jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexandre Goyon | Colin Masui | Lauren E. Sirois | Chong Han | Peter Yehl | Francis Gosselin | Kelly Zhang
Source: Analytical Chemistry (article, 2020-11-03)
Field: Chemistry — Spectroscopy

Achiral–Chiral Two-Dimensional Liquid Chromatography Platform to Support Automated High-Throughput Experimentation in the Field of Drug Development Automated high-throughput experimentation (HTE) is a powerful tool for scientists to explore and optimize chemical transformations by simultaneously screening yield, stereoselectivity, and impurity profiles. To analyze the HTE samples, high-throughput analysis (HTA) platforms must be fast, accurate, generic, and specific at the same time. A large amount of high-quality data is critical for the success of machine learning models in the era of big data. Conventional chiral liquid chromatography-mass spectrometry (LC/MS) HTE methods are hampered by compound co-eluting, possible ion suppression, and limited chiral column lifetime in the presence of crude reaction mixtures or complex sample matrices. To overcome these limitations, a generic and fast achiral-chiral heart-cutting two-dimensional (2D)-LC method has been developed to determine both the yield and stereoselectivity of chemical transformations within a 10 min run time. Successful implementation of the 2D-LC HTA platform in a routine drug development environment was achieved for real-world project support, with the analysis so far of over 2000 reaction mixtures prepared in the 96-well plate format. Excellent performance of the method was demonstrated by relative standard deviation (RSD) lower than 0.83% for the <sup>1</sup>D and <sup>2</sup>D retention times, and determination coefficients higher than 0.99. The presented HTA 2D-LC platform has had a significant impact on drug development by analyzing the HTE samples rapidly with unambiguous peak tracking and providing a robust approach for accurately generating a large amount of high-quality data in a short time. Chemistry ; Throughput ; Yield (engineering) ; Chromatography ; Quality by Design ; Process engineering ; Impurity ; Stereoselectivity ; Field (mathematics) ; Combinatorial chemistry ; Computer science ; Catalysis ; Organic chemistry Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Shingo Maruyama | Kana Ouchi | Tomoyuki Koganezawa | Yuji Matsumoto
Source: ACS Combinatorial Science (article, 2020-06-17)
Field: Materials Science — Materials Chemistry

High-Throughput and Autonomous Grazing Incidence X-ray Diffraction Mapping of Organic Combinatorial Thin-Film Library Driven by Machine Learning High-throughput X-ray diffraction (XRD) is one of the most indispensable techniques to accelerate materials research. However, the conventional XRD analysis with a large beam spot size may not best appropriate in a case for characterizing organic materials thin film libraries, in which various films prepared under different process conditions are integrated on a single substrate. Here, we demonstrate that high-resolution grazing incident XRD mapping analysis is useful for this purpose: A 2-dimensional organic combinatorial thin film library with the composition and growth temperature varied along the two orthogonal axes was successfully analyzed by using synchrotron microbeam X-ray. Moreover, we show that the time-consuming mapping process is accelerated with the aid of a machine learning technique termed as Bayesian optimization based on Gaussian process regression. Microbeam ; Throughput ; Synchrotron ; Diffraction ; Substrate (aquarium) ; Thin film ; Bayesian optimization ; Process (computing) ; Optics ; X-ray crystallography ; X-ray ; Chemistry ; Computer science ; Nanotechnology ; Artificial intelligence ; Materials science ; Physics ; Telecommunications Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Catalysis and Oxidation Reactions
Authors: Maamar Laidi | el Abdallah | Cherif Si‐Moussa | Othmane Benkortebi | Mohamed Hentabli | Salah Hanini
Source: Chemical Industry and Chemical Engineering Quarterly (article, 2020-12-07)
Field: Chemistry — Analytical Chemistry

CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA Quantitative structure-property relationship (QSPR) technique provides a suitable tool to predict the critical micelle concentration (CMC) of Gemini surfactants from their structure descriptors. In this study, a comparative work was conducted to model the CMC property of 211 diverse Gemini surfactants based on their structural characteristics using linear and non-linear quantitative structure?property relationship models. Least squares model (OLS) and partial least squares (PLS) against k-nearest neighbours regression model (KNN), artificial neural network (ANN) and support vector regression (SVR) have been developed to model the CMC. Molecular descriptors were calculated and screened to remove unsuitable descriptors and improve the learning. Results indicate that the improved performance of support vector regression when the hyper-parameters are optimized using Dragonfly algorithm (SVR-DA) was highly capable of predicting the pCMC (-log CMC) values with an average absolute relative deviation (AARD) of 0.666 and coefficient of determination (R?) of 0.9971 for the global dataset. Support vector machine ; Quantitative structure–activity relationship ; Linear regression ; Artificial neural network ; Property (philosophy) ; Partial least squares regression ; Biological system ; Regression analysis ; Correlation coefficient ; Computer science ; Mathematics ; Artificial intelligence ; Machine learning Spectroscopy and Chemometric Analyses ; Surfactants and Colloidal Systems ; Water Quality Monitoring and Analysis
Authors: Z. Chen | Samantha Daly
Source: Materials Characterization (article, 2020-09-16)
Field: Materials Science — Biomaterials

Automated identification of deformation twin systems in Mg WE43 from SEM DIC Crystal twinning ; Materials science ; Deformation (meteorology) ; Digital image correlation ; Microscale chemistry ; Deformation mechanism ; Deformation bands ; Identification (biology) ; Microstructure ; Artificial intelligence ; Composite material ; Computer science ; Mathematics Magnesium Alloys: Properties and Applications ; Advanced Welding Techniques Analysis ; MXene and MAX Phase Materials
Authors: Airlie J. McCoy | Duncan H. Stockwell | Massimo Sammito | Robert D. Oeffner | Kaushik Hatti | Tristan I. Croll | Randy J. Read
Source: Acta Crystallographica Section D Structural Biology (article, 2020-12-17)
Field: Materials Science — Materials Chemistry

<i>Phasertng</i>: directed acyclic graphs for crystallographic phasing Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng, which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng, the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng, its advantages are demonstrated in the context of phasertng.xtricorder, a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng, as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies. Phaser ; Computer science ; Directed acyclic graph ; Modular design ; Software ; Theoretical computer science ; Mixed graph ; Data structure ; Graph ; Directed graph ; Algorithm ; Programming language ; Line graph ; Engineering Enzyme Structure and Function ; Protein Structure and Dynamics ; Porphyrin Metabolism and Disorders
Authors: Samad Jafar‐Zanjani | Mohammad Mahdi Salary | Dat Huynh | Ehsan Elhamifar | Hossein Mosallaei
Source: Advanced Theory and Simulations (article, 2020-11-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

TCO‐Based Active Dielectric Metasurfaces Design by Conditional Generative Adversarial Networks Abstract While researchers in the field of active flat optics continue to make groundbreaking progress by seeking novel materials and control systems, the complexity and sensitivity of the nanostructures that they aspire to design are unavoidably increasing. Inverse design of the popular class of transparent conducting oxide (TCO)‐based active metasurfaces is particularly challenging, largely due to the limited choice of the active materials, and sensitive physical mechanisms that give rise to their tunability. In this contribution, a new machine learning method based on a combination of the K‐means clustering algorithm and conditional Wasserstein generative adversarial networks (cWGANs) for broadband multi‐modal inverse design of TCO‐based active metasurfaces is developed. Multi‐objective evolutionary optimization is adopted to efficiently generate a diverse training dataset of high‐performance active metasurfaces. The training dataset includes samples that operate at specific wavelengths throughout the optical telecommunications (telecom) band. K‐means algorithm is then used to extract the clusters (modes) present in the training dataset, and exclusive cWGAN models are fit on each of them. The model is capable of generating designs operating at wavelengths that are not present in the training dataset. It also provides a clear picture of the feasibility and interplay between the design objectives. Computer science ; Broadband ; Inverse ; Generative grammar ; Modal ; Artificial intelligence ; Electronic engineering ; Machine learning ; Telecommunications ; Materials science ; Engineering ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Georges Kfoury | Vanessa El Habbaki | Waddah Malaeb | Sean Weaver | Dmitry Momotenko | Rami Mhanna
Source: Advanced Biosystems (article, 2020-05-27)
Field: Materials Science — Biomaterials

