jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu-Chung Chang | Yu-Chun Lu | Yu-Ju Hung
Source: Nanomaterials (article, 2019-03-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Controlling the Nanoscale Gaps on Silver Island Film for Efficient Surface-Enhanced Raman Spectroscopy We control the nanoscale gaps on silver island films by different processing methods and investigate the surface-enhanced Raman scattering (SERS) efficiency on the films. We propose a facile technique to control the film morphology by substrate bending while keeping the evaporation rate constant. The films developed by our new method are compared to the films developed by traditional methods at various evaporation rates. The SERS signals generated on the samples prepared by the new method have similar strengths as the traditional methods. Substrate bending allows us to reduce the gap sizes while using a higher evaporation rate, hence the film can be developed in a shorter time. This cost-effective and time-efficient method is suitable for the mass production of large-area SERS sensors with good sensitivity. Scanning electron microscope images are analyzed to quantify the gap densities and widths to elucidate the relationship between the film morphology and the SERS intensity. While the gap size appears to be the major factor influencing the enhancement, the shape of the nano-island also seems to influence the SERS efficiency. Materials science ; Nanoscopic scale ; Raman spectroscopy ; Substrate (aquarium) ; Raman scattering ; Evaporation ; Scanning electron microscope ; Nanotechnology ; Bending ; Surface-enhanced Raman spectroscopy ; Optoelectronics ; Optics ; Analytical Chemistry (journal) ; Composite material ; Chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Electrochemical Analysis and Applications
Authors: Benoît Colasson | Alberto Credi | Barbara Ventura
Source: Chemistry - A European Journal (article, 2019-10-22)
Field: Chemistry — Organic Chemistry

Photoinduced Electron Transfer Involving a Naphthalimide Chromophore in Switchable and Flexible [2]Rotaxanes The interlocking of ring and axle molecular components in rotaxanes provides a way to combine chromophoric, electron-donor and electron-acceptor moieties in the same molecular entity, in order to reproduce the features of photosynthetic reaction centers. To this aim, the photoinduced electron transfer processes involving a 1,8-naphthalimide chromophore, embedded in several rotaxane-based dyads, were investigated by steady-state and time-resolved absorption and luminescence spectroscopic experiments in the 300 fs-10 ns time window. Different rotaxanes built around the dialkylammonium/ dibenzo[24]crown-8 ether supramolecular motif were designed and synthesized to decipher the relevance of key structural factors, such as the chemical deactivation of the ammonium-crown ether recognition, the presence of a secondary site for the ring along the axle, and the covalent functionalization of the macrocycle with a phenothiazine electron donor. Indeed, the conformational freedom of these compounds gives rise to a rich dynamic behavior induced by light and may provide opportunities for investigating and understanding phenomena that take place in complex (bio)molecular architectures. Rotaxane ; Chromophore ; Crown ether ; Photoinduced electron transfer ; Photochemistry ; Supramolecular chemistry ; Chemistry ; Electron transfer ; Molecular switch ; Covalent bond ; Supramolecular chirality ; Molecule ; Organic chemistry Supramolecular Chemistry and Complexes ; Porphyrin and Phthalocyanine Chemistry ; Luminescence and Fluorescent Materials
Authors: Yongda Huang | Meiqian Chen | Q. H. Li
Source: Journal of Thermal Analysis and Calorimetry (article, 2019-04-24)
Field: Materials Science — Materials Chemistry

Artificial neural network model for the evaluation of chemical kinetics in thermally induced solid-state reaction Kinetics ; Kinetic energy ; Artificial neural network ; Activation energy ; Chemical kinetics ; Biological system ; Thermodynamics ; Reaction rate ; Materials science ; Chemistry ; Computer science ; Artificial intelligence ; Physical chemistry ; Physics ; Organic chemistry Thermal and Kinetic Analysis ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Zhilei Wang | Toshio Ogawa | Yoshitaka Adachi
Source: Advanced Theory and Simulations (article, 2019-11-18)
Field: Materials Science — Materials Chemistry

