jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wilem Musu | Akihiro Tsuchida | Hirofumi Kawazumi | Nobuto Oka
Source:  (article, 2019-08-01)
Field: Chemistry — Analytical Chemistry

Application of PCA-SVM and ANN Techniques for Plastic Identification by Raman Spectroscopy The mechanical recycling of plastics is one of the most efficient approaches for reducing carbon dioxide emissions. Purification of the plastics from shredded waste materials requires versatile techniques, such as optical identification by Raman spectroscopy. The identification procedure demands the spectroscopy expertise to assign molecular structures from spectral peaks. In this study, we demonstrate applications to classify plastics using machine learning techniques under practical recycling industry conditions. Combining the techniques of principal component analysis (PCA) and support vector machine provides an accurate and robust classification of the valuable plastics of polypropylene, polystyrene, and acrylonitrile-butadiene-styrene copolymer. The identification accuracy remained above 95%, even with noise 3 times larger than the original intensity. For noise 10 times larger, the accuracy was more than 70%. Fast and simple computation is also useful for industrial applications, resulting from dimension reduction of the spectroscopic data by PCA Furthermore, artificial neural networks showed high accuracy, close to 100%, after a few epoch calculations. Principal component analysis ; Raman spectroscopy ; Polypropylene ; Polystyrene ; Support vector machine ; Artificial neural network ; Materials science ; Spectroscopy ; Identification (biology) ; Artificial intelligence ; Computer science ; Dimensionality reduction ; Noise (video) ; Acrylonitrile butadiene styrene ; Pattern recognition (psychology) ; Process engineering ; Biological system ; Engineering ; Composite material ; Optics ; Polymer ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Justin S. Smith | R.I. Zubatyuk | Benjamin Nebgen | Nicholas Lubbers | Kipton Barros | Adrián E. Roitberg | Olexandr Isayev | Sergei Tretiak
Source: ChemRxiv (preprint, 2019-10-29)
Field: Materials Science — Materials Chemistry

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based eneral-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data set diversity, we visualize them with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5M density functional theory calculations, while the ANI-1ccx data set contains 500k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM properties from density functional theory and coupled cluster are provided: energies, atomic forces, multipole moments, atomic charges, and more. We provide this data to the community to aid research and development of ML models for chemistry. Extrapolation ; Density functional theory ; Coupled cluster ; Multipole expansion ; Data set ; Computer science ; Quantum chemistry ; Time-dependent density functional theory ; Chemistry ; Dimensionality reduction ; Statistical physics ; Molecule ; Physics ; Computational chemistry ; Quantum mechanics ; Artificial intelligence ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: George Alexandru Nemneş | T. L. Mitran | Andrei Manolescu
Source: Journal of Nanomaterials (article, 2019-05-16)
Field: Materials Science — Materials Chemistry

Gap Prediction in Hybrid Graphene-Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks The electronic properties of graphene nanoflakes (GNFs) with embedded hexagonal boron nitride (hBN) domains are investigated by combined ab initio density functional theory calculations and machine-learning techniques. The energy gaps of the quasi-0D graphene-based systems, defined as the differences between LUMO and HOMO energies, depend not only on the sizes of the hBN domains relative to the size of the pristine graphene nanoflake but also on the position of the hBN domain. The range of the energy gaps for different configurations increases as the hBN domains get larger. We develop two artificial neural network (ANN) models able to reproduce the gap energies with high accuracies and investigate the tunability of the energy gap, by considering a set of GNFs with embedded rectangular hBN domains. In one ANN model, the input is in one-to-one correspondence with the atoms in the GNF, while in the second model the inputs account for basic structures in the GNF, allowing potential use in upscaled systems. We perform a statistical analysis over different configurations of ANNs to optimize the network structure. The trained ANNs provide a correlation between the atomic system configuration and the magnitude of the energy gaps, which may be regarded as an efficient tool for optimizing the design of nanostructured graphene-based materials for specific electronic properties. Graphene ; Materials science ; Artificial neural network ; Hexagonal boron nitride ; Band gap ; Ab initio ; Hybrid functional ; Range (aeronautics) ; Nanotechnology ; Energy (signal processing) ; Boron nitride ; Density functional theory ; Position (finance) ; Topology (electrical circuits) ; Machine learning ; Optoelectronics ; Computer science ; Physics ; Quantum mechanics ; Mathematics Graphene research and applications ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Suryanaman Chaube | Prerna Khullar | Sriram Goverapet Srinivasan | Beena Rai
Source: Journal of Electronic Materials (article, 2019-11-12)
Field: Materials Science — Materials Chemistry

