jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tharanga K. Wijethunga | Jelena Stojaković | Michael A. Bellucci | Xingyu Chen | Allan S. Myerson | Bernhardt L. Trout
Source: Langmuir (article, 2018-07-27)
Field: Materials Science — Materials Chemistry

General Method for the Identification of Crystal Faces Using Raman Spectroscopy Combined with Machine Learning and Application to the Epitaxial Growth of Acetaminophen Crystal morphology is one of the key crystallographic characteristics that governs the macroscopic properties of crystalline materials. The identification of crystal faces, or face indexing, is an important technique that is used to get information regarding a crystal's morphology. However, it is mainly limited to single crystal X-ray diffraction (SCXRD) and it is often not applicable to products of routine crystallizations becasue it requires high quality single crystals in a narrow size range. To overcome the limitations of the SCXRD method, we have developed a robust and convenient Raman face indexing method based on work by Moriyama et al. This method exploits small but detectable differences in Raman spectra of crystal faces caused by different orientations of the crystallographic axis relative to the direction and polarization of the excitation laser beam. The method requires the compilation of a Raman spectral library for each compound and must be built and validated by SCXRD face indexing. Once the spectral library is available for a compound, the identity of unknown crystal faces (from any crystal that is larger than laser beam) can be inferred by collecting and comparing the Raman spectra to spectra within the library. We have optimized this approach further by developing a machine-learning algorithm that identifies crystal faces by performing a statistical comparison of the spectra in the Raman library and the Raman spectra of the unknown crystal faces. Here, we report the development of the Raman face indexing method and apply it to three different epitaxial systems: Acetaminophen (APAP) grown as an overlayer crystal on d-mannitol (MAN), d-galactose (GAL), and xylitol (XYL) substrates. For each of these epitaxial systems, the crystals were grown under various experimental conditions and have a wide range of sizes and quality. Using the Raman face indexing method, we were able to perform high-throughput indexing of a large number of crystals from different crystallization conditions, which could not be achieved using SCXRD or other analytical techniques. Raman spectroscopy ; Epitaxy ; Identification (biology) ; Crystal growth ; Crystal (programming language) ; Materials science ; Acetaminophen ; Spectroscopy ; Chemistry ; Nanotechnology ; Artificial intelligence ; Computer science ; Crystallography ; Optics ; Physics ; Biology ; Biochemistry Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Benjamin Sanchez-Lengeling | Loı̈c M. Roch | José Darío Perea | Stefan Langner | Christoph J. Brabec | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2018-07-16)
Field: Materials Science — Materials Chemistry

A Bayesian Approach to Predict Solubility Parameters Solubility is a ubiquitous phenomenon in many aspects of material science. While solubility can be determined by considering the cohesive forces in a liquid via the Hansen solubility parameters (HSP), quantitative structure-property relationship models are often used for prediction, notably due to their low computational cost. Herein, we report gpHSP, an interpretable and versatile probabilistic approach to determining HSP. Our model is based on Gaussian processes (GP), a Bayesian machine learning approach that provides uncertainty bounds to prediction. gpHSP achieves its flexibility by leveraging a variety of input data, such as SMILES strings, COSMOtherm simulations, and quantum chemistry calculations. gpHSP is built on experimentally determined HSP, including a general solvents set aggregated from literature, and a polymer set experimentally characterized by this group of authors. In all sets, we obtained a high degree of agreement, surpassing well-established machine learning methods. We demonstrate the general applicability of gpHSP to miscibility of organic semiconductors, drug compounds and in general solvents, which can be further extended to other domains. gpHSP is a fast and accurate toolbox, which could be applied to molecular design for solution processing technologies. Solubility ; Bayesian probability ; Computer science ; Miscibility ; Flexibility (engineering) ; Toolbox ; Probabilistic logic ; Set (abstract data type) ; Consistency (knowledge bases) ; Biological system ; Algorithm ; Artificial intelligence ; Chemistry ; Polymer ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Timothy Hjorth | Michael Svärd | Åke C. Rasmuson
Source: CrystEngComm (article, 2018-11-19)
Field: Materials Science — Materials Chemistry