A Machine Learning Tool for Materials Informatics Abstract In response to the increasing demand for the highly efficient design of materials, materials informatics has been proposed for using data and computational sciences to extract data features that provide insight into how properties track with microstructure variables. However, the general metrics of microstructural features often ignore the complexities of the microstructure geometry for many properties of interest. An independently developed machine learning tool called shiny materials genome integration system for phase and property analysis (ShinyMIPHA), which is designed with either standalone software or cloud system based on an R programing package of “Shiny”, is introduced. ShinyMIPHA provides topological microstructure analysis methods based on image processing technology by employing a two‐point correlation function, persistent homology, and mean ( H )–Gauss ( K ) curvature approaches, as well as sparse study and regression analysis methods that enable a data‐driven properties‐to‐microstructure‐to‐processing inverse materials‐design approach. The demo version is available at https://adachi-lab.shinyapps.io/demo/ . Computer science ; Software ; Cloud computing ; Topological data analysis ; Informatics ; Computational science ; Persistent homology ; Materials informatics ; Point cloud ; Image processing ; Machine learning ; Data mining ; Artificial intelligence ; Algorithm ; Health informatics ; Image (mathematics) ; Programming language ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topological and Geometric Data Analysis
Authors: Bhavya Kailkhura | Brian Gallagher | Sookyung Kim | Anna M. Hiszpanski | T. Yong-Jin Han
Source: npj Computational Materials (preprint, 2019-11-14)
Field: Materials Science — Materials Chemistry

Reliable and explainable machine-learning methods for accelerated material discovery Abstract Despite ML’s impressive performance in commercial applications, several unique challenges exist when applying ML in materials science applications. In such a context, the contributions of this work are twofold. First, we identify common pitfalls of existing ML techniques when learning from underrepresented/imbalanced material data. Specifically, we show that with imbalanced data, standard methods for assessing quality of ML models break down and lead to misleading conclusions. Furthermore, we find that the model’s own confidence score cannot be trusted and model introspection methods (using simpler models) do not help as they result in loss of predictive performance (reliability-explainability trade-off). Second, to overcome these challenges, we propose a general-purpose explainable and reliable machine-learning framework. Specifically, we propose a generic pipeline that employs an ensemble of simpler models to reliably predict material properties. We also propose a transfer learning technique and show that the performance loss due to models’ simplicity can be overcome by exploiting correlations among different material properties. A new evaluation metric and a trust score to better quantify the confidence in the predictions are also proposed. To improve the interpretability, we add a rationale generator component to our framework which provides both model-level and decision-level explanations. Finally, we demonstrate the versatility of our technique on two applications: (1) predicting properties of crystalline compounds and (2) identifying potentially stable solar cell materials. We also point to some outstanding issues yet to be resolved for a successful application of ML in material science. Interpretability ; Computer science ; Machine learning ; Artificial intelligence ; Context (archaeology) ; Metric (unit) ; Pipeline (software) ; Reliability (semiconductor) ; Quality (philosophy) ; Data mining Machine Learning in Materials Science ; Software Engineering Research ; Imbalanced Data Classification Techniques
Authors: Sukeharu Nomoto | Hiroshi Wakameda | Masahito Segawa | Akinori Yamanaka | Toshiyuki Koyama
Source: Modelling and Simulation in Materials Science and Engineering (article, 2019-07-18)
Field: Materials Science — Materials Chemistry

