jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xun Liu | Lihao Yang | Zhufeng Hou | Bo Da | Kenji Nagata | Hideki Yoshikawa | Shigeo Tanuma | Yang Sun | Zejun Ding
Source: Physical Review Materials (article, 2021-03-24)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning approach for the prediction of electron inelastic mean free paths The prediction of electron inelastic mean free paths (IMFPs) from simple material parameters is a challenging problem in studies using electron spectroscopy and microscopy. Herein, we propose a machine learning (ML) approach to predict IMFPs from some basic material property data. The ML model showed excellent performance based on the calculated IMFPs for a group of 41 elemental materials [Li, Be, C (graphite), C (diamond), C (glassy), Na, Mg, Al, Si, K, Sc, Ti, V, Cr, Fe, Co, Ni, Cu, Ge, Y, Nb, Mo, Ru, Rh, Pd, Ag, In, Sn, Cs, Gd, Tb, Dy, Hf, Ta, W, Re, Os, Ir, Pt, Au, and Bi] from a previous paper [Shinotsuka et al., Surf. Interface Anal. 47, 871 (2015); Surf. Interface Anal. 47, 1132 (2015)]. which was comparable with that of the robust Tanuma-Powell-Penn (TPP-2M) formula. The developed ML model was then extended to materials that do not have reported IMFPs in the database. The IMFPs for 18 transition and lanthanide metals (Mn, Zn, Zr, Tc, Cd, La, Ce, Pr, Nd, Pm, Sm, Eu, Ho, Er, Tm, Yb, Lu, and Hg) were predicted by the ML model. In the comparison with full-Penn algorithm-calculated IMFPs through two newly found experimental energy loss functions (ELFs), i.e., Mn and Zr, the Gaussian process regression-predicted IMFPs not only agreed well with those calculated using the TPP-2M formula in the energy range &gt;50 eV but were also consistent with the trend of IMFPs calculated through experimental ELFs in the range of 2.7--50 eV, where the TPP-2M formula cannot be used. Our findings suggest that ML is very powerful and efficient and has great potential to complete a database of IMFPs for materials that can prove solutions closer to reality than empirical models on materials with similar physical and chemical properties and can be applied to other different situations for correlated information prediction. Materials science ; Inelastic mean free path ; X-ray photoelectron spectroscopy ; Range (aeronautics) ; Analytical Chemistry (journal) ; Electron ; Physics ; Chemistry ; Mean free path ; Nuclear magnetic resonance ; Composite material Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Zhisen Meng | Xiaosi Zhu | Péter Kovács | En‐Wei Liang | Zhao Wang
Source: arXiv (Cornell University) (article, 2021-10-07)
Field: Chemistry — Organic Chemistry

Machine Learning interpretation of the correlation between infrared emission features of interstellar polycyclic aromatic hydrocarbons Supervised machine learning models are trained with various molecular descriptors to predict infrared emission spectra of interstellar polycyclic aromatic hydrocarbons. We demonstrate that a feature importance analysis based on the random forest algorithm can be utilized to explore the physical correlation between emission features. Astronomical correlations between infrared bands are analyzed as examples of demonstration by finding the common molecular fragments responsible for different bands, which improves the current understanding of the long-observed correlations. We propose a way to quantify the band correlation by measuring the similarity of the feature importance arrays of different bands, via which a correlation map is obtained for emissions in the out-of-plane bending region. Moreover, a comparison between the predictions using different combinations of descriptors underscores the strong prediction power of the extended-connectivity molecular fingerprint, and shows that the combinations of multiple descriptors of other types in general lead to improved predictivity. Physics ; Infrared ; Astrochemistry ; Polycyclic aromatic hydrocarbon ; Interpretation (philosophy) ; Interstellar medium ; Astrophysics ; Astronomy ; Astrobiology ; Galaxy Chemical Thermodynamics and Molecular Structure ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: M. Hamed Mozaffari | Li‐Lin Tay
Source: arXiv (Cornell University) (preprint, 2021-06-01)
Field: Chemistry — Analytical Chemistry

