jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhijin Chen | Albert C. Aragonès | Ismael Díez‐Pérez | Yosuke Tani | K. Yamashita | Ryo Yamada | Hirokazu Tada | Takuji Ogawa
Source: The Japan Society of Applied Physics (article, 2019-07-10)
Field: Materials Science — Materials Chemistry

Measurement of Single Molecule Rectifying Behavior of Perpendicularly Connected Porphyrin-imide by Blinking Approach and Data Analysis by Machine Learning Porphyrin ; Imide ; Materials science ; Perpendicular ; Molecule ; Nanotechnology ; Photochemistry ; Polymer chemistry ; Organic chemistry ; Chemistry ; Geometry ; Mathematics Porphyrin and Phthalocyanine Chemistry ; Molecular Junctions and Nanostructures ; Surface Chemistry and Catalysis
Authors: Masahiko Demura | Junya Sakurai | Masayoshi Yamazaki | Junya Inoue
Source: International Conference on Computational & Experimental Engineering and Sciences (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Creep Rupture Time for Steels Creep is a complicated and time-dependent phenomenon, which is affected by the initial state and the degradation of microstructures. It is thus considered that the information about the microstructure is essential to predict the creep rupture time. On the other hand, there is a strong, practical need for the prediction without the investigation of microstructures nor the disclosure of the detailed process that should control the initial microstructures. In this study, we examined how modern machine learning technique can help to predict the creep rupture time in heat-resistant ferrite-type steels without the direct information about the microstructures and the process by using the high-quality experimental data generated in NIMS. The descriptors were here chemical composition expressed by each mass percent of 20 elements including Fe and test conditions such as creep stress and temperature. We used XGBoost for the machine learning model, one of the cutting-edge ensemble tree regression models, together with Tree Parzen estimator algorithm for tuning hyperparameters. <br/> The result demonstrated that the learned model provided a reasonable prediction. The root mean square error was 0.485 in natural logarithm of creep rupture time between the predicted and the experimental values for the test data that were not used for the training. This means that the creep rupture time can be predicted with an error within 1.7 (e^0.485) times. Note that we used only the chemical composition and the test conditions to build the model. It indicates that the information about the microstructures could be indirectly included in those descriptors. In the presentation, we will discuss on this point. <br/> This work was supported by Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), “Structural Materials for Innovation” and “Materials Integration for Revolutionary Design System of Structural Materials” (Funding agency: JST). Creep ; Microstructure ; Materials science ; Computer science ; Composite material Material Properties and Failure Mechanisms ; Metallurgy and Material Forming ; Engineering Diagnostics and Reliability
Authors: Jesús G. Estrada
Source:  (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Part I: On The Effects of Electron-Deficient Olefins in a Ni Catalyzed Csp3–Csp3 Cross Coupling Part II: Using Machine Learning to Predict Reaction Performance and Infer Mechanism in a Pd Catalyzed C–N Coupling Catalysis ; Mechanism (biology) ; Coupling (piping) ; Reaction mechanism ; Chemistry ; Computer science ; Photochemistry ; Combinatorial chemistry ; Physics ; Materials science ; Organic chemistry ; Metallurgy Machine Learning in Materials Science
Authors: Filippo Cellini | Francesco Lavini | Claire Berger | Walt de Heer | Elisa Riedo
Source: arXiv (Cornell University) (preprint, 2019-01-25)
Field: Materials Science — Materials Chemistry

