jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ümit H. Kaynar
Source: Inorganic and Nano-Metal Chemistry (article, 2022-05-04)
Field: Chemistry — Inorganic Chemistry

Modeling and optimization for adsorption of thorium (IV) ions using nano Gd:ZnO: application of response surface methodology (RSM) and artificial neural network (ANN) The waste problem created by nuclear materials both in nuclear reactors and after their medical and industrial use is evaluated differently from other wastes because they can harm human and environmental health. In this study, it is aimed to study the adsorption properties of Gd ions doped nano ZnO (Gd/nano-ZnO) material synthesized by microwave assisted ignition method for the adsorption of Thorium (IV) from aqueous medium. We tested how pH (3-8), temperature (20-60 °C), Th (IV) concentration (25-125 mg/L) and adsorbent amount (0.005-0.08 g) affect adsorption efficiency. The best possible combinations of these parameters were examined by Response Surface Methodology (RSM) and Artificial Neural Network (ANN). R2 values for RSM and ANN were 0.9970 and 0.9666, respectively. According to the models, the experimental adsorption capacity under the optimum conditions determined for the RSM and ANN model was found to be 192.62 mg/g and 218.47 mg/g, respectively. Response surface methodology ; Adsorption ; Materials science ; Thorium ; Aqueous solution ; Nano- ; Nuclear chemistry ; Artificial neural network ; Uranium ; Chemistry ; Chromatography ; Composite material ; Metallurgy ; Computer science ; Machine learning ; Physical chemistry Radioactive element chemistry and processing ; Adsorption and biosorption for pollutant removal ; Extraction and Separation Processes
Authors: B. J. Owen | Katherine M. P. Wheelhouse | Grazziela P. Figueredo | Ender Özcan | Simon Woodward
Source: Results in Chemistry (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learnt patterns in rhodium-catalysed asymmetric Michael addition using chiral diene ligands Interpretable featurisation allows Quantitative Structure-Property Relationships (QSPR) between chiral diene ligand structures and product stereoselectivities in rhodium-catalysed asymmetric Michael additions (RhCASA). The machine learning approach developed herein is simple to implement yet provides instructive insights and useful predictions (q2/R2 0.7–0.8). These simple interpretable features are found to give comparable predictive ability to both commercial (Dragon) and molecular signature descriptor library approaches. Strong insights into the specific areas of the chiral diene ligand that engender improved RhCASA process enantioselectivity are achieved. These were more interpretable, to synthetic chemists, than existing ML approaches, greatly facilitating design of new chiral diene-ligands. Diene ; Rhodium ; Ligand (biochemistry) ; Quantitative structure–activity relationship ; Michael reaction ; Combinatorial chemistry ; Chemistry ; Computer science ; Artificial intelligence ; Stereochemistry ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Benyamin Motevalli | Bronwyn Fox | Amanda S. Barnard
Source: Computational Materials Science (article, 2022-05-25)
Field: Materials Science — Materials Chemistry

Charge-dependent Fermi level of graphene oxide nanoflakes from machine learning Graphene ; Fermi level ; Oxide ; Ionic bonding ; Materials science ; Chemical physics ; Charge (physics) ; Passivation ; Fermi energy ; Nanotechnology ; Chemistry ; Physics ; Ion ; Electron ; Layer (electronics) ; Quantum mechanics Graphene research and applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Tianhui Liu | Huixia Shi | Bina Fu | Dong H. Zhang
Source: The Journal of Chemical Physics (article, 2022-12-02)
Field: Materials Science — Materials Chemistry

