jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Frank Noé
Source: arXiv (Cornell University) (preprint, 2018-12-18)
Field: Materials Science — Materials Chemistry

Machine Learning for Molecular Dynamics on Long Timescales Molecular Dynamics (MD) simulation is widely used to analyze the properties of molecules and materials. Most practical applications, such as comparison with experimental measurements, designing drug molecules, or optimizing materials, rely on statistical quantities, which may be prohibitively expensive to compute from direct long-time MD simulations. Classical Machine Learning (ML) techniques have already had a profound impact on the field, especially for learning low-dimensional models of the long-time dynamics and for devising more efficient sampling schemes for computing long-time statistics. Novel ML methods have the potential to revolutionize long-timescale MD and to obtain interpretable models. ML concepts such as statistical estimator theory, end-to-end learning, representation learning and active learning are highly interesting for the MD researcher and will help to develop new solutions to hard MD problems. With the aim of better connecting the MD and ML research areas and spawning new research on this interface, we define the learning problems in long-timescale MD, present successful approaches and outline some of the unsolved ML problems in this application field. Computer science ; Field (mathematics) ; Representation (politics) ; Machine learning ; Molecular dynamics ; Estimator ; Sampling (signal processing) ; Artificial intelligence ; Dynamics (music) ; Mathematics ; Physics ; Chemistry ; Statistics ; Computational chemistry Machine Learning in Materials Science ; Machine Learning and Algorithms ; Neural Networks and Applications
Authors: Bhagoji Bapurao Sul | Chinari Subhechha Subudhi | K. Dhanalakshmi
Source:  (article, 2018-10-01)
Field: Materials Science — Materials Chemistry

Neural Network Based Displacement Modeling of Shape Memory Alloy Spring Actuator Spring actuator is a widely used component in most engineering applications. The active spring actuator i.e., shape memory alloy (SMA) spring allows larger work space, force and controllable stiffness than SMA wires. Due to the highly nonlinear behavior of SMA actuator, a Neural Network is preferable to be employed to model the functional relationship and to predict the displacement of the actuator using force, temperature and current under different operating conditions. This paper presents a neural network based electro-thermo-mechanical model of displacement of an SMA helical spring actuator under different currents and different frequencies. The inputs to the neural network are proposed in such a way that enough information is given, to be able to predict the displacement for a range of initial conditions. An experimental facility is realized; experiments are performed to validate the model. SMA* ; Actuator ; Shape-memory alloy ; Spring (device) ; Displacement (psychology) ; Artificial neural network ; Stiffness ; Nonlinear system ; Control theory (sociology) ; Work (physics) ; Materials science ; Structural engineering ; Computer science ; Mechanical engineering ; Engineering ; Artificial intelligence ; Physics Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Topology Optimization in Engineering
Authors: Yıldız Şahin | Şenol Şahin | Melih İnal
Source: IFAC-PapersOnLine (article, 2018-01-01)
Field: Materials Science — Polymers and Plastics