Layer dependence of graphene-diamene phase transition in epitaxial and\n exfoliated few-layer graphene using machine learning The study of the nanomechanics of graphene $-$ and other 2D materials $-$ has\nled to the discovery of exciting new properties in 2D crystals, such as their\nremarkable in-plane stiffness and out of plane flexibility, as well as their\nunique frictional and wear properties at the nanoscale. Recently, nanomechanics\nof graphene has generated renovated interest for new findings on the\npressure-induced chemical transformation of a few-layer thick epitaxial\ngraphene into a new ultra-hard carbon phase, named diamene. In this work, by\nmeans of a machine learning technique, we provide a fast and efficient tool for\nidentification of graphene domains (areas with a defined number of layers) in\nepitaxial and exfoliated films, by combining data from Atomic Force Microscopy\n(AFM) topography and friction force microscopy (FFM). Through the analysis of\nthe number of graphene layers and detailed \\r{A}-indentation experiments, we\ndemonstrate that the formation of ultra-stiff diamene is exclusively found in\n1-layer plus buffer layer epitaxial graphene on silicon carbide (SiC) and that\nan ultra-stiff phase is not observed in neither thicker epitaxial graphene\n(2-layer or more) nor exfoliated graphene films of any thickness on silicon\noxide (SiO$_{2}$).\n Graphene ; Materials science ; Nanomechanics ; Nanotechnology ; Epitaxy ; Layer (electronics) ; Graphene nanoribbons ; Silicon carbide ; Nanoscopic scale ; Nanoindentation ; Phase (matter) ; Composite material ; Optoelectronics ; Atomic force microscopy ; Chemistry Graphene research and applications ; Force Microscopy Techniques and Applications ; Diamond and Carbon-based Materials Research
Authors: Pavao Santak | G. J. Conduit
Source: Apollo (University of Cambridge) (article, 2019-08-06)
Field: Materials Science — Materials Chemistry

Predicting physical properties of alkanes with neural networks Boiling point ; Artificial neural network ; Physical property ; Heat capacity ; Alkane ; Vapor pressure ; Exploit ; Property (philosophy) ; Viscosity ; Computer science ; Thermodynamics ; Statistical physics ; Biological system ; Artificial intelligence ; Chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Oliver Strickson | Nikos Nikiforakis | Emilio Artacho
Source: Physical Review Research (article, 2019-12-24)
Field: Materials Science — Materials Chemistry

Dynamical continuum simulation of condensed matter from first principles Macro scale continuum dynamics of condensed matter depends on properties of matter defined at the atomic scale. The authors propose a machine learning algorithm to steer the ab initio molecular dynamics simulations needed to feed continuum simulations from first principles. Molecular dynamics ; Atomic units ; Scale (ratio) ; Ab initio ; Dynamics (music) ; Formalism (music) Nonlocal and gradient elasticity in micro/nano structures ; Elasticity and Material Modeling ; Thermoelastic and Magnetoelastic Phenomena
Authors: Anna Elizabeth Buzolits
Source: eScholarship (California Digital Library) (article, 2019-01-01)
Field: Materials Science — Biomaterials

The Effect of Grain Size on Deformation Twinning in Magnesium There is great interest in magnesium and magnesium alloys for use as structural materials due to their high strength-to-weight ratio compared to aluminum and steel. However, the relationship between the macroscopic failure behavior of magnesium and the micro- and mesoscopic deformation mechanisms in magnesium are relatively poorly understood. Basal slip is ubiquitous in magnesium deformation, but the factors that determine the secondary deformation mechanisms, non-basal slip and twinning, are less well studied. For this reason, the aim of this study was to identify the relationship between twin occurrence and grain size. Towards this goal, a combination of electron backscatter diffraction (EBSD), scanning electron microscope digital image correlation (SEM-DIC) were used to capture full-field strain maps of a ~1mm x 1mm field of view on a pure magnesium dogbone sample under monotonic compression. It was found that the likelihood that a grain exhibits twinning increases as the size of the grain increases. K-means clustering, filtered cross-correlation, k-means clustering, and an AlexNet convolutional neural network (CNN) were also used to attempt to automate the twin identification process. Though the results of this work exhibited the same trend (increased twin likelihood with increased grain size), the precision, sensitivity, and accuracy of these methods was analyzed and it was found that more work needs to be done before significant conclusions can be drawn from their results. Crystal twinning ; Electron backscatter diffraction ; Materials science ; Magnesium ; Grain size ; Slip (aerodynamics) ; Digital image correlation ; Deformation mechanism ; Metallurgy ; Composite material ; Microstructure ; Physics ; Thermodynamics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Microstructure and mechanical properties
Authors: Elena Uteva
Source: Repository@Nottingham (University of Nottingham) (article, 2019-07-17)
Field: Materials Science — Materials Chemistry

