jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Issa Amini | Kaushik Pal | Sharmin Esmaeilpoor | Abdelkarim Aydi
Source: Advanced Journal of Chemistry-Section A (article, 2018-12-30)
Field: Chemistry — Spectroscopy

Prediction of two-dimensional gas chromatography time-of-flight mass spectrometry retention times of 160 pesticides and 25 environmental organic pollutants in grape by multivariate chemometrics methods A quantitative structure–retention relation (QSRR) study was conducted on the retention times of 160 pesticides and 25 environmental organic pollutants in wine and grape. The genetic algorithm was used as descriptor selection and model development method. Modeling of the relationship between selected molecular descriptors and retention time was achieved by linear (partial least square; PLS) and nonlinear (kernel PLS: KPLS and Levenberg-Marquardt artificial neural network; L-M ANN) methods. The QSRR models were validated by cross-validation as well as application of the models to predict the retention of external set compounds, which did not have contribution in model development steps. Linear and nonlinear methods resulted in accurate prediction whereas more accurate results were obtained by L-M ANN model. The best model obtained from L-M ANN showed a good R2 value (determination coefficient between observed and predicted values) for all compounds, which was superior to those of other statistical models. This is the first research on the QSRR of the compounds in wine and grape against the retention time using the GA-KPLS and L-M ANN. Chemometrics ; Coefficient of determination ; Wine ; Molecular descriptor ; Chemistry ; Multivariate statistics ; Chromatography ; Pollutant ; Partial least squares regression ; Biological system ; Environmental chemistry ; Mathematics ; Quantitative structure–activity relationship ; Statistics Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety ; Advanced Chemical Sensor Technologies
Authors: Liam Wilbraham | Reiner Sebastian Sprick | Kim E. Jelfs | Martijn A. Zwijnenburg
Source: ChemRxiv (preprint, 2018-12-11)
Field: Materials Science — Materials Chemistry

Mapping Binary Copolymer Property Space with Neural Networks The extremely large number of unique polymer compositions that can be achieved through copolymerisation makes it an attractive strategy for tuning their optoelectronic properties. However, this same attribute also makes it challenging to explore the resulting property space and understand the range of properties that can be realised. In an effort to enable the rapid exploration of this space in the case of binary copolymers, we train a neural network using a tiered data generation strategy to accurately predict the optical and electronic properties of 350,000 binary copolymers that are, in principle, synthesizable from their dihalogen monomers via Yamamoto, or Suzuki-Miyaura and Stille coupling after one-step functionalisation. By extracting general features of this property space that would otherwise be obscured in smaller datasets, we identify simple models that effectively relate the properties of these copolymers to the homopolymers of their constituent monomers, and challenge common ideas behind copolymer design. We find that binary copolymerisation does not appear to allow access to regions of the optoelectronic property space that are not already sampled by the homopolymers, although conceptually allows for more fine-grained property control. Using the large volume of data available, we test the hypothesis that copolymerisation of ‘donor’ and ‘acceptor’ monomers can result in copolymers with a lower optical gap than their related homopolymers. Overall, despite the prevalence of this concept in the literature, we observe that this phenomenon is relatively rare, and propose conditions that greatly enhance the likelihood of its experimental realisation. Finally, through a ‘topographical’ analysis of the co-polymer property space, we show how this large volume of data can be used to identify dominant monomers in specific regions of property space that may be amenable to a variety of applications, such as organic photovoltaics, light emitting diodes, and thermoelectrics. &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt; &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Copolymer ; Binary number ; Monomer ; Materials science ; Property (philosophy) ; Space (punctuation) ; Computer science ; Stille reaction ; Polymer ; Mathematics Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Organic Electronics and Photovoltaics
Authors: Paulina Kaźmierska | Anna Kobrzycka | Renata Bocian | Tomasz Kowalczyk | Anita Krokosz | Jacek Grębowski
Source: Experimental and Molecular Pathology (article, 2018-06-23)
Field: Chemistry — Organic Chemistry

