N. R. Aluru
Source: arXiv (Cornell University) (preprint, 2022-04-15)
Field: Materials Science — Materials Chemistry

Topology-based Phase Identification of Bulk, Interface, and Confined Water using Edge-Conditioned Convolutional Graph Neural Network Water plays a significant role in various physicochemical and biological processes. Understanding and identifying water phases in various systems such as bulk, interface, and confined water is crucial in improving and engineering state-of-the-art nano-devices. Various order parameters have been developed to distinguish water phases, including bond-order parameters, local structure index, and tetrahedral order parameters. These order parameters are often developed with the assumption of homogenous bulk systems, while most applications involve heterogeneous and non-bulk systems, thus, limiting their generalizability. Our study develops a methodology based on the graph neural network to distinguish water phases directly from data and to learn features instead of predefining them. We provide comparisons between baseline methods trained using conventional order-parameters as features, and a graph neural network model trained using radial distance and hydrogen-bonding information for phase classification and phase transition of water in bulk, interface, and confined systems with continuous and discontinuous phase transitions. Artificial neural network ; Computer science ; Convolutional neural network ; Graph ; Topology (electrical circuits) ; Network topology ; Generalizability theory ; Phase (matter) ; Phase transition ; Algorithm ; Biological system ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Physics ; Thermodynamics Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Neural Networks and Applications
Authors: Maryam Zamanian | Hassan Sadrnia | Mehdi Khojastehpour | Abbas Rohani | Jules Thibault | Fereshte Hosseini
Source: Research Square (preprint, 2022-05-19)
Field: Materials Science — Biomaterials

Optimization of PVA/TiO2/MMT mixed matrix membrane for food packaging Abstract Nanocomposite films performance parameters, including barrier and properties for packaging films, can be affected by variables such as the type and concentration of nanoparticles. In this investigation, Polyvinyl alcohol (PVA) nanocomposite films were prepared by solution casting method with different combinations of Montmorillonite (MMT) platelets and Titanium Oxide (TiO 2 ) spherical nanoparticles.A support vector machine (SVM) was implemented to study the thin nanocomposite films' behavior to changes in the independent variables. The SVM model predicted oxygen transmission rate (OTR), water vapor permeability (WVP|), Young ̓s Modulus (YM), ΔE, opacity, tensile strength (TS), and elongation at the breakpoint (EB) with an error of less than 6.43%. A Genetic Algorithm (GA) was applied to find the optimal nanoparticle concentration to achieve optimum film performance. Therefore, the results exhibited that the optimum film performance depends on the type and concentration of nanoparticles. Results show that the optimum loading of nanoparticles in this research should be between 0.5-1 wt% for TiO 2 and 2.5–3.5 wt% for MMT. Food packaging ; Matrix (chemical analysis) ; Membrane ; Food science ; Materials science ; Composite material ; Polymer science ; Chemistry Nanocomposite Films for Food Packaging ; Conducting polymers and applications ; biodegradable polymer synthesis and properties
Authors: Yuanqi Du | Xian Liu | Shengchao Liu | Jieyu Zhang | Bolei Zhou
Source: ChemRxiv (preprint, 2022-06-22)
Field: Materials Science — Materials Chemistry

ChemSpacE: Toward Steerable and Interpretable Chemical Space Exploration Discovering new structures in the chemical space is a long-standing challenge and has important applications to various fields such as chemistry, material science, and drug discovery. Deep generative models have been used in de novo molecule design to embed molecules in a meaningful latent space and then sample new molecules from it. However, the steerability and interpretability of the learned latent space remain much less explored. In this paper, we introduce a new task named molecule manipulation, which aims to align the molecular properties of the generated molecule and its latent activation in order to achieve interactive molecule editing. Then we develop a method called Chemical Space Explorer (ChemSpacE), which identifies and traverses interpretable directions in the latent space that align with molecular structures and property changes. Specifically, ChemSpacE leverages the properties of the learned latent space by generative models and utilizes linear models to identify such directions and thus is highly efficient in terms of training/inference time, data, and the number of oracle calls. Experiments show that ChemSpacE can efficiently steer the latent spaces of multiple state-of-the-art molecule generative models for interactive molecule discovery. Chemical space ; Interpretability ; Generative model ; Space (punctuation) ; Computer science ; Inference ; Generative grammar ; Oracle ; Artificial intelligence ; Machine learning ; Drug discovery ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research
Authors: Jordi Ibáñez
Source: Minerals (article, 2022-09-16)
Field: Materials Science — Materials Chemistry