Gaussian process machine learning of potential energy hypersurfaces Born-Oppenheimer potential energy surfaces (PES) are a central concept in computational chemistry. Extensive research has been geared towards developing novel methods of generating accurate but computationally feasible, global potential energy surfaces. Recent approaches have stepped away from the use of analytical methods and have instead honed in on the application of machine learning techniques. These methods primarily focus on just mapping the input-output space of a training data set as accurately as possible, without delving too deeply into the chemistry of the system studied. One such machine learning technique is Gaussian Processes regression - a non-parametric learning tool that has shown significant promise in being able to predict the intermolecular energies of a variety of systems to an arbitrary level of accuracy. This method is adopted in our research, producing high accuracy global PES for several van der Waals bimolecular complexes at a fraction of the usual computational cost, when using accurate training data. The research also introduces several algorithms for sequential design, where the model selects training data points iteratively. Points are chosen based on information learnt from the previous training step. The sequential design results are pitted against ‘one shot’ space-filling designs (where training data are just chosen in bulk on the basis of the best coverage of space). Two of the sequential design methods significantly outperform the space filling sets in terms of predictive accuracy. Global potential energy surfaces are produced for CO2-H2, CO2 – Ne, CO2 – CO, HF-HF, CH4 – N2 and Ar3 but the algorithm can be easily applied to other molecular interactions with a only few trivial modifications. Gaussian process ; Data point ; Energy (signal processing) ; Set (abstract data type) ; Variety (cybernetics) ; Process (computing) ; van der Waals force ; Kriging ; Gaussian ; Basis (linear algebra) Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Chemical Physics Studies
Authors: Forest Agostinelli
Source: eScholarship (California Digital Library) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Deep Learning for Puzzles and Circadian Rhythms The combination of deep learning with reinforcement learning and the application of deep learning to the sciences is a relatively new and flourishing field. We show how deep reinforcement learning techniques can learn to solve problems, often in the most efficient way possible, when faced with many possibilities but little information by designing an algorithm that can learn to solve seven different combinatorial puzzles, including the Rubik's cube. Furthermore, we show how deep learning can be applied to the field of circadian rhythms. Circadian rhythms are fundamental for all forms of life. Using deep learning, we can gain insight into circadian rhythms on the molecular level. Finally, we propose new deep learning algorithms that yield significant performance improvements on computer vision and high energy physics tasks. Artificial intelligence ; Reinforcement learning ; Flourishing ; Deep learning ; Computer science ; Circadian rhythm ; Field (mathematics) ; Rhythm ; Machine learning ; Mathematics ; Psychology ; Neuroscience ; Physics Machine Learning in Materials Science ; Economic and Technological Innovation ; Astronomy and Astrophysical Research
Authors: Giuseppe D’Alessio | Gianmarco Aversano | Alberto Cuoci | Alessandro Parente
Source: Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (article, 2019-05-07)
Field: Chemistry — Physical and Theoretical Chemistry