Modelling of the Tensile Properties of Calcium Carbonate Filled Polypropylene Composite Materials with Taguchi and Artificial Neural Networks © 2016In this experimental study, Taguchi experiment design and Artificial Neural Networks methods were used to optimize the Tensile strength of polypropylene / calcium carbonate composite materials depending on their production parameters and filler ratios in different percentages by weight. The long-term strength (life or time) values of materials are usually obtained as a result of long-term tests also after production. For example it's known that tensile strength of polypropylene based material is decreased depending upon time after production. But changes to tensile properties, depending upon CC filler content of PPH/CC composite materials, after a long storage period which follows its production are still unclear. In this context, in this study, Tensile strength properties optimization of the calcium carbonate (CC) filled polypropylene homopolymer (PPH) composite, depending on the injection parameters is studied via Taguchi Experimental Design Method and Artificial Neural Networks (ANN). To examine the effect of injection parameters the injection pressure, the melt temperature and mold temperature impose conditions are used. CC-filled composite samples have been produced at different injection parameters by injection molding based on Taguchi experimental design method. Samples were stored for 12 months without daylight comfort conditions. Tensile tests for samples are made according to the ISO 527 standard. By this experimental study, parameter optimizations for Tensile strength properties of the PPH / CC composite materials have been revealed. At the second phase of the study, a fully connected feedforward Artificial Neural Network is utilized for modeling the Tensile strength properties of CC filled PP composite materials. Ultimate tensile strength ; Materials science ; Polypropylene ; Taguchi methods ; Composite material ; Composite number ; Calcium carbonate ; Molding (decorative) ; Context (archaeology) ; Izod impact strength test Polymer crystallization and properties ; Natural Fiber Reinforced Composites ; Injection Molding Process and Properties
Authors: Håkon Wiik Ånes | Ingrid Marie Vincent Andersen | Antonius T. J. van Helvoort
Source: Microscopy and Microanalysis (article, 2018-08-01)
Field: Materials Science — Materials Chemistry

Crystal Phase Mapping by Scanning Precession Electron Diffraction and Machine Learning Decomposition Analyzing the distribution of crystal phases is required to understand nanostructures. Conventional analysis in the transmission electron microscope (TEM) include acquiring dark-field (DF) images for each expected crystal phases. However, this becomes laborious if a series of phases are present, a nm-scale resolution is required and larger areas (&gt; 0.5 µm2 ) are to be analyzed. The latter is even worse for highresolution (HR) TEM. An alternative approach is scanning precession electron diffraction (SPED), allowing for automatic determination of the crystal phase distribution. Here we combine SPED with machine learning to a model system of a ~400 nm-thick GaAs nanowire (NW) with multiple axial GaAsSb-based superlattices [1]. Decomposition ; Diffraction ; Phase (matter) ; Materials science ; Precession ; Action (physics) ; Scanning electron microscope ; Crystal (programming language) ; Crystallography ; Computer science ; Optics ; Physics ; Chemistry ; Condensed matter physics ; Composite material ; Quantum mechanics X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques ; Microstructure and mechanical properties
Authors: Xiao Rui-juan | Li Hong | Liquan Chen
Source: Acta Physica Sinica (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Development of new lithium battery materials by material genome initiative After the continuous research on the discovering new materials based on theoretical methods and material genome initiative, the high-throughput simulation platform is established. With this new research mode and platform, the screening, optimization and design of lithium battery materials are realized by using lithium migration properties as criteria. The attempt at introducing machine learning method into material design is also made. With the high-throughput bond-valence calculations, two coating materials for Li-rich cathode are found, the modified -Li3PS4 and a new layered oxysulfide as novel lithium superionic conductors are designed, and the relationship between the volume change of electrode during delithiation and the atomic structure is investigated. The application of the material genome method to the development of lithium battery materials provides the possibility to promote this new research and development model in other types of materials. Materials science ; Battery (electricity) ; Coating ; Lithium (medication) ; Lithium battery ; Cathode ; Nanotechnology ; Material Design ; Fast ion conductor ; Computer science ; Valence (chemistry) ; Electrode ; Composite material ; Electrolyte ; Physical chemistry ; Thermodynamics ; Chemistry ; Physics Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography
Authors: Anurag Pramanik | Sanjeev Garg
Source: Drug Delivery and Translational Research (article, 2018-04-26)
Field: Chemistry — Spectroscopy

Prediction of the partition coefficients using QSPR modeling and simulation of paclitaxel release from the diffusion-controlled drug delivery devices Partition coefficient ; Quantitative structure–activity relationship ; Computer science ; Biological system ; Drug delivery ; Drug ; Paclitaxel ; Notation ; Chemistry ; Materials science ; Nanotechnology ; Mathematics ; Chromatography ; Pharmacology ; Machine learning Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Cancer Treatment and Pharmacology
Authors: Nicholas Rosa | Marko Ristić | B. Marshall | Janet Newman
Source: Acta Crystallographica Section F Structural Biology Communications (article, 2018-06-26)
Field: Materials Science — Materials Chemistry