Alginate Sulfate Substrates Control Growth Factor Binding and Growth of Primary Neurons: Toward Engineered 3D Neural Networks Sulfated glycosaminoglycans (sGAGs) are vital molecules of the extracellular matrix (ECM) of the nervous system known to regulate proliferation, migration, and differentiation of neurons mainly through binding relevant growth factors. Alginate sulfate (AlgSulf) mimics sGAGs and binds growth factors such as basic fibroblast growth factor (FGF-2). Here, thin films of biotinylated AlgSulf (b-AlgSulf<sub>n</sub> ) are engineered with sulfation degrees (DS = 0.0 and 2.7) and the effect of polysaccharide concentration on FGF-2 and nerve growth factor (β-NGF) binding and subsequent primary neural viability and neurite outgrowth is assessed. An increase in b-AlgSulf<sub>n</sub> concentration results in higher FGF-2 and β-NGF binding as demonstrated by greater frequency and dissipation shifts measured with quartz crystal microbalance with dissipation monitoring (QCM-D). Primary neurons seeded on the 2D b-AlgSulf<sub>n</sub> films maintain high viability comparable to positive controls grown on poly-d-lysine. Neurons grown in 3D AlgSulf hydrogels (DS = 0.8) exhibit a significantly higher viability, neurite numbers and mean branch length compared to neurons grown in nonsulfated controls. Finally, a first step is made toward constructing 3D neuronal networks by controllably patterning neurons encapsulated in AlgSulf into an alginate carrier. The substrates and neural networks developed in the current study can be used in basic and applied neural applications. Neurite ; Nerve growth factor ; Quartz crystal microbalance ; Sulfation ; Fibroblast growth factor ; Extracellular matrix ; Biophysics ; Growth factor ; Basic fibroblast growth factor ; Chemistry ; Cell biology ; Biochemistry ; Biology ; Receptor ; In vitro ; Organic chemistry Supramolecular Self-Assembly in Materials ; Proteoglycans and glycosaminoglycans research ; Advanced Sensor and Energy Harvesting Materials
Authors: Florent Langenfeld | Yuxu Peng | Yu-Kun Lai | Paul L. Rosin | Tunde Aderinwale | Genki Terashi | Charles Christoffer | Daisuke Kihara | Halim Benhabiles | Karim Hammoudi | Adnane Cabani | Féryal Windal | Mahmoud Melkemi | Andrea Giachetti | Stelios Mylonas | Απόστολος Αξενόπουλος | Petros Daras | Ekpo Otu | Reyer Zwiggelaar | David W. Hunter | Yonghuai Liu | Matthieu Montès
Source: Computers & Graphics (article, 2020-07-31)
Field: Materials Science — Materials Chemistry

SHREC 2020: Multi-domain protein shape retrieval challenge Domain (mathematical analysis) ; Scope (computer science) ; Modular design ; Computer science ; Protein domain ; Function (biology) ; Computational biology ; Protein structure ; Surface protein ; Artificial intelligence ; Biology ; Mathematics ; Biochemistry ; Evolutionary biology ; Gene Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Proteomics Techniques and Applications
Authors: Ranjan Kumar Ghadai | Kanak Kalita
Source: International Journal of Materials Research (formerly Zeitschrift fuer Metallkunde) (article, 2020-05-18)
Field: Materials Science — Materials Chemistry