Quantum dynamics reveal different ligand effects by vibrational excitation in the dissociative chemisorption of HCl on the Au/Ag(111) surface The reactivity and selectivity of bimetallic surfaces are of fundamental importance in industrial applications. Here, we report the first six-dimensional (6D) quantum dynamics study for the role of surface strain and ligand effects on the reactivity of HCl on a strained pseudomorphic monolayer of Au deposited onto a Ag(111) substrate, with the aid of accurate machine learning-based potential energy surfaces. The substitute of Au into Ag changes the location of the transition state; however, the static barrier height remains roughly the same as pure Au(111). The 6D quantum dynamics calculations reveal that the surface strain due to lattice expansion slightly enhances the reactivity. The ligand effect due to electronic structure interactions between Au and Ag substantially suppresses the reactivity of HCl in the ground vibrational state but promotes the reactivity via vibrational excitation at high kinetic energies. This finding can be attributed to more close interaction with Ag atoms at the transition state close to the fcc site, as well as the tight transition-state region, making the vibrational excitation highly efficient in enhancing the reactivity. Our study quantitatively unravels the dynamical origin of reactivity control by two metals, which will ultimately provide valuable insight into the selectivity of the catalyst. Reactivity (psychology) ; Chemistry ; Chemisorption ; Bimetallic strip ; Excitation ; Chemical physics ; Ground state ; Monolayer ; Transition metal ; Physical chemistry ; Catalysis ; Atomic physics ; Adsorption ; Physics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalytic Processes in Materials Science
Authors: Kameyab Raza Abidi | Pekka Koskinen
Source: Physical Review Materials (article, 2022-12-27)
Field: Materials Science — Materials Chemistry

Optimizing density-functional simulations for two-dimensional metals Unlike covalent two-dimensional (2D) materials like graphene, 2D metals have nonlayered structures due to their nondirectional, metallic bonding. While experiments on 2D metals are still scarce and challenging, density-functional theory (DFT) provides an ideal approach to predict their basic properties and assist in their design. However, DFT methods have rarely been benchmarked against metallic bonding at low dimensions. Therefore, to identify optimal DFT attributes for a desired accuracy, we systematically benchmark exchange-correlation functionals from LDA to hybrids and basis sets from plane waves to local basis with different pseudopotentials. With 1D chain, 2D honeycomb, 2D square, 2D hexagonal, and 3D bulk metallic systems, we compare the DFT attributes using bond lengths, cohesive energies, elastic constants, densities of states, and computational costs. Although today most DFT studies on 2D metals use plane waves, our comparisons reveal that local basis with often-used Perdew-Burke-Ernzerhof exchange correlation is quite sufficient for most purposes, while plane waves and hybrid functionals bring limited improvement compared to the greatly increased computational cost. These results ease the demands for generating DFT data for better interaction with experiments and for data-driven discoveries of 2D metals incorporating machine learning algorithms. Density functional theory ; Materials science ; Basis (linear algebra) ; Benchmark (surveying) ; Plane wave ; Graphene ; Hybrid functional ; Computational chemistry ; Metal ; Computation ; Statistical physics ; Nanotechnology ; Computer science ; Algorithm ; Geometry ; Physics ; Mathematics ; Quantum mechanics ; Metallurgy ; Chemistry Graphene research and applications ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Dipanwita Bhattacharjee | Krishnaraj Kundavu | Deepanshi Saraswat | Parul R. Raghuvanshi | Amrita Bhattacharya
Source: ACS Applied Energy Materials (article, 2022-06-29)
Field: Materials Science — Materials Chemistry

Thorough Descriptor Search to Machine Learn the Lattice Thermal Conductivity of Half-Heusler Compounds Predicting the lattice thermal conductivity (κL) of compounds prior to synthesis is an extremely challenging task because of complexity associated with determining the phonon scattering lifetimes for underlying normal and Umklapp processes. An accurate ab initio prediction is computationally very expensive, and hence one seeks for data-driven alternatives. We perform machine learning (ML) on theoretically computed κL of half-Heusler (HH) compounds. An exhaustive descriptor list comprising elemental and compound descriptors is used to build several ML models. We find that ML models built with compound descriptors can reach high accuracy with a fewer number of descriptors, while a set of a large number of elemental descriptors may be used to tune the performance of the model as accurately. Thereby, using only the elemental descriptors, we build a model with exceptionally high accuracy (with an R2 score of ∼0.98/0.97 for the train/test set) using one of the compressed sensing techniques. This work, while unfolding the complex interplay of the descriptors in different dimensions, reveals the competence of the readily available elemental descriptors in building a robust model for predicting κL. Lattice (music) ; Test set ; Thermal conductivity ; Artificial intelligence ; Computer science ; Ab initio ; Phonon ; Thermal ; Support vector machine ; Quantitative structure–activity relationship ; Set (abstract data type) ; Materials science ; Machine learning ; Physics ; Thermodynamics ; Condensed matter physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Chao Liu | Nathan Wong | Etsuko Watanabe | William Hou | Leonardo Biral | Jonalyn DeCastro | Melod Mehdipour | Kiana Aran | Michael J. Conboy | Irina M. Conboy
Source: Rejuvenation Research (article, 2022-03-24)
Field: Chemistry — Organic Chemistry