A Statistical Learning Framework for Accelerated Bandgap Prediction of Inorganic Compounds Support vector machine ; Band gap ; Curse of dimensionality ; Artificial intelligence ; Computer science ; Machine learning ; Perovskite (structure) ; k-nearest neighbors algorithm ; Regression ; Principal component analysis ; Artificial neural network ; Materials science ; Chemistry ; Mathematics ; Statistics ; Crystallography ; Optoelectronics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Kai-Qi Zhang | Haiqing Yin | Xue Jiang | Xiuqin Liu | Fei He | Zhenghua Deng | Dil Faraz Khan | Qingjun Zheng | Xuanhui Qu
Source: International Journal of Minerals Metallurgy and Materials (article, 2019-02-01)
Field: Chemistry — Spectroscopy

A novel approach to predict green density by high-velocity compaction based on the materials informatics method Compaction ; Materials science ; Artificial neural network ; Multilayer perceptron ; Biological system ; Correlation coefficient ; Computer science ; Soil science ; Artificial intelligence ; Machine learning ; Composite material ; Environmental science Adsorption, diffusion, and thermodynamic properties of materials ; Engineering Technology and Methodologies ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Supriya Sahu | Surajit Kumar Ghosh | Jun Moni Kalita | Murali C Ginjupalli | Kranthi Raj K
Source: European Journal of Pharmaceutical Sciences (article, 2019-12-25)
Field: Chemistry — Organic Chemistry

Discovery of potential 1,3,5-Triazine compounds against strains of Plasmodium falciparum using supervised machine learning models Quantitative structure–activity relationship ; Plasmodium falciparum ; Antimalarial Agent ; Malaria ; Partial least squares regression ; Computational biology ; Drug discovery ; Stereochemistry ; Combinatorial chemistry ; Chemistry ; Machine learning ; Pharmacology ; Biology ; Computer science ; Biochemistry Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity ; Click Chemistry and Applications
Authors: Ruud van Deursen | Peter Ertl | Igor V. Tetko | Guillaume Godin
Source: ChemRxiv (preprint, 2019-09-12)
Field: Materials Science — Materials Chemistry

GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks Recurrent neural networks have been widely used to generate millions of de novo molecules in a known chemical space. These deep generative models are typically setup with LSTM or GRU units and trained with canonical SMILES. In this study, we introduce a new robust architecture, Generative Examination Network GEN, based on bidirectional RNNs with concatenated sub-models to learn and generate molecular SMILES within a trained target space. GENs autonomously learn the target space in a few epochs while being subjected to an independent online examination to measure the quality of the generated set. Here we have used online statistical quality control (SQC) on the percentage of valid molecular SMILES as examination measure to select the earliest available stable model weights. Very high levels of valid SMILES (95-98%) can be generated using multiple parallel encoding layers in combination with SMILES augmentation using unrestricted SMILES randomization. Our architecture combines an excellent novelty rate (85-90%) while generating SMILES with strong conservation of the property space (95-99%). Our flexible examination mechanism is open to other quality criteria. Computer science ; Artificial intelligence ; Generative grammar ; Novelty ; Generative model ; Chemical space ; Recurrent neural network ; Measure (data warehouse) ; Set (abstract data type) ; Machine learning ; Space (punctuation) ; Natural language processing ; Artificial neural network ; Data mining ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research
Authors: Pascal Friederich | Artem Fediai | Simon Kaiser | Manuel Konrad | Nicole Jung | Wolfgang Wenzel
Source: Advanced Materials (article, 2019-06-01)
Field: Materials Science — Polymers and Plastics