Accurate estimation of DLC thin film hardness using genetic programming Abstract In the current research, diamond-like carbon thin films are deposited on silicon substrates by plasma-enhanced chemical vapor deposition. The effect of argon-C 2 H 2 flow rate, hydrogen flow rate and deposition temperature on the thin film hardness is investigated. Morphology of the DLC films is investigated by scanning electron microscopy and atomic force microscopy, while the nano-hardness is investigated using nanoindentation. Raman spectroscopy is used for the characterization of the structural properties of the film. A metamodel of the DLC deposition process with argon- C 2 H 2 flow rate, H 2 flow rate and deposition temperature as the regressor variables and coating hardness as the response is built by using a novel symbolic regression approach. A state-of-the-art machine learning approach - genetic programming (GP) - is used for the symbolic regression. By carefully evaluating the performance of the current GP metamodel against a classical RSM (response surface methodology) metamodel, it is seen that the GP significantly outperforms RSM. Nanoindentation ; Materials science ; Scanning electron microscope ; Thin film ; Diamond-like carbon ; Raman spectroscopy ; Chemical vapor deposition ; Genetic programming ; Volumetric flow rate ; Symbolic regression ; Deposition (geology) ; Argon ; Composite material ; Analytical Chemistry (journal) ; Nanotechnology ; Chemical engineering ; Machine learning ; Optics ; Computer science ; Chemistry ; Thermodynamics Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Semiconductor materials and devices
Authors: M. Navaneethakrishnan | S. Vairamuthu | G Parthasarathy | R. Cristin
Source: IET Image Processing (article, 2020-12-22)
Field: Materials Science — Materials Chemistry

Atom search‐Jaya‐based deep recurrent neural network for liver cancer detection Abstract Automatic detection of liver cancer is the fundamental requirement of computer‐aided diagnosis in the clinical sector. The traditional methods used in the liver detection process are not effective in accurately detecting the tumour region using a large‐sized dataset. Moreover, segmenting the large intensity of the tumour region is a complex issue with the existing methods. To overcome these issues, an accurate and efficient liver cancer detection method named atom search‐Jaya‐based deep recurrent neural network is proposed in this research. The proposed method mimics the atomic motion based on the interaction forces and the constraint forces of the hybrid molecules. The optimal solution is revealed through the fitness measure, which in turn accepts the minimal error value as the optimal solution. The weights of the classifier are optimally updated based on the position of the atom with respect to the iterations. The proposed atom search‐Jaya‐based deep recurrent neural network attained significantly better performance in accurately detecting the tumor region using the exploration ability of atoms in the search space. The results obtained by the proposed model in terms of accuracy, specificity, sensitivity, and precision are 93.64%, 96%, 95%, and 94.88%, respectively, while considering the three features using 80% of training data. Artificial neural network ; Computer science ; Atom (system on chip) ; Cancer ; Liver cancer ; Artificial intelligence ; Pattern recognition (psychology) ; Medicine ; Embedded system ; Internal medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Rose Ellen N. Macabiog | Nemilyn A. Fadchar | Jennifer C. Dela Cruz
Source:  (article, 2020-02-01)
Field: Chemistry — Analytical Chemistry