<i>Cinder</i>: keeping crystallographers app-y The process of producing suitable crystals for X-ray diffraction analysis most often involves the setting up of hundreds (or thousands) of individual crystallization trials, each of which must be repeatedly examined for crystals or hints of crystallinity. Currently, the only real way to address this bottleneck is to use an automated imager to capture images of the trials. However, the images still need to be assessed for crystals or other outcomes. Ideally, there would exist some rapid and reliable machine-analysis tool to translate the images into a quantitative result. However, as yet no such tool exists in wide usage, despite this being a well recognized problem. One of the issues in creating robust automatic image-analysis software is the lack of reliable data for training machine-learning algorithms. Here, a mobile application, Cinder, has been developed which allows crystallization images to be scored quickly on a smartphone or tablet. The Cinder scores are inserted into the appropriate table in a crystallization database and are immediately available to the user through a more sophisticated web interface, allowing more detailed analyses. A sharp increase in the number of scored images was observed after Cinder was released, which in turn provides more data for training machine-learning tools. Cinder ; Computer science ; Bottleneck ; Software ; Process (computing) ; Pleiades ; Artificial intelligence ; Machine learning ; Computer vision ; Operating system ; Embedded system ; Engineering Enzyme Structure and Function ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Ankita Mangal | Elizabeth A. Holm
Source: Data in Brief (article, 2018-06-28)
Field: Materials Science — Materials Chemistry

A dataset of synthetic face centered cubic 3D polycrystalline microstructures, grain-wise microstructural descriptors and grain averaged stress fields under uniaxial tensile deformation This data article presents a data set comprised of 36 synthetic 3D equiaxed polycrystalline microstructures, the microstructural descriptors for each grain, and the stress and strain fields resulting from crystal plasticity simulations mimicking uniaxial tensile deformation to a total strain of 4%. This is related to the research article entitled “Applied Machine Learning to predict stress hotspots I: Face Centered Cubic Materials” (Mangal and Holm, 2018) [1]. The microstructures were created using an open source Dream.3D software tool, and the crystal plasticity simulations were carried out using the elasto-viscoplastic fast Fourier transform (EVPFFT) method. Six different kinds of FCC textures are represented with six stochastically different microstructures with varying texture intensity for each texture kind. This dataset is freely available in a Mendeley Data archive “A dataset of synthetic face centered cubic 3D polycrystalline microstructures, grain-wise microstructural descriptors and grain averaged stress fields under uniaxial tensile deformation” located at 〈http://dx.doi.org/10.17632/ss75fdg5dg.1〉 for any academic, educational, or research purposes. Equiaxed crystals ; Materials science ; Microstructure ; Crystallite ; Texture (cosmology) ; Cubic crystal system ; Deformation (meteorology) ; Ultimate tensile strength ; Stress (linguistics) ; Composite material ; Grain boundary ; Metallurgy ; Crystallography ; Artificial intelligence ; Computer science Microstructure and mechanical properties ; Composite Material Mechanics ; Advanced ceramic materials synthesis
Authors: Sabine M. Neumayer | Liam Collins | Rama K. Vasudevan | Christopher R. Smith | Suhas Somnath | V. Ya. Shur | Stephen Jesse | Andréi L. Kholkin | Sergei V. Kalinin | Brian J. Rodriguez
Source: ACS Applied Materials & Interfaces (article, 2018-11-20)
Field: Materials Science — Materials Chemistry