Organic Semiconductors: Toward Design of Novel Materials for Organic Electronics (Adv. Mater. 26/2019) In article number 1808256, Wolfgang Wenzel and co-workers discuss the state of the art of predictive simulation methods, including machine learning, to complement experimental research in the identification of novel materials for organic electronics. Their potential is illustrated by highlighting some prominent recent applications. Materials science ; Organic electronics ; Organic semiconductor ; Electronics ; Nanotechnology ; Complement (music) ; Semiconductor ; Identification (biology) ; Engineering physics ; Optoelectronics ; Engineering ; Transistor ; Electrical engineering Conducting polymers and applications ; Machine Learning in Materials Science
Authors: Hagit Sason | Yosi Shamay
Source: Israel Journal of Chemistry (article, 2019-09-25)
Field: Materials Science — Biomaterials

Nanoinformatics in Drug Delivery Abstract The use of nanomedicine for targeted drug delivery, though well established, is still a growing and developing field of research with potential benefits to many biomedical problems. There is a plethora of nano‐carriers with myriads of designs of shapes, sizes and composition that involves complex, trial and error based preparation protocols. The digital age brought an information revolution with automated data analysis, machine learning and data mining applied to almost every field of research including drug delivery. Indeed, nanomedicine has benefitted from the use of data science and information science to optimize, standardize, and understand the synthesis, characterization, and biological effects of nanomaterials. This short review will describe several concepts and a few examples of nanoinformatics, including Nano‐Quantitative Structure‐Activity Relationship (Nano‐QSAR), the use of computational methods for predicting different properties of nanomedicine in drug delivery and propose an outlook for the future. Nanomedicine ; Drug delivery ; Field (mathematics) ; Nanotechnology ; Data science ; Quantitative structure–activity relationship ; Chemistry ; Risk analysis (engineering) ; Computer science ; Management science ; Engineering ; Machine learning ; Medicine ; Nanoparticle Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoparticles: synthesis and applications
Authors: Mohammad Farajvand | Vahid Kiarostami | Mehran Davallo | A.M. Ghaedi | Farnoosh Fatahi
Source: Journal of Food Science and Technology (article, 2019-07-08)
Field: Chemistry — Analytical Chemistry

Rapid extraction of copper ions in water, tea, milk and apple juice by solvent-terminated dispersive liquid–liquid microextraction using p-sulfonatocalix (4) arene: optimization by artificial neural networks coupled bat inspired algorithm and response surface methodology Detection limit ; Extraction (chemistry) ; Disperser ; Reagent ; Solvent ; Central composite design ; Chromatography ; Chemistry ; Linear range ; Response surface methodology ; Analytical Chemistry (journal) ; Materials science ; Organic chemistry Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies
Authors: Matthias Filez | E. Redekop | Jolien Dendooven | Ranjith K. Ramachandran | Eduardo Solano | Unni Olsbye | Bert M. Weckhuysen | Vladimir Galvita | Hilde Poelman | Christophe Detavernier | Guy Marin
Source: Angewandte Chemie (article, 2019-04-01)
Field: Materials Science — Materials Chemistry