Effects of the training dataset and data preprocessing on adaptive chemistry simulations Machine learning provides the tools for the mitigation of the high computational costs associated with the numerical simulations of reacting flows with detailed kinetic schemes, while preserving high levels of accuracy. By means of unsupervised learning, the composition space is partitioned into clusters where the full chem- ical mechanism is reduced by Directed Relation Graphs with Error Propagation. Therefore, at each time-step of the numerical simu- lation, all grid points are classified and different reduced schemes are applied locally. Several unsupervised learning techniques for multivariate data partitioning exist in literature, such as Local Principal Component Analysis, Self-Organizing Maps, k-Means, Auto-encoders. The effectiveness of these machine learning models depend on the training data quality and availability, and on how the multivariate dataset is preprocessed. Since a certain manifold in composition space needs to be learned, it is necessary to have sufficient available data coming either from the system of interest itself or from simpler systems that can be representative for it. This implies that also 0D or 1D data can be used to train a machine learning model for a 2D system, as long as the same manifold is represented. The aforementioned techniques and different preprocessing strategies were tested for an adaptive- chemistry simulation of a laminar co-flow methane flame, with the objective to assess the different models robustness and potential in terms of chemistry reduction and simulation reliability. Training (meteorology) ; Computer science ; Preprocessor ; Training set ; Chemistry ; Artificial intelligence ; Data mining ; Physics Various Chemistry Research Topics
Authors: Kamel Benyelloul | S. Bekhechi | Abdelkader Djellouli | Youcef Bouhadda
Source: AIP conference proceedings (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Formation enthalpy: A comparative study for datamining approach and first-principle calculations: Hydrogen storage The main and crucial fundamental thermodynamic properties are the enthalpy’s formation. It is know that the enthalpy of formation plays a keys role in energy balance calculation combustion properties, equilibrium constants of reactions and fuel characteristics of materials. Several methods have been carried out in calculation and estimating the enthalpy of formation, such a semi empirical model, Miedema model and density functional theory calculations. Currently, the datamining is the newly developed approach which attracts and increasing care of the material science researches. In the current paper, the one know datamining approach based on artificial neural network (predictive technique) has been proposed to estimate the enthalpy formation for several hydrides. An (3-13-1) architecture structure has been developed to predict the enthalpy of formation for rare-earth dihydrides CeH2, PrH2 and DyH2, Also, an (6-15-1) architecture of artificial neural network has been developed to estimate the enthalpy of formation, for the 39 ternary hydrides selected from different published works. The performance indices such a relative error, coefficient of determination and mean square error were used to control the performance of obtained results. The obtained results for enthalpy of formation for CeH2, PrH2 and DyH2 are basically consistent with the density functional theory and are in good agreement with experimental data. In other hand, the obtained results for 39 ternary hydrides are also in good agreement with experimental data available in the literature. Enthalpy ; Thermodynamics ; Ternary operation ; Standard enthalpy of formation ; Density functional theory ; Hydrogen storage ; Standard enthalpy change of formation ; Artificial neural network ; Materials science ; Chemistry ; Hydrogen ; Computer science ; Machine learning ; Computational chemistry ; Physics Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Jiayan Xu | Xiaoming Cao | Pengbo Hu
Source: Research Portal (Queen's University Belfast) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Improved Prediction for the Methane Activation Mechanism on Rutile Metal Oxides by a Machine Learning Model with Geometrical Descriptors Methane activation could occur via either the radical-like or the surface-stabilized mechanism on metal oxides. The linear Bronsted–Evans–Polanyi (BEP) relationship between activation energies and the adsorption energies of products has made it possible to swiftly predict some reaction mechanisms. However, it is not accurate enough to predict the preferential methane activation mechanism on metal oxides. Herein, to improve the prediction for the methane activation mechanism, the machine learning method percentile-LASSO was developed to extract energetic and geometrical descriptors on the basis of a series of surface-stabilized and radical-like transition states of methane activation on rutile-type metal oxides from density functional theory calculations. Revised relations are capable of classifying those two mechanisms on the same surface with a higher accuracy, which will facilitate high-throughput catalyst screening for methane activation on metal oxides. Chemistry ; Methane ; Rutile ; Metal ; Catalysis ; Mechanism (biology) ; Activation energy ; Reaction mechanism ; Adsorption ; Transition metal ; Oxide ; Computational chemistry ; Physical chemistry ; Chemical physics ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Alec Kaija
Source: D-Scholarship@Pitt (University of Pittsburgh) (article, 2019-09-10)
Field: Chemistry — Inorganic Chemistry