Solidification analysis by non-equilibrium phase field model using thermodynamics data estimated by machine learning Abstract A multi-phase field method (MPFM) using the finite interface dissipation model is applied to simulate the solidification microstructure evolution of a stainless-steel composition, including the delta-ferrite to gamma austenite peritectic transformation. The calculation is performed for a quinary system of engineering steel in a two-dimensional field. Thermodynamics calculations using the CALPHAD database in this MPFM are replaced by machine learning prediction to reduce the numerical time. Neural network methodology is introduced for machine learning in this study. The Gibbs free energy and chemical potential values estimated from the CALPHAD database coupling results are inputted into the neural network learning procedure, together with the composition and temperature values. The microstructure evaluated using the obtained neural network parameter is in good agreement with that directly coupled with the CALPHAD database. This calculation is approximately five times faster than direct CALPHAD calculation. CALPHAD ; Materials science ; Austenite ; Thermodynamics ; Quinary ; Microstructure ; Gibbs free energy ; Artificial neural network ; Ferrite (magnet) ; Phase (matter) ; Dissipation ; Phase diagram ; Machine learning ; Alloy ; Metallurgy ; Computer science ; Physics Solidification and crystal growth phenomena ; Metallurgical Processes and Thermodynamics ; Microstructure and Mechanical Properties of Steels
Authors: Luiza De Lazari Ferreira | Felipe Medeiros | Bárbara Caroline Rodrigues de Araujo | Milton S. Gomes | Maria Luiza M. Rocco | Rita C. O. Sebastião | Hállen D. R. Calado
Source: Thermochimica Acta (article, 2019-04-02)
Field: Materials Science — Materials Chemistry

Kinetic study of MWCNT and MWCNT@P3HT hybrid thermal decomposition under isothermal and non-isothermal conditions using the artificial neural network and isoconversional methods Thermal stability ; Chemical engineering ; Isothermal process ; Thermal decomposition ; Materials science ; Activation energy ; Polymer ; Carbon nanotube ; Reagent ; Polymerization ; Polymer chemistry ; Chemistry ; Nanotechnology ; Organic chemistry ; Composite material ; Thermodynamics Thermal and Kinetic Analysis ; Carbon Nanotubes in Composites ; Polymer Nanocomposites and Properties
Authors: T. Mazaheri | Bo Sun | J. Scher-Zagier | A. S. Thind | D. Magee | P. Ronhovde | T. Lookman | R. Mishra | Z. Nussinov
Source: Physical Review Materials (article, 2019-06-19)
Field: Materials Science — Materials Chemistry

Stochastic replica voting machine prediction of stable cubic and double perovskite materials and binary alloys A machine-learning approach that we term the ``stochastic replica voting machine'' (SRVM) algorithm is presented and applied to a binary and a three-class classification problem in materials science. Here, we employ SRVM to predict candidate compounds capable of forming stable perovskites and double perovskites and further classify binary ($AB$) solids. The results of our binary and ternary classifications compared well to those obtained by SVM and neural network algorithms. Replica ; Binary number ; Ternary operation ; Perovskite (structure) ; Artificial neural network ; Term (time) ; Support vector machine Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; X-ray Diffraction in Crystallography
Authors: Yintao Song | Nobumichi Tamura | Chenbo Zhang | Mostafa Karami | Xian Chen
Source: Acta Crystallographica Section A Foundations and Advances (article, 2019-10-29)
Field: Materials Science — Materials Chemistry

Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis A novel data-driven approach is proposed for analyzing synchrotron Laue X-ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. It is demonstrated through typical examples including polycrystalline BaTiO<sub>3</sub>, multiphase transforming alloys and finely twinned martensite. The computational pipeline is implemented for beamline 12.3.2 at the Advanced Light Source, Lawrence Berkeley National Laboratory. The conventional analytical pathway for X-ray diffraction scans is based on a slow pattern-by-pattern crystal indexing process. This work provides a new way for analyzing X-ray diffraction 2D patterns, independent of the indexing process, and motivates further studies of X-ray diffraction patterns from the machine learning perspective for the development of suitable feature extraction, clustering and labeling algorithms. Beamline ; Diffraction ; Synchrotron ; Pipeline (software) ; Synchrotron radiation ; X-ray crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray Imaging Techniques
Authors: Federico Bianchini | Aldo Glielmo | James R. Kermode | Alessandro De Vita
Source: Physical Review Materials (article, 2019-04-16)
Field: Materials Science — Materials Chemistry