First-Principles Calculations of Minerals and Related Materials As stated in their announcements and accompanying information, Special Issues published in scientific journals are usually aimed at compiling recent progress on highly specialized topics [...] Earth science ; Data science ; Engineering ethics ; Computer science ; Geology ; Engineering X-ray Diffraction in Crystallography ; Crystal Structures and Properties ; High-pressure geophysics and materials
Authors: Neeraja M. Krishnan | Saroj Kumar | Ujjwal Kumar | Binay Panda
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-10-17)
Field: Chemistry — Analytical Chemistry

Single and multi-analyte deep learning-based analysis framework for class prediction in biological images Abstract Measurement of biological analytes, characterizing flavor in fruits, is a cumbersome, expensive and time-consuming process. Fruits with higher concentration of analytes have greater commercial or nutritional values. Here, we tested a deep learning-based framework with fruit images to predict the class (sweet or sour and high or low) of analytes using images from two types of trees in a single and multi-analyte mode. We used fruit images from kinnow ( n = 3,451), an edible hybrid mandarin and neem ( n = 1,045), a tree with agrochemical and pharmaceutical properties. We measured sweetness in kinnows and five secondary metabolites in neem fruits (azadirachtin or A, deacetyl-salannin or D, salannin or S, nimbin or N and nimbolide or E) using a refractometer and high-performance liquid chromatography, respectively. We trained the models for 300 epochs, before and after hyper-parameters’ evolution, using 300 generations with 50 epochs/generation, estimated the best models and evaluated their performance on 10% of independent images. The validation F1score and test accuracies were 0.79 and 0.77, and 82.55% and 60.8%, respectively for kinnow and neem A analyte. A multi-analyte model enhanced the neem A model’s prediction to ‘high’ class when the D:N:S’s combined class predictions were high:low:high and to ‘low’ class when D:N’s combined class predictions were low:high respectively. The test accuracy increased further to ~70% with a 10-fold cross-validation error of 0.257 across ten randomly split train:validation:test sets proving the potential of a multi-analyte model to enhance the prediction accuracy, especially when the numbers of images are limiting. Analyte ; Artificial intelligence ; Mathematics ; Azadirachtin ; Class (philosophy) ; Machine learning ; Pattern recognition (psychology) ; Biological system ; Chromatography ; Chemistry ; Computer science ; Biology ; Agronomy Spectroscopy and Chemometric Analyses ; Food Science and Nutritional Studies ; Postharvest Quality and Shelf Life Management
Authors: Alessandro Crnjar | Aransa Griñén | Shina Caroline Lynn Kamerlin | César A. Ramírez‐Sarmiento
Source: ChemRxiv (preprint, 2022-10-10)
Field: Materials Science — Biomaterials

Conformational Selection of a Tryptophan Side Chain Drives the Generalized Increase in Activity of PET Hydrolases Through a Ser/Ile Double Mutation Polyethylene terephthalate (PET) is the most common polyester plastic in the packaging industry, and a major source of environmental pollution due to its single use. Several enzymes, termed PET hydrolases (PETases), have been found to hydrolyze this polymer at different temperatures, with the enzyme from I. sakaiensis (IsPETase) having optimal catalytic activity at 40ºC. Crystal structures of IsPETase have revealed that the side chain of a conserved tryptophan residue within an active site loop (W185) shifts between 3 conformations to enable substrate binding and product release. This is facilitated by two residues unique to IsPETase, S214 and I218 (S/I). When these residues are inserted into other PETases in place of the otherwise strictly conserved His/Phe (H/F) residues found at their respective positions, they enhance activity and decrease Topt. Herein, we combine conventional molecular dynamics and well-tempered metadynamics simulations to investigate dynamic changes of the S/I and H/F variants of IsPETase, as well as three other mesophilic and thermophilic PETases, at their respective temperature and pH optima. Our simulations show that the S/I insertion both increases the flexibility of active site loop regions harboring key catalytic residues and the conserved Trp, as well as expanding the conformational plasticity of this Trp side chain, allowing the conformational transitions that allow for substrate binding and product release in IsPETase. The observed catalytic enhancement caused by this substitution in other PETases appears to be due to conformational selection, by capturing the conformational ensemble observed in IsPETase. Side chain ; Tryptophan ; Chemistry ; Active site ; Stereochemistry ; Molecular dynamics ; Residue (chemistry) ; Conformational change ; Enzyme kinetics ; Enzyme ; Biophysics ; Biochemistry ; Amino acid ; Polymer ; Computational chemistry ; Biology ; Organic chemistry biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Enzyme Production and Characterization
Authors: M Salter | B Choi | See Govindan | K Choi | See Han | Tcpmt Jan | F Li | G Li | Z Liang | J Li | Z Liu | M See Chen | R Gao | G Xie | H Long | T Wu | P Lu | Y Li | M Tang | J Mao | H Peng | K Li | H See Zhu | B Liu | L Li | G See Lv | M Zhou | R Hou | F Hou | P Li | A Yang | X Zhang | Q Li | K See Lyu | J Cai | R Li | F Wei | W Li | W Qian | Z Wang | W Wang | Y Zhao | D Yu | X Li | W Zhao | X See Luo | S See Chen | K Ning | Xuan | Y | J Hong | Z Fan | Z Li | J Silveira | C Liang | C Chiang | Y Liang | S Liao | H Wang | B Zhou | Z Chen | Z Fu | S Chen | X Yang | C Peng | C Huang
Source: IEEE Transactions on Components Packaging and Manufacturing Technology (article, 2022-12-01)
Field: Materials Science — Polymers and Plastics