Rationalising crystal nucleation of organic molecules in solution using artificial neural networks Artificial neural network modelling is used to analyse and predict primary nucleation based on various physicochemical solute and solvent parameters. Nucleation ; Artificial neural network ; Crystal (programming language) ; Biological system ; Organic molecules ; Solvent ; Molecule ; Materials science ; Organic solvent ; Artificial intelligence ; Chemical physics ; Biochemical engineering ; Computer science ; Chemistry ; Organic chemistry ; Chemical engineering ; Engineering ; Biology Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Hoon Byun | Jae-Joon Song
Source: Tunnel and Underground Space (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Prediction of the Stress-Strain Curve of Materials under Uniaxial Compression by Using LSTM Recurrent Neural Network Artificial neural network ; Compression (physics) ; Strain (injury) ; Stress–strain curve ; Stress (linguistics) ; Artificial intelligence ; Structural engineering ; Computer science ; Materials science ; Deformation (meteorology) ; Composite material ; Engineering ; Medicine ; Anatomy High-Velocity Impact and Material Behavior ; Advanced machining processes and optimization ; Neural Networks and Applications
Authors: Mykhaylo Krykunov | Tom K. Woo
Source: Journal of Chemical Theory and Computation (article, 2018-08-27)
Field: Materials Science — Materials Chemistry

Bond Type Restricted Property Weighted Radial Distribution Functions for Accurate Machine Learning Prediction of Atomization Energies Understanding the performance of machine learning algorithms is essential for designing more accurate and efficient statistical models. It is not always possible to unravel the reasoning of neural networks. Here, we propose a method for calculating machine learning kernels in closed and analytic form by combining atomic property weighted radial distribution function (AP-RDF) descriptor with a Gaussian kernel. This allowed us to analyze and improve the performance of the Bag-of-Bonds descriptor when the bond type restriction is included in AP-RDF. The improvement is achieved for the prediction of molecular atomization energies (MAE = 1.7 kcal/mol for QM7 data set) and is due to the incorporation of a tensor product into the kernel, which captures the multidimensional representation of the AP-RDF. On the other hand, the numerical version of the AP-RDF is a constant size descriptor, making it more computationally efficient than Bag-of-Bonds. We have also discussed a connection between molecular quantum similarity and machine learning kernels with first-principles kinds of descriptors. Kernel (algebra) ; RDF ; Computer science ; Gaussian ; Property (philosophy) ; Artificial neural network ; Similarity (geometry) ; Type (biology) ; Artificial intelligence ; Function (biology) ; Representation (politics) ; Machine learning ; Gaussian function ; Algorithm ; Mathematics ; Computational chemistry ; Chemistry ; Discrete mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Khushmeen Sakloth | Wesley Beckner | Jim Pfaendtner | Garrett B. Goh
Source:  (article, 2018-12-01)
Field: Materials Science — Materials Chemistry

IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks Deep neural networks (DNN) excel at extracting patterns. Through representation learning and automated feature engineering on large datasets, such models have been highly successful in computer vision and natural language applications. Designing optimal network architectures from a principled or rational approach, however, has been less than successful, with the best successful approaches utilizing an additional machine learning algorithm to tune the network hyperparameters. This is despite that, in many technical fields, there exist established domain knowledge and understanding about the subject matter. In this work, we develop a novel furcated neural network architecture that utilizes domain knowledge as high-level design principles of the network. We demonstrate proof-of-concept by developing IL-Net, a furcated network for predicting the properties of ionic liquids, which is a class of complex multi-chemical entities. Compared to existing state-of-the-art approaches, we show that furcated networks can improve model accuracy by approximately ~20-35%, without using additional labeled data. Lastly, we distill two key design principles for furcated networks that can be adapted to other domains. Computer science ; Artificial intelligence ; Feature engineering ; Artificial neural network ; Machine learning ; Domain (mathematical analysis) ; Hyperparameter ; Key (lock) ; Subject-matter expert ; Domain knowledge ; Class (philosophy) ; Representation (politics) ; Feature (linguistics) ; Nervous system network models ; Deep learning ; Expert system ; Recurrent neural network ; Types of artificial neural networks Machine Learning in Materials Science ; Ionic liquids properties and applications ; Catalysis and Oxidation Reactions
Authors: Mehdi Hajizadegan | Pai‐Yen Chen
Source:  (article, 2018-07-01)
Field: Materials Science — Materials Chemistry