Enabling QM-accurate simulation of dislocation motion in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>γ</mml:mi><mml:mtext>−</mml:mtext><mml:mi>Ni</mml:mi></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>α</mml:mi><mml:mtext>−</mml:mtext><mml:mi>Fe</mml:mi></mml:mrow></mml:math> using a hybrid multiscale approach We present an extension of the `learn on the fly' method to the study of the motion of dislocations in metallic systems, developed with the aim of producing information-efficient force models that can be systematically validated against reference QM calculations. Nye tensor analysis is used to dynamically track the quantum region centered at the core of a dislocation, thus enabling quantum mechanics/molecular mechanics simulations. The technique is used to study the motion of screw dislocations in Ni-Al systems, relevant to plastic deformation in Ni-based alloys, at a variety of temperature/strain conditions. These simulations reveal only a moderate spacing ($\ensuremath{\sim}5\phantom{\rule{0.16em}{0ex}}\AA{}$) between Shockley partial dislocations, at variance with the predictions of traditional molecular dynamics (MD) simulation using interatomic potentials, which yields a much larger spacing in the high stress regime. The discrepancy can be rationalized in terms of the elastic properties of an hcp crystal, which influence the behavior of the stacking fault region between Shockley partial dislocations. The transferability of this technique to more challenging systems is addressed, focusing on the expected accuracy of such calculations. The bcc $\ensuremath{\alpha}\text{\ensuremath{-}}\mathrm{Fe}$ phase is a prime example, as its magnetic properties at the open surfaces make it particularly challenging for embedding-based QM/MM techniques. Our tests reveal that high accuracy can still be obtained at the core of a dislocation, albeit at a significant computational cost for fully converged results. However, we find this cost can be reduced by using a machine learning approach to progressively reduce the rate of expensive QM calculations required during the dynamical simulations, as the size of the QM database increases. Materials science ; Stacking fault ; Dislocation ; Partial dislocations ; Interatomic potential ; Molecular dynamics ; Condensed matter physics ; Crystallography ; Physics ; Quantum mechanics ; Chemistry Microstructure and mechanical properties ; Machine Learning in Materials Science ; High Temperature Alloys and Creep
Authors: Thomas J. Struble | Connor W. Coley | Klavs F. Jensen
Source: ChemRxiv (preprint, 2019-08-28)
Field: Chemistry — Organic Chemistry

Multitask Prediction of Site Selectivity in Aromatic C-H Functionalization Reactions Aromatic C-H functionalization reactions are an important part of the synthetic chemistry toolbox. Accurate prediction of site selectivity can be crucial for prioritizing target compounds and synthetic routes in both drug discovery and process chemistry. However, selectivity may be highly dependent on subtle electronic and steric features of the substrate. We report a generalizable approach to prediction of site selectivity that is accomplished using a graph-convolutional neural network for the multitask prediction of 123 C-H functionalization tasks. In an 80/10/10 training/validation/testing pseudo-time split of about 58,000 aromatic C-H functionalization reactions from the Reaxys database, the model achieves a mean reciprocal rank of 92%. Once trained, inference requires approximately 200 ms per compound to provide quantitative likelihood scores for each task. This approach and model allow a chemist to quickly determine which C-H functionalization reactions-if any-might proceed with high selectivity. Surface modification ; Selectivity ; Combinatorial chemistry ; Toolbox ; Chemistry ; Steric effects ; Inference ; Computer science ; Machine learning ; Artificial intelligence ; Stereochemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science
Authors: Gota Kikugawa | Yuta Nishimura | Koji Shimoyama | Taku Ohara | Tomonaga Okabe | Fumio S. Ohuchi
Source: Chemical Physics Letters (article, 2019-04-29)
Field: Materials Science — Materials Chemistry