Decoupling Mesoscale Functional Response in PLZT across the Ferroelectric–Relaxor Phase Transition with Contact Kelvin Probe Force Microscopy and Machine Learning Relaxor ferroelectrics exhibit a range of interesting material behavior, including high electromechanical response, polarization rotations, as well as temperature and electric field-driven phase transitions. The origin of this unusual functional behavior remains elusive due to limited knowledge on polarization dynamics at the nanoscale. Piezoresponse force microscopy and associated switching spectroscopy provide access to local electromechanical properties on the micro- and nanoscale, which can help to address some of these gaps in our knowledge. However, these techniques are inherently prone to artefacts caused by signal contributions emanating from electrostatic interactions between tip and sample. Understanding functional behavior of complex, disordered systems like relaxor materials with unknown electromechanical properties therefore requires a technique that allows distinguishing between electromechanical and electrostatic response. Here, contact Kelvin probe force microscopy (cKPFM) is used to gain insight into the evolution of local electromechanical and capacitive properties of a representative relaxor material lead lanthanum zirconate across the phase transition from a ferroelectric to relaxor state. The obtained multidimensional data set was processed using an unsupervised machine learning algorithm to detect variations in functional response across the probed area and temperature range. Further analysis showed the formation of two separate cKPFM response bands below 50 °C, providing evidence for polarization switching. At higher temperatures only one band is observed, indicating an electrostatic origin of the measured response. In addition, the junction potential difference, which was extracted from the cKPFM data, becomes independent of the temperature in the relaxor state. The combination of this multidimensional voltage spectroscopy technique and machine learning allows to identify the origin of the measured functional response and to decouple ferroelectric from electrostatic phenomena necessary to understand the functional behavior of complex, disordered systems like relaxor materials. Materials science ; Piezoresponse force microscopy ; Kelvin probe force microscope ; Ferroelectricity ; Phase transition ; Condensed matter physics ; Polarization (electrochemistry) ; Nanoscopic scale ; Relaxor ferroelectric ; Scanning probe microscopy ; Nanotechnology ; Dielectric ; Optoelectronics ; Atomic force microscopy ; Physics Ferroelectric and Piezoelectric Materials ; Force Microscopy Techniques and Applications ; Acoustic Wave Resonator Technologies
Authors: Bing Huang | Nadine O. Symonds | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2018-07-11)
Field: Materials Science — Materials Chemistry

The fundamentals of quantum machine learning Within the past few years, we have witnessed the rising of quantum machine learning (QML) models which infer electronic properties of molecules and materials, rather than solving approximations to the electronic Schrodinger equation. The increasing availability of large quantum mechanics reference data sets have enabled these developments. We review the basic theories and key ingredients of popular QML models such as choice of regressor, data of varying trustworthiness, the role of the representation, and the effect of training set selection. Throughout we emphasize the indispensable role of learning curves when it comes to the comparative assessment of different QML models. Trustworthiness ; Representation (politics) ; Key (lock) ; Set (abstract data type) ; Quantum ; Artificial intelligence ; Computer science ; Quantum machine learning ; Machine learning ; Schrödinger equation ; Schrödinger's cat ; Selection (genetic algorithm) ; Theoretical computer science ; Mathematics ; Quantum mechanics ; Quantum computer ; Physics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Enrique S. Pariente | John C. Cancilla | Kacper Wierzchoś | José S. Torrecilla
Source: Postharvest Biology and Technology (article, 2018-03-21)
Field: Chemistry — Analytical Chemistry

On-site images taken and processed to classify olives according to quality – The foundation of a high-grade olive oil Olive oil ; Artificial neural network ; Quality (philosophy) ; Artificial intelligence ; Set (abstract data type) ; Product (mathematics) ; Computer science ; Pattern recognition (psychology) ; Mathematics ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Yuta Suzuki | Hideitsu Hino | Tetsuro Ueno | Yasuo Takeichi | Masato Kotsugi | Kanta Ono
Source: Microscopy and Microanalysis (article, 2018-08-01)
Field: Materials Science — Materials Chemistry