Harmonics-Based RFID Sensor Based on Graphene Frequency Multiplier and Machine Learning We present here a compact graphene-based radiofrequency identification (RFID) sensor that can simultaneously detect two (bio-)chemical agents with the molecular-level sensitivity. We demonstrate that a simple circuit comprising the functionalized graphene transistors can realize a chemically-sensitive frequency modulator, whose output high harmonics are sensitively influenced by two (bio-)chemical agents. Moreover, the machine learning method is used to analyze multiple output harmonics for retrieving chemical properties of (bio-)chemical agents under detection. The proposed RFID sensor may find various wireless sensing applications and internet-of-things (IoTs). Harmonics ; Graphene ; Frequency multiplier ; Computer science ; Internet of Things ; Chemical sensor ; Bandwidth (computing) ; Sensitivity (control systems) ; Electronic engineering ; Wireless ; Multiplier (economics) ; Materials science ; Electrical engineering ; Nanotechnology ; Embedded system ; Telecommunications ; Engineering ; Physics ; Voltage ; Electrode Graphene research and applications ; Molecular Junctions and Nanostructures ; Conducting polymers and applications
Authors: Abdurrahman Elbasir | M. Balasubramanian | Khalid Kunji | Prasanna R. Kolatkar | Halima Bensmail | Raghvendra Mall
Source:  (article, 2018-12-01)
Field: Materials Science — Materials Chemistry

DeepCrystal: A Deep Learning Framework for Sequence-based Protein Crystallization Prediction Protein structure determination has primarily been performed using X-ray crystallography. To overcome the expensive cost, high attrition rate and series of trial-and-error settings, many in-silico methods have been developed to predict crystallization propensities of proteins based on their sequences. However, majority of these methods build predictors by extracting features from protein sequences which is computationally expensive and can potentially explode the feature space. We propose, DeepCrystal, a deep learning framework for sequence-based protein crystallization prediction. It uses deep learning to identify proteins which can produce diffraction quality crystals without the need to manually engineer additional biochemical and structural features from sequence. Our model is based on Convolutional Neural Networks (CNNs) which can exploit frequently occurring k-mers and sets of k-mers from the protein sequences to discriminate diffraction quality crystals from non-crystallizable ones. Our model outperforms previous sequence-based protein crystallization predictors in terms of recall, F-score, accuracy and MCC on three independent test sets. DeepCrystal achieves an average improvement of 1.4 %, 12.1% in recall, when compared to its closest competitors, Crysalis II and Crysf respectively. In addition, DeepCrystal attains an average improvement of 2.1%, 6.0% for F-score, 1.9%, 3.9% for accuracy and 3.8%, 7.0% for MCC respectively w.r.t. Crysalis II and Crysf on independent test sets. The standalone source code and models are available at https://github.com/elbasir/DeepCrystal and a web-server is also available at https://deeplearning-protein.qcri.org. Computer science ; Protein crystallization ; Artificial intelligence ; Sequence (biology) ; Deep learning ; Convolutional neural network ; Protein sequencing ; Algorithm ; Crystallization ; Machine learning ; Pattern recognition (psychology) ; Peptide sequence ; Biology Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Bioinformatics
Authors: Abiola J. Adeyi | Mondiu Ọlayinka Durowoju | Oladayo Adeyi
Source: Landmark University Repository (Landmark University) (article, 2018-11-30)
Field: Materials Science — Polymers and Plastics