Mechanisms and Minimization of False Discovery of Metabolic Bioorthogonal Noncanonical Amino Acid Proteomics Metabolic proteomics has been widely used to characterize dynamic protein networks in many areas of biomedicine, including in the arena of tissue aging and rejuvenation. Bioorthogonal noncanonical amino acid tagging (BONCAT) is based on mutant methionine-tRNA synthases (MetRS) that incorporates metabolic tags, for example, azidonorleucine [ANL], into newly synthesized proteins. BONCAT revolutionizes metabolic proteomics, because mutant MetRS transgene allows one to identify cell type-specific proteomes in mixed biological environments. This is not possible with other methods, such as stable isotope labeling with amino acids in cell culture, isobaric tags for relative and absolute quantitation and tandem mass tags. At the same time, an inherent weakness of BONCAT is that after click chemistry-based enrichment, all identified proteins are assumed to have been metabolically tagged, but there is no confirmation in mass spectrometry data that only tagged proteins are detected. As we show here, such assumption is incorrect and accurate negative controls uncover a surprisingly high degree of false positives in BONCAT proteomics. We show not only how to reveal the false discovery and thus improve the accuracy of the analyses and conclusions but also approaches for avoiding it through minimizing nonspecific detection of biotin, biotin-independent direct detection of metabolic tags, and improvement of signal to noise ratio through machine learning algorithms. Proteomics ; Computational biology ; Proteome ; Bioorthogonal chemistry ; Isobaric labeling ; Biotin ; False positive paradox ; Tandem mass spectrometry ; Chemistry ; Quantitative proteomics ; Biochemistry ; Computer science ; Mass spectrometry ; Biology ; Artificial intelligence ; Combinatorial chemistry ; Click chemistry ; Gene ; Chromatography Click Chemistry and Applications ; Biotin and Related Studies ; RNA and protein synthesis mechanisms
Authors: Saaketh Desai | Rémi Dingreville
Source: Materials & Design (article, 2022-06-06)
Field: Materials Science — Materials Chemistry

Learning time-dependent deposition protocols to design thin films via genetic algorithms Designing next generation thin films tailor-made for specific applications relies on the availability of robust process-structure-property relationships. Traditional structure zone diagrams that relate one or two deposition conditions to microstructures are limited to simple mappings, with machine-learning methods only recently attempting to relate multiple processing parameters to the final microstructure. Despite this progress, process-structure relationships are unknown for deposition conditions that vary during thin-film deposition, limiting the range of achievable microstructures and properties. We combine phase-field simulations with a genetic algorithm to identify and design time-dependent deposition protocols that achieve tailor-made microstructures. We simulate the physical vapor deposition of a binary-alloy thin film by employing a phase-field model, where deposition rates and diffusivities of the deposited species vary in time and are controlled via the genetic algorithm. Our genetic-algorithm-guided protocols achieve targeted microstructures with lateral and vertical concentration modulations, as well as more complex, hierarchical microstructures previously not described in classical structure zone diagrams. By elucidating the process-structure mechanisms during physical vapor deposition and using this knowledge to achieve precise thin-film microstructures, our algorithm provides insights to the thin film, physical vapor deposition, and film functionality communities looking for additional avenues to design novel thin-film microstructures. Microstructure ; Deposition (geology) ; Materials science ; Thin film ; Physical vapor deposition ; Chemical vapor deposition ; Genetic algorithm ; Combustion chemical vapor deposition ; Nanotechnology ; Computer science ; Algorithm ; Carbon film ; Composite material ; Machine learning ; Geology Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: Upala Dasmahapatra | Sreerama Rajasekhar | Grandhe Neelima | Barnali Maiti | K. Ramanathan | Poornima Murali | Balamurali MM | Kaushik Chanda
Source: Chemistry & Biodiversity (article, 2022-12-15)
Field: Chemistry — Organic Chemistry