Soil NPK Levels Characterization Using Near Infrared and Artificial Neural Network Soil, being the medium for plant growth serves as the primary nutrient source. Nitrogen (N), Phosphorus (P), and Potassium (K) are the vital nutrients plants need in the largest amounts. There should be sufficient quantities of these nutrients in the soil to ensure optimum plant growth resulting in high quality crops and improved crop yields. Farm managers then have a critical responsibility of assessing the NPK levels in soil. Soil chemical analysis was the reference method used to define soil NPK levels. Near Infrared (NIR) Spectroscopy was adopted as a non-destructive, fast, and environmentally friendly method for determining characteristic absorbance spectra of soil. The resulting variations in the measured NIR absorbance wavelengths ranging from 1240 nm to 1480 nm were evaluated and used to characterize the NPK nutrient levels of the various soil samples. Furthermore, the dataset was modelled and analysed using Artificial Neural Network (ANN) to determine the relationship of the NIR absorbance data to the soil NPK nutrient levels. Based on the regression analysis, the model's training R is 0.998, the testing R is 0.996, the test R is 0.996 and the overall R is 0.998. This study proves that NPK soil nutrient levels can be characterized in terms of NIR absorbance spectra. Absorbance ; Nutrient ; Soil test ; Near-infrared spectroscopy ; Environmental science ; Phosphorus ; Soil nutrients ; Nitrogen ; Crop ; Soil science ; Agronomy ; Chemistry ; Soil water ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: Estefanía Garijo del Río | Sami Kaappa | José Antonio Garrido Torres | Thomas Bligaard | Karsten W. Jacobsen
Source: Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine learning with bond information for local structure optimizations in surface science Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. We introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations. Bond ; Surface (topology) ; Computer science ; Artificial intelligence ; Business ; Mathematics ; Geometry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Mehdi Ataei Azimi | Mazyar Ahmadi Golsefidi | Ali Varasteh Moradi | Mehdi Ebadi | Ramin Zafar Mehrabian
Source: Journal of Analytical Methods in Chemistry (article, 2020-03-01)
Field: Chemistry — Analytical Chemistry

A Novel Method for Extraction of Galegine by Molecularly Imprinted Polymer (MIP) Technique Reinforced with Graphene Oxide and Its Evaluation Using Polarography <i>Galega officinalis</i> products have been used for the control of diabetes (type 2) across the world. Experimental and clinical evaluations of galegine substance produced by a medicinal plant (<i>Galega officinalis</i>) provided the pharmacological and chemical basis for metformin discovery which was confirmed for diabetes therapy. In this paper, the molecularly imprinted polymer (MIP) was synthesized for galegine, using galegine as a template molecule, methacrylic acid (MAA) as a functional monomer, ethylene glycol dimethacrylate (EGDMA) as a cross-linker, azobisisobutyronitrile (AIBN) as a reaction initiator, and acetonitrile as a solvent. The assisted functional groups, morphology, topographic image of surface, and crystalline structure of synthesized MIP were characterized by FT-IR spectroscopy, field emission scanning electron microscopy (FE-SEM), atomic force microscopy (AFM) images, and XRD diffraction pattern techniques, respectively. Also, the performance of the mentioned electrode was quantified and qualified by the differential pulse voltammetry technique (DPV). The galegine amount was determined with the polarographic technique. In this research, the galegine extraction conditions were optimized and graphene nanoparticles were used to increase the adsorption. In addition, different parameters affecting extraction were investigated such as MIP adsorbent amount, pH of solution, effect of the surfactant, and ionic compound to achieve high recovery percent. The recovery percent, limit of detection (LOD), limit of quantification (LOQ), and relative standard deviation (RSD %) were 4.101 <i>μ</i>g·mL<sup>-1</sup>, 12.427 <i>μ</i>g·mL<sup>-1</sup>, and 1.199% (<i>n</i> = 3), respectively. The results show that the prepared MIP can be used as an effective and inexpensive adsorbent for preconcentration and galegine extraction from a natural sample. It is noteworthy that this developed method was used successfully to determine galegine extracted from <i>Galega officinalis L</i>. Molecularly imprinted polymer ; Ethylene glycol dimethacrylate ; Detection limit ; Differential pulse voltammetry ; Methacrylic acid ; Materials science ; Adsorption ; Graphene ; Acetonitrile ; Extraction (chemistry) ; Cyclic voltammetry ; Nuclear chemistry ; Chemistry ; Polymer ; Chromatography ; Monomer ; Electrochemistry ; Electrode ; Nanotechnology ; Organic chemistry ; Selectivity Analytical chemistry methods development ; Electrochemical sensors and biosensors ; Analytical Chemistry and Chromatography