EXPERIMENTAL STUDIES AND ARTIFICIAL NEURAL NETWORKS (ANN) MODELING OF MOISTURE ABSORPTION CHARACTERISTICS OF POLYESTER/MOMODICALFIBRE REINFORCED COMPOSITE Nowadays, there are interests around the world concerning the applicability of natural fibers in polymer composite development fortechnological advancement. Natural fiber inclusion in synthetic polymer is termed partially degradable polymer composite which is considered environmentally friendly and acceptable. However, a notable deficiency is their poor moisture resistance behaviour that degrades their mechanical properties over time. This work therefore investigated the effect of Momodical fiber fractions on the moisture absorption properties ofPolyester/Momodical fiber reinforced composite. The Momodical fibers were alkali treatedto improve the fiber properties. The Polyester/Momodical fiber composite were developed by incorporating the alkali treated Momodical fibers in the weight fraction of 10, 20, 30 and 40 % in polyester resin. Water immersion test was used to evaluate the water absorption characteristics from which the water diffusion mechanisms of the developed composites were established. For the sake of system behaviour prediction and control, Artificial Neural Networks (ANN) was used for modeling and prediction of the moisture gain ofthe Polyester/Momodical reinforced composites. Scanning Electron Microscope (SEM) was used to elucidate the morphology of raw and alkali treated Momodical fiber. The results showed that the water absorption process was diffusion controlled and diffusion mechanisms cut across less Fickian / Fickian behavior for investigated composites. The composite weight gained and percentage water absorption increased with increased immersion time and fiber loading. The moisture diffusivity ranged from 1.98 E – 12 to5.38 E - 12. The ANN structure 2-5-1-1 developed using 'tansig' 'tansig' 'purelin'transfer function showed a high capability and reliability in modeling and prediction of moisture gain observed in the developed composites. The results suggested an outdoor application in desert cooler pad and built material development where the moisture diffusion tendencies could be beneficial and loss of mechanical strength is trivial. Composite material ; Materials science ; Absorption of water ; Composite number ; Polyester ; Natural fiber ; Fiber ; Scanning electron microscope ; Synthetic fiber ; Moisture ; SILK ; Polymer Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Additive Manufacturing and 3D Printing Technologies
Authors: Mihriban Kalkancı | Mahmut Sınecen | Gülseren Kurumer
Source: DergiPark (Istanbul University) (article, 2018-03-31)
Field: Materials Science — Polymers and Plastics

PREDICTION OF DIMENSIONAL CHANGE IN FINISHED FABRIC THROUGH ARTIFICIAL NEURAL NETWORKS When anti-shrinkage precaution is taken for finishing processes, shrinkage could be observed with cotton and viscose fabrics by 8-15% and 20%, respectively. Therefore, capability of estimation of shrinkage rate for fabrics at the end of finishing would be a significant advantage. This study&amp;amp;nbsp;tried to estimate the shrinkage of single jersey and interlock fabrics at the end of relaxation processes by means of the Artificial Neural Networks (ANN). To that end&amp;amp;nbsp;totally 72 varieties of fabric were manufactured in two groups of the elastane and the non-elastane fabrics. Then, in each of two groups included 36 different varieties on the basis of single jersey and interlock weaving types using six different&amp;amp;nbsp;raw materials in three different densities.The processes were applied to fabrics during finishing process are thermo-fixing, washing, drying and sanforizing process.ANN model was used to predict dimensional change at the end of the sanforizing. For ANN, the two-layer feed-forward perceptron, also called single hidden layer feed-forward neural network was used to estimate dimensional change of width and length.&amp;amp;nbsp;Finally, the ANN exhibited successful performance in prediction of dimensional change in fabrics. The prediction of the dimensional properties produced by the neural network model was proved to be highly reliable (R2&amp;amp;gt; 0.98). Artificial neural network ; Artificial intelligence ; Computer science Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Industrial Vision Systems and Defect Detection
Authors: Ankita Mangal | Elizabeth A. Holm
Source: Data in Brief (article, 2018-11-03)
Field: Materials Science — Ceramics and Composites