Porous Pseudomaterials for Studying Structure-PropertyRelationships of Gas Adsorption The discovery in 1995 of metal-organic frameworks (MOFs) – with record-breaking surface areas – sparked exponential growth in research efforts dedicated to the development of new porous adsorbents, particularly for energy related gas storage applications. However, despite their promise, decades of research have yet to yield MOFs that perform well enough for many of these applications, particularly high-pressure vehicular natural gas storage and post-combustion carbon capture. To understand why, I developed a novel computational methodology for generating large (100,000+) libraries of randomly configured Lennard-Jones (LJ) crystals, or “pseudomaterials”, with the intention of calculating various adsorption-related properties of interest en masse using grand canonical Monte Carlo (GCMC) simulations. These libraries were used to map an n-dimensional structure-property space, where n refers to the number different structure- and property-parameters. One approach for generating these libraries of pseudomaterials is random sampling, where each structure is generated algorithmically at random; however, we attempt to improve the overall computational efficiency using alternative methods. These alternative methods include mutation algorithms and augmenting random sampling with property prescreening. Mutation algorithms identify pseudomaterials with unique structure-property combinations and selectively perturb their structural characteristics to sample the sparsely populated regions of the structure-property space. Property prescreening uses machine learning models to predict the properties of a pseudomaterial to justify whether it is a candidate worthy of more computationally expensive GCMC simulations; it is an attempt at reducing the computational expense associated with running simulations on pseudomaterials with redundant properties. The overall goal of this new computational methodology was to observe structure-property relationships for porous materials in general (i.e., not limited to any particular sub-class). I showed that understanding these structure-property relationships provides insights into the design of better adsorbents for a wide range of gas storage and separations applications. In the future, this methodology could potentially be extended to better understanding porous materials for catalysis, sensing, and more. Property (philosophy) ; Computer science ; Sampling (signal processing) ; Monte Carlo method ; Chemical space ; Statistical physics ; Mathematics ; Chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Vibudh Agrawal
Source: cIRcle (University of British Columbia) (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

The discovery of small molecule inhibitors for TOX1 and ERG oncotargets with the development and use of progressive docking PD2.0 approach Drug discovery is a rigorous process that can cost up to 3 billion dollars and takes more than 10 years to bring new therapeutics from bench to bedside. While virtual screening (such as molecular docking) can significantly speed up the discovery process and improve hit rates, its speed already lags behind the rate of the explosive growth of publically available chemical databases which already exceed billions of entries. This recent surge of available chemical entities presents great opportunities for discovering novel classes of small molecule drugs but also brings a significant demand for faster docking methods. In the current thesis, we illustrated the need for a faster screening method by virtually screening 7.6 million molecules against Thymocyte selection-associated high mobility group box protein (TOX). Then we demonstrated that the deep learning-based method of ‘Progressive Docking (PD2.0)’ can speed up such virtual screening by up to hundred folds. In particular, by utilizing deep learning QSAR models trained on the docking scores of a subset of the database, one can approximate in an iterative manner the docking outcome of unprocessed entries. We tested the developed method against various targets including ETS transcription factor ERG, Estrogen Receptor Activation Function 2 (ERAF2), Androgen Receptor (AR), Estrogen Receptor (ER), Sodium-Ion Channel (Nav1.7) and many more. In this work, we identified 18 active compounds against TOX with micro-molar potency. We also used the PD2.0 method to dock up to 1.3 billion compounds from the ZINC15 database and demonstrated that this deep-learning-based approach resulted in 65X speed acceleration and 130X Full Predicted Database Enrichment (FPDE) while retaining more than 90% of good hits. We also demonstrate the method’s robustness by docking 570 million compounds from the ZINC15 database into 13 diverse drug targets including ERG. Docking (animal) ; Computer science ; Chemistry ; Medicine Organic Chemistry Cycloaddition Reactions ; Cancer Treatment and Pharmacology ; Lung Cancer Treatments and Mutations