Raman spectral analysis of mixtures with one-dimensional convolutional neural network Recently, the combination of robust one-dimensional convolutional neural networks (1-D CNNs) and Raman spectroscopy has shown great promise in rapid identification of unknown substances with good accuracy. Using this technique, researchers can recognize a pure compound and distinguish it from unknown substances in a mixture. The novelty of this approach is that the trained neural network operates automatically without any pre- or post-processing of data. Some studies have attempted to extend this technique to the classification of pure compounds in an unknown mixture. However, the application of 1-D CNNs has typically been restricted to binary classifications of pure compounds. Here we will highlight a new approach in spectral recognition and quantification of chemical components in a multicomponent mixture. Two 1-D CNN models, RaMixNet I and II, have been developed for this purpose. The former is for rapid classification of components in a mixture while the latter is for quantitative determination of those constituents. In the proposed method, there is no limit to the number of compounds in a mixture. A data augmentation method is also introduced by adding random baselines to the Raman spectra. The experimental results revealed that the classification accuracy of RaMixNet I and II is 100% for analysis of unknown test mixtures; at the same time, the RaMixNet II model may achieve a regression accuracy of 88% for the quantification of each component. Convolutional neural network ; Raman spectroscopy ; Spectral analysis ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Physics ; Optics ; Spectroscopy ; Astronomy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Yu Kitamura | Emi Terado | Zechen Zhang | Hirofumi Yoshikawa | Tomoko Inose | Hiroshi Uji‐i | Masaharu Tanimizu | Akihiro Inokuchi | Yoshinobu Kamakura | Daisuke Tanaka
Source: Chemistry - A European Journal (article, 2021-10-09)
Field: Chemistry — Inorganic Chemistry

Failure‐Experiment‐Supported Optimization of Poorly Reproducible Synthetic Conditions for Novel Lanthanide Metal‐Organic Frameworks with Two‐Dimensional Secondary Building Units** Novel metal-organic frameworks containing lanthanide double-layer-based secondary building units (KGF-3) were synthesized by using machine learning (ML). Isolating pure KGF-3 was challenging, and the synthesis was not reproducible because impurity phases were frequently obtained under the same synthetic conditions. Thus, dominant factors for the synthesis of KGF-3 were identified, and its synthetic conditions were optimized by using two ML techniques. Cluster analysis was used to classify the obtained powder X-ray diffractometry patterns of the products and thus automatically determine whether the experiments were successful. Decision-tree analysis was used to visualize the experimental results, after extracting factors that mainly affected the synthetic reproducibility. Water-adsorption isotherms revealed that KGF-3 possesses unique hydrophilic pores. Impedance measurements demonstrated good proton conductivities (σ=5.2×10<sup>-4</sup> S cm<sup>-1</sup> for KGF-3(Y)) at a high temperature (363 K) and relative humidity of 95 % RH. Lanthanide ; Materials science ; Metal-organic framework ; Adsorption ; Reproducibility ; Relative humidity ; Chemical engineering ; Chromatography ; Chemistry ; Organic chemistry ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Heitor Luiz Ornaghi | Francisco Maciel Monticeli | Roberta Motta Neves | Ademir J. Zattera | Sandro Campos Amico
Source: Polymers and Polymer Composites (article, 2021-08-14)
Field: Materials Science — Polymers and Plastics

Experimental and artificial neural network approach for prediction of dynamic mechanical behavior of sisal/glass hybrid composites The dynamic mechanical behavior (storage modulus, loss modulus, and tan δ) of hybrid sisal/glass composites was investigated in the temperature range of 30–210 °C, for two different volume percentages of reinforcement along with the different ratios of sisal and glass fibers. Based on the experimental outcome, an artificial neural network (ANN) approach was used to predict the dynamic mechanical properties followed by a surface response methodology (SRM). The ANN analysis showed an excellent fit with the storage modulus, loss modulus, and tan δ experimental data. In addition, the fitted curves with the ANN approach were used to propose equations based on SRM. The simulation result has shown that the ANN is a potential mathematical tool for the structure–property correlation for polymer composites and may help researchers in the development and application of their data, reducing the need for long experimental campaigns. Dynamic mechanical analysis ; SISAL ; Materials science ; Dynamic modulus ; Composite material ; Artificial neural network ; Modulus ; Reinforcement ; Glass fiber ; Polymer ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Textile materials and evaluations
Authors: Romans Maļiks | Roberts Kadiķis
Source:  (article, 2021-12-11)
Field: Chemistry — Analytical Chemistry