<i>In Silico</i> Design and Investigation of Novel Thiazetidine Derivatives as Potent Inhibitors of PrpR in <i>Mycobacterium tuberculosis</i> Tuberculosis is one of the most life-threatening acute infectious diseases diagnosed in humans. In the present investigation, a series of 16 new disubstituted 1,3-thiazetidines derivatives is designed, and investigated via various in silico methods for their potential as anti-tubercular agent by evaluating their ability to block the active site of PrpR transcription factor protein of Mycobacterium tuberculosis. The efficacy of the molecules was initially assessed with the help of AutoDock Vina algorithm. Further Glide module is used to redock the previously docked complexes. The binding energies and other physiochemical properties of the designed molecules were evaluated using the Prime-MM/GBSA and the QikProp module, respectively. The results of docking revealed the nature, site of interaction and the binding affinity between the proposed candidates and the active site of PrpR. Further the inhibitory effect of the scaffolds was predicted and evaluated employing a machine learning-based algorithm and was used accordingly. Further, the molecular dynamics simulation studies ascertained the binding characteristics of the unique 13, when analysed across a time frame of 100 ns with GROMACS software. The results show that the proposed 1,3-thiazetidine derivatives such as 10, 11, 13 and 14 could be potent and selective anti-tubercular agents as compared to the standard drug Pyrazinamide. Finally, this study concludes that designed thiazetidines can be employed as anti-tubercular agents. Undeniably, the results may guide the experimental biologists to develop safe and non-toxic drugs against tuberculosis by demanding further in vivo and in vitro analyses. In silico ; Mycobacterium tuberculosis ; Docking (animal) ; AutoDock ; Chemistry ; Computational biology ; Tuberculosis ; In vivo ; Pyrazinamide ; Combinatorial chemistry ; In vitro ; Biochemistry ; Biology ; Medicine ; Genetics ; Gene Synthesis and biological activity ; Tuberculosis Research and Epidemiology ; Cancer therapeutics and mechanisms
Authors: Shashank Ramesh Babu | Robert Musi | Kathrin Thiele | Susanne Michelic
Source: steel research international (article, 2022-09-29)
Field: Materials Science — Metals and Alloys

Classification of Nonmetallic Inclusions in Steel by Data‐Driven Machine Learning Methods Nonmetallic inclusions have strong influence on final steel properties. An important characterization tool to make a comprehensive analysis of nonmetallic inclusions is the scanning electron microscope equipped with energy‐dispersive spectroscopy (SEM‐EDS). A major drawback which prevents its use for online‐steel assessment is the time taken for analysis. Machine learning methods have been previously introduced which circumvents the usage of the EDS for obtaining chemical information of the inclusion by classifying inclusion based on their back scatter electron images. This study introduces a method based on a simpler tabular data input consisting of morphological and mean gray values of inclusions. Naive Bayes and Support Vector Machine classifier models are built using the R statistical programming language. Two steel grades are considered for this study. The prediction results are shown to be satisfactory for both binary (maximum 89%) and 8‐inclusion class (maximum 61%) categorization. The input dataset is further improved by optimizing the image settings to distinguish the different types of nonmetallic inclusions. It is shown that this improvement results in a higher rate of correct predictions for both binary (maximum 98%) and 8‐class categorization (maximum 81%). Support vector machine ; Naive Bayes classifier ; Binary number ; Artificial intelligence ; Binary classification ; Pattern recognition (psychology) ; Scanning electron microscope ; Computer science ; Inclusion (mineral) ; Strip steel ; Materials science ; Machine learning ; Mathematics ; Metallurgy ; Composite material ; Mineralogy ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Corrosion Behavior and Inhibition
Authors: M. Lucrecia Bogado | Roxana Noelia Villafañe | José Leonardo Gómez Chavez | Emilio Angelina | Gladis L. Sosa | Nélida M. Peruchena
Source: Journal of Chemical Information and Modeling (article, 2022-08-31)
Field: Chemistry — Physical and Theoretical Chemistry