Fullerenol C 60 (OH) 36 at relatively high concentrations impairs hippocampal theta oscillations (in vivo and in vitro) and triggers epilepsy (in vitro) – A dose response study In vivo ; In vitro ; Hippocampal formation ; Epilepsy ; Pharmacology ; Chemistry ; Neuroscience ; Hippocampus ; Psychology ; Medicine ; Biology ; Biochemistry Fullerene Chemistry and Applications ; Anesthesia and Neurotoxicity Research ; ATP Synthase and ATPases Research
Authors: John Bradshaw | Matt J. Kusner | Brooks Paige | Marwin Segler | José Miguel Hernández-Lobato
Source: PolyPublie (École Polytechnique de Montréal) (preprint, 2018-05-23)
Field: Materials Science — Materials Chemistry

A Generative Model For Electron Paths Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using `arrow-pushing' diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction data. Instead of predicting product molecules directly from reactant molecules in one shot, learning a model of electron movement has the benefits of (a) being easy for chemists to interpret, (b) incorporating constraints of chemistry, such as balanced atom counts before and after the reaction, and (c) naturally encoding the sparsity of chemical reactions, which usually involve changes in only a small number of atoms in the reactants.We design a method to extract approximate reaction paths from any dataset of atom-mapped reaction SMILES strings. Our model achieves excellent performance on an important subset of the USPTO reaction dataset, comparing favorably to the strongest baselines. Furthermore, we show that our model recovers a basic knowledge of chemistry without being explicitly trained to do so. Arrow ; Molecule ; Path (computing) ; Chemical reaction ; Atom (system on chip) ; Sequence (biology) ; Computer science ; Generative model ; Redistribution (election) ; Electron ; Chemistry ; Product (mathematics) ; Biological system ; Algorithm ; Generative grammar ; Computational chemistry ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Sepehr Sadighi | Seyed Reza Seif Mohaddecy | Ali Abbasi
Source: International Journal of Technology (article, 2018-01-26)
Field: Chemistry — Analytical Chemistry

Modeling and Optimizing a Vacuum Gas Oil Hydrocracking Plant using an Artificial Neural Network In this research, based on actual data gathered from an industrial scale vacuum gas oil (VGO) hydrocracker and artificial neural network (ANN) method, a model is proposed to simulate yields of products including light gases, liquefied petroleum gas ( Artificial neural network ; Petroleum engineering ; Process engineering ; Cracking ; Fossil fuel ; Environmental science ; Materials science ; Computer science ; Waste management ; Engineering ; Artificial intelligence ; Composite material Petroleum Processing and Analysis ; Fluid Dynamics and Mixing ; Reservoir Engineering and Simulation Methods
Authors: Evan Miller | Matthew L. Jones | Michael M. Henry | Paul Chery | Kyle D. Miller | Eric Jankowski
Source: Preprints.org (preprint, 2018-11-05)
Field: Materials Science — Materials Chemistry