Multispectral data classification with deep CNN for plastic bottle sorting Current global trends and green policies indicate the importance of smart waste sorting. Polymer type identification plays a key role in the circular economy model, where high precision is vital to reduce the impurities of recycled plastic flakes. In this paper, we present a robust, high-accuracy plastic bottle polymer type classification using Convolutional Neural Network (CNN). Near-infrared (NIR) absorbance spectroscopy is used to gather polypropylene (PP), polyethene terephthalate (PET), high-density polyethene (HDPE), and low-density polyethene (LDPE) spectra in a dry and wet state. We propose a data augmentation method that generates additional training examples, and we experimentally determine the impact of the ratio of real and generated samples on the accuracy of the classification. In addition, we compare this classification approach with Support Vector Machine (SVM), Principal Component Analysis (PCA) and t-distributed Stochastic Neighbour Embedding (t-SNE) clas-sification methods and also provide data-preprocessing steps for these methods. Finally, we combine pre-processing, component analysis, and CNN to achieve 98.4% accuracy rate while reducing the sizes of CNN input feature vectors and the CNN model itself. Principal component analysis ; Multispectral image ; Artificial intelligence ; Computer science ; Support vector machine ; High-density polyethylene ; Pattern recognition (psychology) ; Convolutional neural network ; Feature (linguistics) ; Low-density polyethylene ; Sorting ; Remote sensing ; Materials science ; Polymer ; Algorithm ; Polyethylene Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Industrial Vision Systems and Defect Detection
Authors: Hyuntae Lim | YounJoon Jung
Source: Research Square (preprint, 2021-02-18)
Field: Materials Science — Materials Chemistry

Solvation Free Energy Prediction from Pairwise Atomistic Interactions by Machine Learning Abstract Recent advances in machine learning technologies and their applications have led to the development of diverse structure-property relationship models for crucial chemical properties. The solvation free energy is one of them. Here, we introduce a novel ML-based solvation model, which calculates the solvation energy from pairwise atomistic interactions. The novelty of the proposed model consists of a simple architecture: two encoding functions extract atomic feature vectors from the given chemical structure, while the inner product between the two atomistic features calculates their interactions. The results of 6,493 experimental measurements achieve outstanding performance and transferability for enlarging training data owing to its solvent-non-specific nature. An analysis of the interaction map shows that our model has significant potential for producing group contributions on the solvation energy, which indicates that the model provides provides not only predictions of target properties but also more detailed physicochemical insights. Pairwise comparison ; Solvation ; Artificial intelligence ; Machine learning ; Energy (signal processing) ; Computer science ; Statistical physics ; Chemistry ; Physics ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tsuyoshi Okita | Satoshi Terayama | K. Tsugawa | Keita Kobayashi | Masahiko Okumura | Mitsuhiro Itakura | Katsuyuki Suzuki
Source: Computational Materials Science (article, 2021-10-29)
Field: Materials Science — Materials Chemistry

Construction of machine-learning Zr interatomic potentials for identifying the formation process of c-type dislocation loops Vacancy defect ; Dislocation ; Materials science ; Microstructure ; Interatomic potential ; Slip (aerodynamics) ; Surface energy ; Crystallography ; Molecular dynamics ; Chemical physics ; Chemistry ; Composite material ; Computational chemistry ; Thermodynamics ; Physics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Fusion materials and technologies
Authors: Mohammadreza Heidari Pebdani | Ronald E. Miller
Source: Advances in Mechanical Engineering (article, 2021-09-01)
Field: Materials Science — Biomaterials