A dataset of synthetic hexagonal close packed 3D polycrystalline microstructures, grain-wise microstructural descriptors and grain averaged stress fields under uniaxial tensile deformation for two sets of constitutive parameters This data article presents a data set comprised of 54 synthetic 3D equiaxed polycrystalline microstructures, the microstructural descriptors for each grain and the stress fields resulting from two sets of crystal plasticity simulations mimicking uniaxial tensile deformation to a total strain of 2%. This is related to the research article entitled "Applied Machine Learning to predict stress hotspots II: Hexagonal Close Packed Materials" (Mangal and Holm, 2018). The microstructures were created using an open source Dream.3D software tool and the crystal plasticity simulations were carried out using elasto-viscoplastic fast Fourier transform (EVPFFT) method. Eight different kinds of HCP textures are represented with stochastically different microstructures with varying texture intensity for each texture kind. For each texture kind, between six and nine stochastically different microstructures with varying texture intensity (measured by multiples of random density (MRD)) are created. This dataset is freely available in two Mendeley Data archives "Synthetic HCP 3D polycrystalline microstructures with grain-wise microstructural descriptors and stress fields under uniaxial tensile deformation: Part One" and "Synthetic HCP 3D polycrystalline microstructures with grain-wise microstructural descriptors and stress fields under uniaxial tensile deformation: Part Two" located at http://dx.doi.org/10.17632/kt8hfg4t2p.1 and http://dx.doi.org/10.17632/nsfn6tw295.1 respectively for any academic, educational, or research purposes. Equiaxed crystals ; Materials science ; Microstructure ; Crystallite ; Texture (cosmology) ; Deformation (meteorology) ; Ultimate tensile strength ; Stress (linguistics) ; Composite material ; Metallurgy ; Artificial intelligence ; Computer science Advanced ceramic materials synthesis ; Orthopaedic implants and arthroplasty ; Microstructure and mechanical properties
Authors: Miao Zhang | Xinggao Liu | Zeyin Zhang | Guangbi Gong | Guoqing Zhang | Chang-Jun Zhang | Xu Chen | Hongxing Zhang
Source: Transactions of the Institute of Measurement and Control (article, 2018-09-20)
Field: Chemistry — Analytical Chemistry

A novel modeling approach and its application in polymer quality index prediction Accurate and reliable quality index prediction is indispensable in quality control of the industrial propylene polymerization (PP) processes. This paper presents a novel modeling approach for quality index prediction based on optimal fuzzy wavelet neural network (FWNN) with improved gravitational search algorithm (IGSA), where the constant or a linear function of inputs in conclusion part of traditional TSK fuzzy model is replaced with wavelet neural network (WNN). Then, an online learning algorithm of the FWNN model is derived by using gradient descent algorithm, and an IGSA algorithm is proposed to online adapt the learning rates of FWNN. Research on the proposed soft sensor is carried out with the data from a real industrial PP plant, and the results are compared among the WNN, FWNN and IGSA-FWNN models. The research results show that the proposed prediction model achieves a good performance in practical industrial quality index, melt index, prediction process. Gradient descent ; Computer science ; Artificial neural network ; Quality (philosophy) ; Data mining ; Wavelet ; Fuzzy logic ; Process (computing) ; Artificial intelligence ; Index (typography) ; Algorithm Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Chemical Sensor Technologies
Authors: María Rosa | Daniela Helena Pelegrine Guimarães | Pedro F. Arce
Source: International Journal of Heat and Mass Transfer (article, 2018-10-16)
Field: Materials Science — Biomaterials

Experimental measurements and simulation of the fouling phenomena of natural proteins Fouling ; Mass transfer ; Materials science ; Solubility ; Dynamic scraped surface heat exchanger ; Heat transfer ; Chemical engineering ; Heat exchanger ; Turbulence ; Thermodynamics ; Mechanics ; Work (physics) ; Heat transfer coefficient ; Biological system ; Chemistry ; Physics ; Organic chemistry ; Biochemistry Calcium Carbonate Crystallization and Inhibition ; Proteins in Food Systems ; Magnetic and Electromagnetic Effects