Optimization and Validation of Efficient Models for Predicting Polythiophene Self-Assembly We develop an optimized force-field for poly(3-hexylthiophene) (P3HT) and demonstrate its utility for predicting thermodynamic self-assembly. In particular, we consider short oligomer chains, model electrostatics and solvent implicitly, and coarsely model solvent evaporation. We quantify the performance of our model to determine what the optimal system sizes are for exploring self-assembly at combinations of state variables. We perform molecular dynamics simulations to predict the self-assembly of P3HT at ~350 combinations of temperature and solvent quality. Our structural calculations predict that the highest degrees of order are obtained with good solvents just below the melting temperature. We find our model produces the most accurate structural predictions to date, as measured by agreement with grazing incident X-ray scattering experiments. Polythiophene ; Molecular dynamics ; Solvent ; Electrostatics ; Self-assembly ; Oligomer ; Materials science ; Evaporation ; Force field (fiction) ; Thermodynamics ; Statistical physics ; Chemical physics ; Biological system ; Chemistry ; Polymer ; Nanotechnology ; Computer science ; Computational chemistry ; Physics ; Physical chemistry ; Conductive polymer ; Organic chemistry ; Polymer chemistry Material Dynamics and Properties ; Spectroscopy and Quantum Chemical Studies ; Machine Learning in Materials Science
Authors: Alejandro Caicedo‐Ramirez
Source: CU Scholar (University of Colorado Boulder) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Antimicrobial Aggregates for the In-Situ Control of Microbially Induced Concrete Corrosion Concrete is the most common material used to transport storm water and sewage in the world. It is used for pipes, culverts, tunnels and variety of other subterranean appurtenances. Municipalities, water utilities, and many agencies recognize that concrete corrosion is emerging as one of the most serious problems plaguing this critical infrastructure. The dominant form of concrete deterioration in wastewater conveyance systems is microbially mediated (Microbial Induced Concrete Corrosion: MICC). This type of corrosion occurs as a result of ubiquitous microbiological sulfur cycling within sewers: Below the waterline, sulfate present in wastewater is reduced to sulfide (H2S) under anoxic conditions; this partitions into the headspace of pipes and other wastewater structures as H2S gas, which serves as a substrate for biofilms of acidogenic sulfur-oxidizing bacteria (SOBs) above the waterline. These biofilms produce sulfuric acid, which chemically dissolve the cement binder and compromise the concrete structure. While current mitigation technologies focus their attention on developing acid resistant materials, little research has been done on limiting acidophile development in these environments. In response to the current research gaps in this arena, the central aim of my work was to study the effects of substituting metal-impregnated sorbents for a fraction of the fine aggregates traditionally used in cements â for the express purpose of inhibiting the bacterial communities responsible for the corrosion in sewer systems. Laboratory investigations evaluated the inhibition potential of selected heavy metals against SOB communities, and resolved minimum inhibitory concentrations, individually and in combinations. Parallel studies characterized different sorbents along with their metal desorption profiles in response to biogenic acid. Field studies in the Denver Metropolitan wastewater collection system, then assessed anti-corrosion performance of cement mortar formulations with different loads of metal-impregnated sorbents. Corrosion ; Antimicrobial ; Biocide ; In situ ; Chemistry ; Materials science ; Metallurgy ; Organic chemistry Corrosion Behavior and Inhibition ; Odor and Emission Control Technologies ; Antimicrobial agents and applications
Authors: O. Abuomar | Sasan Nouranian | R.L. King | Thomas E. Lacy
Source: Computational Materials Science (article, 2018-11-15)
Field: Materials Science — Polymers and Plastics

Application of materials informatics to vapor-grown carbon nanofiber/vinyl ester nanocomposites through self-organizing maps and clustering techniques Materials science ; Nanocomposite ; Composite material ; Dynamic mechanical analysis ; Flexural strength ; Vinyl ester ; Carbon nanofiber ; Ultimate tensile strength ; Dynamic modulus ; Curing (chemistry) ; Flexural modulus ; Carbon nanotube ; Polymer Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Polymer Nanocomposites and Properties
Authors: Ganna Shchygol | Alexei Yakovlev | Tomáš Trnka | Adri C. T. van Duin | Toon Verstraelen
Source: ChemRxiv (preprint, 2018-06-19)
Field: Materials Science — Materials Chemistry