Formation and Functioning of Bimetallic Nanocatalysts: The Power of X‐ray Probes Abstract Bimetallic nanocatalysts are key enablers of current chemical technologies, including car exhaust converters and fuel cells, and play a crucial role in industry to promote a wide range of chemical reactions. However, owing to significant characterization challenges, insights in the dynamic phenomena that shape and change the working state of the catalyst await further refinement. Herein, we discuss the atomic‐scale processes leading to mono‐ and bimetallic nanoparticle formation and highlight the dynamics and kinetics of lifetime changes in bimetallic catalysts with showcase examples for Pt‐based systems. We discuss how in situ and operando X‐ray spectroscopy, scattering, and diffraction can be used as a complementary toolbox to interrogate the working principles of today's and tomorrow's bimetallic nanocatalysts. Bimetallic strip ; Nanomaterial-based catalyst ; Catalysis ; Nanotechnology ; Materials science ; Nanoparticle ; Characterization (materials science) ; Chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Christian Wülker | Sipu Ruan | Gregory S. Chirikjian
Source: SHILAP Revista de lepidopterología (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Quantizing Euclidean Motions via Double-Coset Decomposition Concepts from mathematical crystallography and group theory are used here to quantize the group of rigid-body motions, resulting in a “motion alphabet” with which robot motion primitives are expressed. From these primitives it is possible to develop a dictionary of physical actions. Equipped with an alphabet of the sort developed here, intelligent actions of robots in the world can be approximated with finite sequences of characters, thereby forming the foundation of a language in which robot motion is articulated. In particular, we use the discrete handedness-preserving symmetries of macromolecular crystals (known in mathematical crystallography as Sohncke space groups) to form a coarse discretization of the space SE(3) of rigid-body motions. This discretization is made finer by subdividing using the concept of double-coset decomposition. More specifically, a very efficient, equivolumetric quantization of spatial motion can be defined using the group-theoretic concept of a double-coset decomposition of the form Γ\SE(3)/Δ, where Γ is a Sohncke space group and Δ is a finite group of rotational symmetries such as those of the icosahedron. The resulting discrete alphabet is based on a very uniform sampling of SE(3) and is a tool for describing the continuous trajectories of robots and humans. An efficient coarse-to-fine search algorithm is presented to round off any motion sampled from the continuous group of motions to the nearest element of our alphabet. It is shown that our alphabet and this efficient rounding algorithm can be used as a geometric data structure to accelerate the performance of other sampling schemes designed for desirable dispersion or discrepancy properties. Moreover, the general “signals to symbols” problem in artificial intelligence is cast in this framework for robots moving continuously in the world. Group (periodic table) ; Mathematics ; Discretization ; Homogeneous space ; Rigid body ; Euclidean group ; Motion (physics) ; Euclidean space ; Algorithm ; Computer science ; Combinatorics ; Geometry ; Artificial intelligence ; Mathematical analysis ; Classical mechanics ; Affine space ; Physics Enzyme Structure and Function ; Machine Learning in Materials Science ; RNA and protein synthesis mechanisms
Authors: Michael H. Abraham | William E. Acree
Source: Journal of Solution Chemistry (article, 2019-05-01)
Field: Chemistry — Spectroscopy

Limiting Diffusion Coefficients for Ions and Nonelectrolytes in Solvents Water, Methanol, Ethanol, Propan-1-ol, Butan-1-ol, Octan-1-ol, Propanone and Acetonitrile at 298 K, Analyzed Using Abraham Descriptors We have used literature data on the conductivity of single ions to obtain limiting diffusion coefficients (Do) of ions in water, alcohols, propanone and acetonitrile. We then used literature data on limiting diffusion coefficients of nonelectrolytes in order to set up linear free energy relationships that combine data for ions and nonelectrolytes, as log10 (Do) in the same equation, all values being at 298 K. These equations are for solvents water (N = 377, SD = 0.0474), methanol (N = 96, SD = 0.0332), ethanol (N = 96, SD = 0.0666), propan-1-ol (N = 71, SD = 0.0487), butan-1-ol (N = 53, SD = 0.0600), octan-1-ol (N = 61, SD = 0.0684), propanone (N = 86, SD = 0.0366) and acetonitrile (N = 74, SD = 0.0438) where N is the number of data points, that is ions plus nonelectrolytes, and SD is the standard deviation in log10 (Do). It is shown that solute hydrogen bond acidity, solute hydrogen bond basicity and solute volume all lower the diffusion constants of ions and nonelectrolytes in the various solvents studied. Chemistry ; Acetonitrile ; Ion ; Diffusion ; Methanol ; Hydrogen ; Limiting ; Analytical Chemistry (journal) ; Thermodynamics ; Organic chemistry Diffusion Coefficients in Liquids ; Thermodynamic properties of mixtures ; Chemical and Physical Properties in Aqueous Solutions
Authors: Tianjun Li | Long Chen | Min Gan
Source: BMC Bioinformatics (article, 2019-11-06)
Field: Chemistry — Spectroscopy