Data analysis of multi-dimensional thermophysical properties of liquid substances based on clustering approach of machine learning Cluster analysis ; Space (punctuation) ; Unsupervised learning ; Computer science ; Order (exchange) ; Artificial intelligence ; Data mining ; Machine learning Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Neural Networks and Applications
Authors: Ghanshyam Pilania | Xiang-Yang Liu | ‪Zhehui Wang
Source: Journal of Materials Science (article, 2019-02-26)
Field: Materials Science — Materials Chemistry

Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators Lanthanide ; Materials science ; Scintillator ; Property (philosophy) ; Solid mechanics ; Chemical engineering ; Composite material ; Organic chemistry ; Optics ; Physics ; Chemistry ; Ion Machine Learning in Materials Science ; Medical Imaging Techniques and Applications ; Radioactive element chemistry and processing
Authors: Siva Kumar Mahalingam | Venkatachalam Gopalan | Hemanth Velivela | Vignesh Pragasam | Prashanth | Vimalanand Suthenthiraveerappa
Source: Emerging Materials Research (article, 2019-12-10)
Field: Materials Science — Polymers and Plastics

Studies on Shear Strength of CNT/Coir Fibre/Fly Ash Reinforced Epoxy Polymer Composites The material requirements for present industries have led researchers to look for materials with high specific strength, stiffness and eco-friendliness. Research studies on natural-fibre-reinforced polymer matrix composite materials have increased to satisfy industry requirements. Reinforcement enhances the rigidness of composite materials, which is measured through shear modulus values. This work attempted to use carbon nanotube (CNT)/coir fibre/fly ash as reinforcements with epoxy. The experimental specimens were made by varying the contents of CNT/coir fibre/fly ash with epoxy by using the response surface methodology, one of design-of-experiment approaches. Analysis of variance was used to study the influence of the reinforcement percentage of CNT/coir fibre/fly ash on the shear modulus of the composite. Two models were obtained to predict the shear modulus for various reinforcement parameters using the experimental data. This was based on the regression equation meant for shear modulus. Of the two models, one was based on an artificial neural network and other was based on a multiple linear regression model. Optimum reinforcement parameters were obtained through a teaching–learning-based optimisation algorithm. A confirmation test was also carried out to determine the shear modulus experimentally for the reinforcement parameters obtained from the optimisation process. Materials science ; Composite material ; Epoxy ; Fly ash ; Reinforcement ; Coir ; Stiffness ; Composite number ; Shear strength (soil) ; Shear modulus ; Response surface methodology ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Innovative concrete reinforcement materials
Authors: Sheng Gong | Wei Wu | Fancy Qian Wang | Jie Liu | Yu Zhao | Yiheng Shen | Shuo Wang | Qiang Sun | Qian Wang
Source: Physical review. A/Physical review, A (article, 2019-02-08)
Field: Materials Science — Materials Chemistry

Classifying superheavy elements by machine learning Among the 118 elements listed in the periodic table, there are nine superheavy elements (Mt, Ds, Mc, Rg, Nh, Fl, Lv, Ts, and Og) that have not yet been well studied experimentally because of their limited half-lives and production rates. How to classify these elements for further study remains an open question. For superheavy elements, although relativistic quantum-mechanical calculations for the single atoms are more accurate and reliable than those for their molecules and crystals, there is no study reported to classify elements solely based on atomic properties. By using cutting-edge machine learning techniques, we find the relationship between atomic data and classification of elements, and further identify that Mt, Ds, Mc, Rg, Lv, Ts, and Og should be metals, while Nh and Fl should be metalloids. These findings not only highlight the significance of machine learning for superheavy atoms but also challenge the conventional belief that one can determine the characteristics of an element only by looking at its position in the table. Superheavy Elements ; Atomic theory ; Enhanced Data Rates for GSM Evolution ; Physics ; Atomic number ; Atomic physics ; Artificial intelligence ; Nuclear physics ; Computer science Machine Learning in Materials Science ; History and advancements in chemistry ; X-ray Diffraction in Crystallography