Systematic Comparison of Monte Carlo Annealing and Covariance Matrix Adaptation for the Optimization of ReaxFF Parameters ReaxFF is a computationally efficient force field to simulate complex reactive dynamics in extended molecular models with diverse chemistries, if reliable force-field parameters are available for the chemistry of interest. If not, they must be calibrated by minimizing the error ReaxFF makes on a relevant training set. Because this optimization is far from trivial, many methods, in particular genetic algorithms (GAs), have been developed to search for the global optimum in parameter space. Recently, two alternative parameter calibration techniques were proposed, i.e.\ Monte-Carlo Force Field optimizer (MCFF) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), which have the potential to find good parameters at a relatively low computational cost. In this work, these two methods are tested, as implemented in ADF2018, using three ReaxFF training sets, which have previously been used to benchmark GAs. Even though MCFF and CMA-ES should not be considered as exhaustive global optimizers, they can find parameters that are comparable in quality to those obtained with GAs. We observe that CMA-ES leads to slightly better results and is less sensitive to the initial guess of the parameters. Concrete recipes are provided for obtaining similar results with new training sets.Besides optimization recipes, a successful ReaxFF parameterization requires the design of a good training set. At every trial set of parameters, ReaxFF is used to optimize molecular geometries in the training set. When the optimization of some geometries fails easily, it becomes increasingly difficult to find the optimal parameters. We have addressed this issue by fixing several bugs in the ReaxFF forces and by improving the robustness of the geometry optimization. These improvements cannot eliminate all geometry convergence issues and we recommend to avoid very flexible geometries in the training set.Both MCFF and CMA-ES are still liable to converge to sub- or near-optimal parameters, which we detected by repeating the calibration with different random seeds. The existence of distinct near-optimal parameter vectors is a general pattern throughout our study and provides opportunities to improve the training set or to detect overfitting artifacts. ReaxFF ; Simulated annealing ; Force field (fiction) ; Computer science ; Monte Carlo method ; Molecular dynamics ; Covariance matrix ; Benchmark (surveying) ; Mathematical optimization ; Algorithm ; Statistical physics ; Mathematics ; Chemistry ; Computational chemistry ; Physics ; Artificial intelligence ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Bo Sun | Baopeng Liao | Mengmeng Li | Yi Ren | Qiang Feng | Dezhen Yang
Source: Materials (article, 2018-03-27)
Field: Materials Science — Materials Chemistry

A Time-Variant Reliability Model for Copper Bending Pipe under Seawater-Active Corrosion Based on the Stochastic Degradation Process In the degradation process, the randomness and multiplicity of variables are difficult to describe by mathematical models. However, they are common in engineering and cannot be neglected, so it is necessary to study this issue in depth. In this paper, the copper bending pipe in seawater piping systems is taken as the analysis object, and the time-variant reliability is calculated by solving the interference of limit strength and maximum stress. We did degradation experiments and tensile experiments on copper material, and obtained the limit strength at each time. In addition, degradation experiments on copper bending pipe were done and the thickness at each time has been obtained, then the response of maximum stress was calculated by simulation. Further, with the help of one kind of Monte Carlo method we propose, the time-variant reliability of copper bending pipe was calculated based on the stochastic degradation process and interference theory. Compared with traditional methods and verified by maintenance records, the results show that the time-variant reliability model based on the stochastic degradation process proposed in this paper has better applicability in the reliability analysis, and it can be more convenient and accurate to predict the replacement cycle of copper bending pipe under seawater-active corrosion. Piping ; Randomness ; Reliability (semiconductor) ; Structural engineering ; Copper ; Bending ; Materials science ; Degradation (telecommunications) ; Monte Carlo method ; Gamma process ; Computer science ; Metallurgy ; Engineering ; Mechanical engineering ; Mathematics ; Physics ; Thermodynamics ; Statistics ; Electronic engineering Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Concrete Corrosion and Durability
Authors: Ningren Han | Rajeev J. Ram
Source: arXiv (Cornell University) (preprint, 2018-05-20)
Field: Chemistry — Analytical Chemistry