Quality control of imbalanced mass spectra from isotopic labeling experiments Our results indicate that this framework is a powerful method for the peptide quality assessment. For the feature extraction part, the extracted ion chromatogram (XIC) based features contribute to the peptide quality assessment. To solve the imbalanced data problem, SMOTE brings a much better classification performance. Finally, the XGBoost is capable for the peptide quality control. Overall, our proposed framework provides reliable results for the further proteomics studies. Computer science ; Classifier (UML) ; Data mining ; Artificial intelligence ; Data quality ; Mass spectrometry ; Pattern recognition (psychology) ; Machine learning ; Chemistry ; Chromatography ; Engineering Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Jong Kiun | Tan Suet May Amelia | Kai‐Hee Huong | Amirul Al‐Ashraf Abdullah | Kesaven Bhubalan
Source: BioTechnologia (article, 2019-12-25)
Field: Materials Science — Biomaterials

Optimizing the biosynthesis of renewable polyhydroxyalkanoate copolymer containing 3-hydroxyvalerate by Massilia haematophila using statistical modeling Polyhydroxyalkanoate (PHA) is a microbial storage polymer that is naturally produced by certain bacteria. This is the first study on the ability of this particular species Massilia haematophila to synthesize a PHA copolymer containing 3-hydroxyvalerate (3HV) monomer. Using the statistical design on Massilia haematophila UMTKB-2, this study highlights the optimization of poly(3-hydroxybutyrate-co-3-hydroxyvalerate), P(3HB-co-3HV), copolymer production for shaken-flask cultivation. Moreover, the mechanical and thermal features of the polymers were determined. The production of P(3HB-co-3HV) by Massilia haematophila UMTKB-2 using optimal conditions provided by response surface methodology (RSM) yielded 5.0 g/l of P(3HB-co-7 mol% 3HV), which was higher than the value obtained from unoptimized conditions such as 4.40 g/l of P(3HB-co-4mol% 3HV). This result showed a 14% increase in copolymer concentration and a two-fold increase in 3HV composition. In this study, the P(3HB-co-3HV) synthesized was determined as a block copolymer and its thermal properties were better than P(3HB). Using RSM, the optimization conditions were successfully obtained for this bacterium, and this result is a starting platform for additional studies of a larger scaled PHA production from Massilia haematophila UMTKB-2 using bioreactors. Polyhydroxyalkanoates ; Copolymer ; Chemistry ; Biosynthesis ; Pulp and paper industry ; Organic chemistry ; Biology ; Bacteria ; Engineering ; Polymer ; Enzyme biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Electrospun Nanofibers in Biomedical Applications
Authors: M. Amirabedia | Samad Jafarmadar | Shahram Khalilarya | Javad Kheyrollahi
Source: International Journal of Engineering (article, 2019-05-01)
Field: Materials Science — Materials Chemistry

Experimental Comparison the Effect of Mn2O3 and Co3O4 Nano Additives on the Performance and Emission of SI Gasoline Fueled with Mixture of Ethanol and Gasoline In this paper, the use of gasoline combinations with ethanol and nano oxide particles has been studied in gasoline-based SI EF7 engines. The mixtures are prepared in five emulsions of gasoline with 10% ethanol, gasoline with 10% ethanol and 10ppm nano Mn2O3, gasoline with 10% ethanol and 20ppm nano Mn2O3, gasoline with 10% ethanol and 10ppm nano Co3O4, gasoline with 10% ethanol and 20ppm nano Co3O4. An ultrasonic cleaner device is used to obtain a homogeneous gasoline combination with ethanol and additives of nano oxides during the test. To measure the output power of the engine, the Eddy current dynamometer has been coupled to the engine and to determine the emissions, an AVL gas analyzer is used. The results indicate that ethanol addition by 10% lead to 2.6% increase in brake power (BP), but interestingly 10 ppm Mn2O3 nano-additive raise the BP to 14.38% and 20 ppm nano-additive led to 19.56% increase of BP. While the BP with 10ppm and 20ppm nano-additives of Co3O4 has increased by 7.96% and 11.5%, respectively. With regard to emissions, ethanol presence with nano additives in the blend reduces CO, UHC, and NOx whereas increases CO2. The best blend is gasoline-10% ethanol-20ppm Mn2O3. Gasoline ; Ethanol ; NOx ; Nano- ; Ethanol fuel ; Chemistry ; Materials science ; Organic chemistry ; Combustion ; Composite material Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Biodiesel Production and Applications