Extraction of Physical Parameters from X-ray Spectromicroscopy Data Using Machine Learning Materials informatics has significantly accelerated the discovery and analysis of materials in the last decade. Spectroscopic data provide essential information about materials and hence are widely used for materials analysis. However, data analysis, that is, the extraction of physical parameters, of spectra is often conducted by manually comparing spectra and on-the-fly data analysis has not been realized yet. Considering that more than 100,000 X-ray absorption spectra (XAS) can be measured per day using the scanning transmission X-ray microscopy system at the Photon Factory, an automated analysis methodology is urgently required. If physical parameters are to be estimated from the spectra, the on-the-fly analysis can be realized by space mapping of these parameters using a high-throughput spectromicroscopy experiment capable of adaptive measurements. XAS often shows complex spectral features with a few hundred or more “high-dimensional” data points, and the physical parameters, such as element, charge, and symmetry can be extracted from the XAS. Feature extraction, a popular machine learning approach to treat high-dimensional datasets, involves the projection of data onto a few features (parameters), thus retaining the relevant information. In this study, we propose a methodology to estimate the physical parameters from XAS using feature extraction with dimensionality reduction. Extraction (chemistry) ; Data extraction ; Computer science ; Content (measure theory) ; Materials science ; Chromatography ; Chemistry ; Mathematics Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; X-ray Diffraction in Crystallography
Authors: Célio Fernando Lipinski | Aline A. Oliveira | Káthia M. Honório | Patrícia R. Oliveira | Albérico B. F. da Silva
Source: Structural Chemistry (article, 2018-02-22)
Field: Chemistry — Organic Chemistry

A molecular modeling study of combretastatin-like chalcones as anticancer agents using PLS, ANN and consensus models Chemistry ; Quantitative structure–activity relationship ; Combretastatin ; Test set ; Partial least squares regression ; Biological system ; Computational biology ; Stereochemistry ; Tubulin ; Machine learning ; Computer science ; Microtubule Synthesis and biological activity ; Computational Drug Discovery Methods ; Multicomponent Synthesis of Heterocycles
Authors: Anton O. Oliynyk | Michael W. Gaultois | Martin Hermus | Andrew J. Morris | Arthur Mar | Jakoah Brgoch
Source: Inorganic Chemistry (article, 2018-06-21)
Field: Materials Science — Materials Chemistry

Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf−In System There remain 21 systems (out of over 3500 possible combinations of the elements) in which the existence of the simple binary equiatomic phases AB has not been established experimentally. Among these, the presumed binary phase HfIn is predicted to adopt the tetragonal CuAu-type structure (space group P4/ mmm) by a recently developed machine-learning model and by structure optimization through global energy minimization. To test this prediction, the Hf-In system was investigated experimentally by reacting the elements in a 1:1 stoichiometry at 1070 K. Under the conditions investigated, the bulk and surface of the sample correspond to different crystalline phases but have nearly the same equiatomic composition, as revealed by energy-dispersive X-ray analysis. The structure of the bulk sample, which was solved from powder X-ray diffraction data through simulated annealing, corresponds to the γ-brass (Cu<sub>5</sub>Zn<sub>8</sub>) type (space group I4̅3 m) with Hf and In atoms disordered over four sites. The structure of crystals selected from the surface, which was solved using single-crystal X-ray diffraction data, corresponds to the CuPt<sub>7</sub> type (space group Fm3̅ m) with Hf and In atoms partially disordered over three sites. The discrepancy between the predicted CuAu-type structure and the two experimentally refined crystal structures is reconciled through close inspection of structural relationships, which reveal that the γ-brass-type structure of the bulk HfIn phase is indeed derived through small distortions and defect formation within the CuAu-type structure. Chemistry ; Tetragonal crystal system ; Crystallography ; Crystal structure ; Diffraction ; Binary number ; Brass ; Type (biology) ; Stoichiometry ; Powder diffraction ; Annealing (glass) ; Energy minimization ; Group (periodic table) ; Copper ; Computational chemistry ; Materials science ; Metallurgy ; Physics ; Physical chemistry ; Optics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Copper Interconnects and Reliability