Targeting Protein Pockets with Halogen Bonds: The Role of the Halogen Environment Protein pockets that form a halogen bond (X-bond) with a halogenated ligand molecule simultaneously form other (mainly hydrophobic) interactions with the halogen atom that can be considered as its "X-bond environment" (XBenv). Most studies in the field have focused on the X-bond, with the properties of the XBenv usually overlooked. In this work, we derived a protocol that evaluates the XBenv strength as a measure of the propensity of a protein pocket to host an X-bond. The charge density-based topological descriptors in combination with machine learning tools were employed to predict formation and strength of the interactions that conform the XBenv as a function of their geometrical parameters. On the basis of these results, we propose that the XBenv can be used as a footprint to judge the chance of a protein pocket to form an X-bond. Halogen ; Halogen bond ; Chemistry ; Stereochemistry ; Organic chemistry Crystallography and molecular interactions ; Inorganic Fluorides and Related Compounds ; Advanced Chemical Physics Studies
Authors: Ling Ma | Yao Zhang | Yiyang Zhang | Jing Wang | Jianshe Li | Yanming Gao | Xiaomin Wang | Longguo Wu
Source: Agronomy (article, 2022-12-19)
Field: Chemistry — Analytical Chemistry

Rapid Nondestructive Detection of Chlorophyll Content in Muskmelon Leaves under Different Light Quality Treatments In order to select the light quality suitable for plant growth, a quantitative detection model of chlorophyll content in muskmelon leaves was established to monitor plant growth quickly and accurately. In the paper, muskmelon “Boyang 91” was used as the experimental material, and six different light proportion treatments were set up. Through measuring plant height, stem diameter, number of leaves, nodes, and other growth indicators, in addition to leaf chlorophyll content, the response difference of muskmelon to different light qualities was explored in a plant factory. The hyperspectral imaging technology was used to establish the prediction model for the chlorophyll content of muskmelon. The original spectrum was preprocessed and optimized by five pretreatments, and then the characteristic wavelengths were extracted by six methods. Partial least squares regression (PLSR), least squares support vector machine (LSSVM), and convolutional neural network (CNN) were established for optimal feature wavelength. The results showed that the plant height and stem diameter of the T3 treatment were higher than those of other treatments, and their values were 14.48 (cm) and 5.02 (mm), respectively. The chlorophyll content of the T3 treatment was the highest, and its value was 40.16 (mg/g), which was higher than that of other treatments. Through comprehensive analysis, the T3 treatment (light ratio: 6R/1B/2W, light quantum flux: 360 μmol/(m2·s), photoperiod: 12 h) was optimal. Meanwhile, the average spectral reflectance data of 216 leaf samples were extracted, and the S-G preprocessing method was selected to preprocess the original spectral data (Rc = 0.860, RMSEC = 1.806; Rcv = 0.790, RMSECV = 2.161). By comparing and analyzing the correlation coefficients and root mean square errors of six feature wavelength extraction methods, it was concluded that the variable combination population analysis (VCPA) method had the best model effect for feature wavelength extraction (RP = 0.824, RMSEP = 1.973). Ten characteristic wavelengths ( 396, 409, 457, 518, 532, 565, 687, 691, 701, and 705 nm) extracted by the VCPA method were used to establish the chlorophyll content prediction model, and the chlorophyll content prediction model of S-G-VCPA-CNN had the best performance (Rc = 0.9151, RMSEC = 1.445; Rp = 0.811, RMSEP = 2.055). The results of this study provide data support and a theoretical basis for screening the light ratio of other crops, and also present technical support for online monitoring of crop growth in plant factories. Partial least squares regression ; Hyperspectral imaging ; Plant factory ; Chlorophyll ; Mathematics ; Horticulture ; Botany ; Remote sensing ; Artificial intelligence ; Computer science ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement