jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohd Razib | Tanveer Saleh
Source: Current Nanoscience (review, 2018-08-23)
Field: Materials Science — Materials Chemistry

A Review on Micro-Patterning Processes of Vertically Aligned Carbon Nanotubes Array (VACNTs Array) Vertically Aligned Carbon Nanotubes array which is also sometimes labeled as carbon nanotubes forests has many applications in several engineering fields for its remarkable mechanical, electrical, optical, and thermal properties. The Vertically Aligned Carbon Nanotubes array is often employed in developing microdevices such as pressure sensor, angle sensor, switches, etc. To successfully integrate carbon nanotubes forest to the micro-electro-mechanical systems based devices, micropatterning of the carbon nanotubes forest is required. There are several methods available to realize micropatterning of Vertically Aligned Carbon Nanotubes array, from in-situ patterning during the growth process to post-patterning process. Each has its advantages and disadvantages. This paper will discuss elaborately different patterning processes of the carbon nanotubes forest and their different characteristics. Carbon nanotube ; Micropatterning ; Materials science ; Nanotechnology ; Carbon fibers ; Potential applications of carbon nanotubes ; Optical properties of carbon nanotubes ; Composite material ; Nanotube Carbon Nanotubes in Composites ; Nanowire Synthesis and Applications ; Mechanical and Optical Resonators
Authors: Y. Kemari | A. Mekhaldi | M. Teguar | G. Teyssèdre
Source: 2018 IEEE 2nd International Conference on Dielectrics (ICD) (article, 2018-07-01)
Field: Materials Science — Materials Chemistry

Nonlinear Regression Modeling to Predict Thermal Endurance of XLPE Material under Thermal Aging In this work, the mechanical behavior of XLPE under accelerated thermal aging is investigated. The tensile strength and elongation at break are used as aging markers in order to assess the degradation level of the insulation. In the aim to describe the evolution of the mechanical properties with aging and to estimate the insulation lifetime, a nonlinear regression model is proposed. The model parameters have been estimated using the method of least squares. The experimental results show that thermal aging leads to mechanical degradation of XLPE by decreasing its tensile strength and elongation at break. The critical deterioration of the material was reached at 120 and 140 °C. The validity of the regression model was verified and the results show that one-phase exponential decay expression could well fit the experimental data. In addition, thermal endurance of XLPE was investigated using the Arrhenius law. On the other hand, permittivity and AC conductivity have been monitored in order to study their correlation with thermal aging. Materials science ; Arrhenius equation ; Ultimate tensile strength ; Composite material ; Nonlinear regression ; Elongation ; Exponential function ; Accelerated aging ; Thermal conductivity ; Thermal ; Nonlinear system ; Exponential decay ; Regression analysis ; Thermodynamics ; Activation energy ; Mathematics ; Chemistry ; Statistics ; Physics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Advanced Battery Technologies Research
Authors: Eric Gossett | Cormac Toher | Corey Oses | Olexandr Isayev | Fleur Legrain | Frisco Rose | Eva Zurek | Jesús Carrete | Natalio Mingo | Alexander Tropsha | Stefano Curtarolo
Source: Computational Materials Science (preprint, 2018-05-31)
Field: Materials Science — Materials Chemistry

AFLOW-ML: A RESTful API for machine-learning predictions of materials properties Workflow ; Computer science ; Unicode ; Cloud computing ; Python (programming language) ; Artificial intelligence ; Machine learning ; Software engineering ; Programming language ; Database ; Operating system Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Onur Çaylak | Anil Yaman | Björn Baumeier
Source: ChemRxiv (preprint, 2018-12-20)
Field: Materials Science — Materials Chemistry