Bayesian Modeling and Computation for Analyte Quantification in Complex Mixtures Using Raman Spectroscopy In this work, we propose a two-stage algorithm based on Bayesian modeling and computation aiming at quantifying analyte concentrations or quantities in complex mixtures with Raman spectroscopy. A hierarchical Bayesian model is built for spectral signal analysis, and reversible-jump Markov chain Monte Carlo (RJMCMC) computation is carried out for model selection and spectral variable estimation. Processing is done in two stages. In the first stage, the peak representations for a target analyte spectrum are learned. In the second, the peak variables learned from the first stage are used to estimate the concentration or quantity of the target analyte in a mixture. Numerical experiments validated its quantification performance over a wide range of simulation conditions and established its advantages for analyte quantification tasks under the small training sample size regime over conventional multivariate regression algorithms. We also used our algorithm to analyze experimental spontaneous Raman spectroscopy data collected for glucose concentration estimation in biopharmaceutical process monitoring applications. Our work shows that this algorithm can be a promising complementary tool alongside conventional multivariate regression algorithms in Raman spectroscopy-based mixture quantification studies, especially when collecting a large training dataset with high quality is challenging or resource-intensive. Analyte ; Chemometrics ; Computation ; Computer science ; Bayesian probability ; Algorithm ; Multivariate statistics ; Artificial intelligence ; Biological system ; Pattern recognition (psychology) ; Machine learning ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Abdalla A. Elbashir | Shahd Moutasim Merghani
Source: Asian Journal of Pharmaceutical Research and Development (article, 2018-12-06)
Field: Chemistry — Analytical Chemistry

Spectrophotometric determination of ranitidine hydrochloride (RNH) in pharmaceutical formulation using 9-fluorenylmethyl chloroformate (FMOC-Cl) A new, simple and sensitive spectrophotometric method is developed for the determination of ranitidine hydrochloride (RNH). The proposed method is based upon reaction of RNH with 9-fluorenylmethyl chloroformate (FMOC-Cl) in borate buffer of pH 8.0 producing an absorption maximum at 255 nm. All parameters required for the reaction conditions are investigated. Linearity is verified with a range of 2-16 μg/mL and is described by the regression equation y = 61129 x + 0.0354 with a correlation coefficient of 0.9998 (n = 7). The limit of detection (LOD) and the limit of quantification (LOQ) were calculated as per ICH guidelines and were found to be 0.2219 and 0.6724 μg/mL, respectively. The method was successfully applied for the determination of RNH in pharmaceutical formulation. Therefore, the method can be used for routine analysis of RNH in quality control laboratories. Chloroformate ; Detection limit ; Chemistry ; Chromatography ; Derivatization ; Ranitidine ; Hydrochloride ; High-performance liquid chromatography ; Organic chemistry ; Pharmacology Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Antibiotics Pharmacokinetics and Efficacy
Authors: Pavel N. Zolotarev | Roman A. Eremin
Source: EPJ Web of Conferences (article, 2018-01-01)
Field: Materials Science — Polymers and Plastics

Topology-based description of the NCA cathode configurational space and an approach of its effective reduction Modification of existing solid electrolyte and cathode materialsis a topic of interest for theoreticians and experimentalists. In particular, itrequires elucidation of the influence of dopants on the characteristics of thestudying materials. For the reason of high complexity of theconfigurational space of doped/deintercalated systems, application of thecomputer modeling approaches is hindered, despite significant advances ofcomputational facilities in last decades. In this study, we propose a scheme,which allows to reduce a set of structures of a modeled configurationalspace for the subsequent study by means of the time-consuming quantumchemistry methods. Application of the proposed approach is exemplifiedthrough the study of the configurational space of the commercialLiNi 0.8 Co 0.15 A l0.05 O 2 (NCA) cathode material approximant. Cathode ; Reduction (mathematics) ; Space (punctuation) ; Topology (electrical circuits) ; Set (abstract data type) ; Computer science ; Electrolyte ; Materials science ; Nanotechnology ; Chemistry ; Mathematics ; Engineering ; Physical chemistry ; Electrical engineering ; Electrode Transition Metal Oxide Nanomaterials ; X-ray Diffraction in Crystallography ; Polyoxometalates: Synthesis and Applications