2022 Index IEEE Transactions on Components, Packaging and Manufacturing Technology Vol. 12 Index (typography) ; Business ; Manufacturing engineering ; Computer science ; Engineering ; World Wide Web Synthesis and properties of polymers
Authors: Zuraila Iberahim | Mohd Zamzuri Mohammad Zain | Abdul Halim Ismail | Suit Mun Ng | Farah Izzati Subri | Nooraizedfiza Zainon | Elmi Abu Bakar
Source: Journal of Engineering and Science Research (article, 2022-10-30)
Field: Materials Science — Materials Chemistry

Analysis of Multilevel Otsu Thresholding for Corrosion Rate of Anodised AZ91D Magnesium Alloy Magnesium and its alloy are renowned for their high strength-to-weight ratio, excellent machinability and good castability, making it a favourable material for manufacturing various automotive parts. But when exposed to the environment, magnesium and its alloy have a weak ability to resist corrosion. AZ91D magnesium alloy was anodised and immersed in a salt solution to accelerate deterioration. The image of the corroded surface resulting from the immersion test was analysed by quantising the corroded regions. This paper studies the feasibility of an image processing approach using the multilevel Otsu thresholding method by quantising the corroded region on the AZ91D magnesium alloy. This method converts the original colour image to a greyscale image and then converts it to a binary image and RGB image for the multilevel thresholding. The pixel distributions from the multilevel Otsu thresholding display a trend that, with a higher corrosion rate, the pixel’s frequency of occurrence shifted more to the darker regions. This conforms with the Otsu thresholding, where the darker region is identified as the corroded area of the AZ91D magnesium alloy. By implementing the negative image, the binary image is segmented into non-corroded and corroded areas and labelled as 0 (black) and 1 (white,) respectively. As for multilevel Otsu thresholding, the RGB image is assigned in three colours representing different corrosion severity regions. The regions of the corrosion severity are categorised into severe corrosion, moderate corrosion and low corrosion, coloured in dark blue, light blue, and yellow, respectively. Thus, the trend of the corrosion activity can be seen by implementing the multilevel Otsu thresholding and correlating it with the corrosion rate value. Furthermore, obtaining data on corrosion attack severity can provide helpful information in predicting a component’s failure and future maintenance planning. Corrosion ; Materials science ; Thresholding ; Magnesium alloy ; Magnesium ; RGB color model ; Metallurgy ; Castability ; Composite material ; Artificial intelligence ; Alloy ; Computer science ; Image (mathematics) Corrosion Behavior and Inhibition ; Welding Techniques and Residual Stresses ; Non-Destructive Testing Techniques
Authors: Asmaa Ahmed Hatem
Source: International Journal of Health Sciences (article, 2022-09-03)
Field: Materials Science — Materials Chemistry

effectiveness of nano-nickel oxide with manufactured plant extract and its properties on inhibiting the growth of some types of bacteria and fungi isolated from skin infections Nickel oxide nanoparticles prepared in the first and second methods were synthesized and the properties of the solutions were shown. The XRD examination according to the MT values was at the drawing amount (111) 37.2, (200) 44.6, (220) 62.5 corresponding to the international measurements of Miller's Theta2 coefficient, while it was Sem examined the appearance of the shape of the rocky, cork, or spherical and spherical particles with a size of 10-25 nm, the TEM examination gave that nanoparticles with a size not exceeding 0.5-40 nm, the UV test of absorbance and permeability indicated that the solution is homogeneous and very light and the nanomaterial is at a value between 236-800 nm and indicates Energy gap values are 2.0-5.6 ev and that the material is semiconductive, which is evidence of the presence of nickel oxide. The Ftir test shows that the bonding sites for cobalt oxide are at 500-600, and the zeta Potential (ZP) test indicates that all solutions have very small values and that the material is not agglomerating and very light. Absorbance ; Nickel ; Zeta potential ; Materials science ; Nanomaterials ; Nanoparticle ; Nanometre ; Oxide ; Fourier transform infrared spectroscopy ; Nickel oxide ; Chemical engineering ; Nano- ; Nuclear chemistry ; Nanotechnology ; Composite material ; Chemistry ; Metallurgy ; Chromatography Nanoparticles: synthesis and applications ; Pigment Synthesis and Properties