An Evolutionary Approach to Constructing a Deep Feedforward Neural Network for Prediction of Electronic Coupling Elements in Molecular Materials &lt;div&gt;We present a general framework for the construction of a deep feedforward neural network (FFNN) to predict distance and orientation dependent electronic coupling elements in disordered molecular materials. An evolutionary algorithm automatizes the selection of an optimal architecture of the artificial neural network within a predefined search space. Systematic guidance, beyond minimizing the model error with stochastic gradient descent based backpropagation, is provided by simultaneous maximization of a model fitness that takes into account additional physical properties, such as the field-dependent carrier mobility. As a prototypical system, we consider hole transport in amorphous tris(8-hydroxyquinolinato)aluminum. Reference data for training and validation is obtained from multiscale ab initio simulations, in which coupling elements are evaluated using density-functional theory, for a system containing 4096 molecules. The Coulomb matrix representation is chosen to encode the explicit molecular pair coordinates into a rotation and translation invariant feature set for the FFNN. The final optimized deep feedforward neural network is tested for transport models without and with energetic disorder. It predicts electronic coupling elements and mobilities in excellent agreement with the reference data. Such a FFNN is readily applicable to much larger systems at negligible computational cost, providing a powerful surrogate model to overcome the size limitations of the ab initio approach.&lt;/div&gt; Computer science ; Artificial neural network ; Backpropagation ; Feedforward neural network ; Feed forward ; Ab initio ; Artificial intelligence ; Coupling (piping) ; Biological system ; Topology (electrical circuits) ; Physics ; Materials science ; Mathematics ; Quantum mechanics ; Engineering Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: Connor W. Coley | Wengong Jin | Luke Rogers | Timothy F. Jamison | Tommi Jaakkola | William H. Green | Regina Barzilay | Klavs F. Jensen
Source: ChemRxiv (preprint, 2018-10-04)
Field: Materials Science — Materials Chemistry

A Graph-Convolutional Neural Network Model for the Prediction of Chemical Reactivity We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). The prediction task is factored into two stages comparable to manual expert approaches: considering possible sites of reactivity and evaluating their relative likelihoods. By training on hundreds of thousands of reaction precedents covering a broad range of reaction types from the patent literature, the neural model makes informed predictions of chemical reactivity. The model predicts the major product correctly over 85% of the time requiring around 100 ms per example, a significantly higher accuracy than achieved by previous machine learning approaches, and performs on par with expert chemists with years of formal training. We gain additional insight into predictions via the design of the neural model, revealing an understanding of chemistry qualitatively consistent with manual approaches. Computer science ; Artificial neural network ; Artificial intelligence ; Machine learning ; Reactivity (psychology) ; Task (project management) ; Convolutional neural network ; Graph ; Range (aeronautics) ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ivaylo Belovski | Todor Petkov | Sotir Sotirov | Anatoliy Aleksandrov
Source:  (article, 2018-05-01)
Field: Materials Science — Materials Chemistry

Prediction of the parameters of a Thermoelectric Battery with Neural Network Recently, active developments of projects related to alternative energy sources can be observed, including revival of long known effects like the effect of Seebeck. The present paper presents studies on three thermoelectric modules working in a generator mode. On the basis of the results obtained, a neural network for prediction of the parameters of the thermoelectric batteries made from such modules was synthesized. Thermoelectric effect ; Artificial neural network ; Battery (electricity) ; Thermoelectric generator ; Computer science ; Generator (circuit theory) ; Thermoelectric materials ; Seebeck coefficient ; Electrical engineering ; Materials science ; Power (physics) ; Engineering ; Artificial intelligence ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Gas Sensing Nanomaterials and Sensors ; Advanced Thermodynamics and Statistical Mechanics
Authors: Ben Nebgen | Nick Lubbers | Justin S. Smith | Andrew E. Sifain | Andrey Y. Lokhov | Olexandr Isayev | Adrián E. Roitberg | Kipton Barros | Sergei Tretiak
Source: arXiv (Cornell University) (preprint, 2018-03-12)
Field: Materials Science — Materials Chemistry

Transferable Molecular Charge Assignment Using Deep Neural Networks We use HIP-NN, a neural network architecture that excels at predicting molecular energies, to predict atomic charges. The charge predictions are accurate over a wide range of molecules (both small and large) and for a diverse set of charge assignment schemes. To demonstrate the power of charge prediction on non-equilibrium geometries, we use HIP-NN to generate IR spectra from dynamical trajectories on a variety of molecules. The results are in good agreement with reference IR spectra produced by traditional theoretical methods. Critically, for this application, HIP-NN charge predictions are about 104 times faster than direct DFT charge calculations. Thus, ML provides a pathway to greatly increase the range of feasible simulations while retaining quantum-level accuracy. In summary, our results provide further evidence that machine learning can replicate high-level quantum calculations at a tiny fraction of the computational cost. Charge (physics) ; Artificial neural network ; Quantum ; Range (aeronautics) ; Set (abstract data type) ; Computer science ; Spectral line ; Statistical physics ; Molecule ; Algorithm ; Physics ; Computational physics ; Topology (electrical circuits) ; Artificial intelligence ; Materials science ; Quantum mechanics ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Marwah N. Mohammed | Kamal Yusoh | Jun Haslinda Shariffuddin
Source: MATEC Web of Conferences (article, 2018-01-01)
Field: Materials Science — Biomaterials

Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network A novel comparison study based on a radial basis function neural network (RBFNN) and Response Surface Methodology (RSM) is proposed to predict the conversion rate (yield) of the experimental data for PNVCL polymerization. A statistical and optimization model was performing to show the effect of each parameter and their interactions on the conversion rate. The influence of the time, polymerization temperature, initiator concentration and concentration of the monomer were studied. The results obtained in this study indicate that the RBFNN was an effective method for predicting the conversion rate. The time of the PNVCL polymerization as well as the concentration of the monomer show the maximum effect on the conversion rate. In addition, compared with the RSM method, the RBFNN showed better conversion rate comparing with the experimental data. Response surface methodology ; Polymerization ; Parametric statistics ; Materials science ; Monomer ; Biological system ; Polymer ; Artificial neural network ; Chemical engineering ; Computer science ; Composite material ; Chromatography ; Chemistry ; Mathematics ; Artificial intelligence ; Statistics biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Advanced Polymer Synthesis and Characterization
Authors: Jiang Wang | Simon Olsson | Christoph Wehmeyer | Adrià Pérez | Nicholas E. Charron | Gianni De Fabritiis | Frank Noé | Cecilia Clementi
Source: arXiv (Cornell University) (preprint, 2018-12-04)
Field: Materials Science — Materials Chemistry

Machine Learning of coarse-grained Molecular Dynamics Force Fields Atomistic or ab-initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multi-body terms that emerge from the dimensionality reduction. Granularity ; Computer science ; Curse of dimensionality ; Molecular dynamics ; Statistical physics ; Dimensionality reduction ; Artificial intelligence ; Machine learning ; Algorithm ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: R.I. Zubatyuk | Justin S. Smith | Jerzy Leszczyński | Olexandr Isayev
Source: ChemRxiv (preprint, 2018-10-01)
Field: Materials Science — Materials Chemistry

Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network &lt;p&gt;Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets the state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in computational cost. With AIMNet we show a new dimension of transferability: the ability to learn new targets utilizing multimodal information from previous training. The model can learn implicit solvation energy (like SMD) utilizing only a fraction of original training data, and archive MAD error of 1.1 kcal/mol compared to experimental solvation free energies in MNSol database.&lt;/p&gt; Benchmark (surveying) ; Solvation ; Artificial neural network ; Transferability ; Atom (system on chip) ; Dimension (graph theory) ; Computer science ; Modalities ; Modular design ; Artificial intelligence ; Modality (human–computer interaction) ; Quantum ; Machine learning ; Algorithm ; Molecule ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Tomohiko Konno | Hodaka Kurokawa | Fuyuki Nabeshima | Ryō Ogawa | Michiaki Iwazume | Iwao Hosako | Atsutaka Maeda
Source: arXiv (Cornell University) (article, 2018-12-03)
Field: Materials Science — Materials Chemistry

Deep Learning of Superconductors I: Estimation of Critical Temperature of Superconductors Toward the Search for New Materials Exploration of new superconductors still relies on the experience and intuition of experts and is largely a process of experimental trial and error. In one study, only 3% of the candidate materials showed superconductivity. Here, we report the first deep learning model for finding new superconductors. We introduced the method named reading periodic which represented the periodic table in a way that allows deep learning to learn to read the periodic table and to learn the law of elements for the purpose of discovering novel superconductors that are outside the training data. It is recognized that it is difficult for deep learning to predict something outside the training data. Although we used only the chemical composition of materials as information, we obtained an $R^{2}$ value of 0.92 for predicting $T_\text{c}$ for materials in a database of superconductors. We also introduced the method named garbage-in to create synthetic data of non-superconductors that do not exist. Non-superconductors are not reported, but the data must be required for deep learning to distinguish between superconductors and non-superconductors. We obtained three remarkable results. The deep learning can predict superconductivity for a material with a precision of 62%, which shows the usefulness of the model; it found the recently discovered superconductor CaBi2 and another one Hf0.5Nb0.2V2Zr0.3, neither of which is in the superconductor database; and it found Fe-based high-temperature superconductors (discovered in 2008) from the training data before 2008. These results open the way for the discovery of new high-temperature superconductor families. The candidate materials list, data, and method are openly available from the link this https URL. Superconductivity ; Deep learning ; Condensed matter physics ; Periodic table ; Artificial intelligence ; High-temperature superconductivity ; Physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Rare-earth and actinide compounds ; Advanced Materials Characterization Techniques
Authors: Melih İnal | Şenol Şahin | Yıldız Şahin
Source: IFAC-PapersOnLine (article, 2018-01-01)
Field: Materials Science — Polymers and Plastics

Optimization of the Young’s Modulus of Low Flow Polypropylene Talc/Colemanite Hybrid Composite Materials with Artificial Neural Networks © 2016In this experimental study, Taguchi experiment design and Artificial Neural Networks (ANN) methods were used to optimize the Young's modulus of polypropylene / talc / colemanite hybrid composite materials depending on their production parameters and hybrid filler ratios in different percentages by weight. Low flow polypropylene homopolymer (LPPH) is a thermoplastic widely used in the automotive industry as a buffer and fender material (raw material) because of its high impact resistance. It is known, that in the automotive industry, weight reduction efforts are under way. However, there is currently no published scientific and / or experimental studies on the Tensile properties of polypropylene / talc / colemanite hybrid composite materials. On the other hand, it is also known that the Tensile properties of thermoplastic composites are influenced by injection molding parameters and that these characteristic features influenced by injection molding parameters can not be predicted and/or determined, for example from analysis and/or computer-aided design programs. In this context, in the first phase of the study, the predicted boundary conditions of the design parameters such as hybrid filler ratio and injection parameters such as nozzle temperature, injection speed and mold temperature were determined, which can influence the tensile properties and also increase and / or decrease the Young's modulus. In the second phase of the study, tensile tests were performed on talc (TC) and colemanite (GC) filled polypropylene (LPPH) hybrid composite materials according to ISO 527-1, and the ANN method was used to model the Young's modulus of LPPH / TC / GC hybrid composite materials. The results obtained were compared with the ANOVA results obtained with the Taguchi experiment design method. It was found that the ANN method provided satisfactory results. Polypropylene ; Talc ; Composite number ; Materials science ; Composite material ; Artificial neural network ; Modulus ; Computer science ; Artificial intelligence Polymer Nanocomposites and Properties ; Asphalt Pavement Performance Evaluation ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Joshua F. Einsle | Ben Martineau | Iris Buisman | Zoja Vukmanovic | Duncan N. Johnstone | Alex Eggeman | Paul A. Midgley | R. J. Harrison
Source: Microscopy and Microanalysis (article, 2018-08-01)
Field: Materials Science — Materials Chemistry

All Mixed Up: Using Machine Learning to Address Heterogeneity in (Natural) Materials An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button. Computer science ; Action (physics) ; Natural (archaeology) ; Content (measure theory) ; Artificial intelligence ; Mathematics ; Geography ; Physics ; Archaeology Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Cuong Q. Nguyen
Source: eScholarship (California Digital Library) (article, 2018-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning for SERS Quantitative Detection of Pyocyanin Since its discovery in 1977, surface-enhanced Raman scattering (SERS) spectroscopy has been cemented as a powerful spectroscopic technique. Taking advantage of local electric field enhancement from plasmonic nanostructures, SERS provides vibrational fingerprints down to the single molecule detection limits. Yet fully capitalizing on the technique has proven challenging. The problem is rooted in (1) inherent variances in SERS enhancement factors and (2) dated spectral analysis technique. To address (1), I present a fabrication scheme that produces optically uniform SERS substrates by employing electrohydrodynamic flow to drive chemical crosslinking between colloidal gold nanospheres. The resulting substrates exhibit SERS signals with relative standard deviation of 10.4 % over 100 × 100 μm2. With pyocyanin as analyte - a secondary metabolite produced by P. aeruginosa - SERS substrates exhibit limit of quantification of 1 ng·mL-1 and robust quantification of concentrations spanning 5-orders of magnitude. To address (2), I implemented three machine learning algorithms to analyze SERS spectra. Partial least squares regression is crucial in monitoring of P. aeruginosa biollm formation in a microfluidic environment, enabling detection as early as 3 h after inoculation in complex media. Feedforward artificial neural networks trained on pyocyanin data produces prediction errors of 6.2 ± 1.1 %. Finally, 1D convolutional neural networks trained with spectra stack further reduces prediction errors to an impressive 4.9 ± 0.9 %. Overall, this thesis demonstrates SERS spectroscopy as a potential diagnostic tool while laying the foundation to fully exploit its sensing capability by integrating machine learning in the analysis pipeline. Surface-enhanced Raman spectroscopy ; Pyocyanin ; Detection limit ; Analyte ; Artificial intelligence ; Nanotechnology ; Raman scattering ; Computer science ; Materials science ; Machine learning ; Chemistry ; Raman spectroscopy ; Optics ; Physics ; Chromatography Gold and Silver Nanoparticles Synthesis and Applications ; Water Quality Monitoring and Analysis