Molecular dynamics simulation of pull-out Halloysite nanotube from polyurethane matrix Molecular dynamics (MD) simulation has been applied to study of pull-out of Halloysite nanotubes (HNTs) from a polyurethane (PU) matrix. First, the Machine learning (ML) particle swarm optimization (PSO) method was used to obtain force field parameters for MD from data of density functional theory (DFT) calculations. The current study shows the possibility of using a PSO technique to modify the force field with DFT data with less than 5 kcal/mol discrepancy. Second, we considered the influence of atomic interface on pulling out of HNT from PU. Energy variation has been proposed as the cohesion strength between matrix and nanoparticle. In addition, the best Lennard Jones parameters in the MD simulation make good agreement with an experimental sample stress-strain response. Halloysite ; Molecular dynamics ; Materials science ; Polyurethane ; Force field (fiction) ; Nanotube ; Matrix (chemical analysis) ; Particle swarm optimization ; Density functional theory ; Composite material ; Computational chemistry ; Physics ; Computer science ; Chemistry ; Carbon nanotube ; Algorithm Clay minerals and soil interactions ; Iron oxide chemistry and applications ; Force Microscopy Techniques and Applications
Authors: Zi Li | Bharath Basti Shenoy | Лалита Удпа | Satish Udpa | Yiming Deng
Source: Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems (article, 2021-04-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic Barkhausen Noise Technique for Early-Stage Fatigue Prediction in Martensitic Stainless-Steel Samples Abstract Martensitic grade stainless-steel is generally used to manufacture steam turbine blades in power plants. The material degradation of those turbine blades, due to fatigue, will induce unexpected equipment damage. Fatigue cracks, too small to be detected, can grow severely in the next operating cycle and may cause failure before the next inspection opportunity. Therefore, a nondestructive electromagnetic technique, which is sensitive to microstructure changes in the material, is needed to provide a means to estimate the specimen’s fatigue life. To tackle these challenges, this paper presents a novel magnetic Barkhausen noise (MBN) technique for garnering information relating to the material microstructure changes under test. The MBN signals are analyzed in time as well as frequency domain to infer material information that are influenced by the samples’ material state. Principal component analysis (PCA) is applied to reduce the dimensionality of feature data and extract higher order features. Afterward, probabilistic neural network (PNN) classifies the sample based on the percentage fatigue life to discover the most correlated MBN features to indicate the remaining fatigue life. Furthermore, one criticism of MBN is its poor repeatability and stability, therefore, analysis of variance (ANOVA) is carried out to analyze the uncertainty associated with MBN measurements. The feasibility of MBN technique is investigated in detecting early-stage fatigue, which is associated with plastic deformation in ferromagnetic metallic structures. Experimental results demonstrate that the magnetic Barkhausen noise technique is a promising candidate for characterizing. Barkhausen effect ; Materials science ; Nondestructive testing ; Tempering ; Turbine blade ; Turbine ; Metallurgy ; Mechanical engineering ; Engineering ; Magnetic field Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Structural Health Monitoring Techniques
Authors: Shokirbek Shermukhamedov | L. Chen | Renat R. Nazmutdinov | Alexander Kaiser | Michael Probst
Source: Nuclear Fusion (article, 2021-05-24)
Field: Materials Science — Materials Chemistry

Modelling the sputtering and reflection from a beryllium surface: atomistic analysis Abstract Sputtering from plasma-facing surfaces upon particle impact can limit the lifetime of components in fusion devices, especially in the diverter region. Atomistic simulations of the processes associated with plasma–wall interactions allow for a detailed analysis of sputtering, reflection and adsorption. Most former works of beryllium sputtering by hydrogen isotopes were aimed mostly on the sputtering yield. We investigate the influence of impact energy and angle on sputtering, and analyze these quantities also for the outgoing particle. We model the sputtering by non-cumulative molecular dynamics simulations with a large number of trajectories for the various parameters. The underlying forces and energies are obtained from high-dimensional neural networks fitted to density functional calculations. We find a good agreement with the previously reported sputtering yields for perpendicular impact and a qualitative accordance with experimental data. In detail, the sputtering yield increases with increasing impact energy for angles of incidence larger than 45° with respect to the surface normal, while smaller angles show a maximal yield up to 100 eV. In cases where D reflection rather than sputtering occurs, a similar pattern is found for all angles, with the maximal reflection rate at 80°. Sputtering ; Beryllium ; Materials science ; Yield (engineering) ; Atomic physics ; Reflection (computer programming) ; Physics ; Nuclear physics ; Nanotechnology ; Thin film ; Composite material Fusion materials and technologies ; Nuclear Materials and Properties ; Ion-surface interactions and analysis
Authors: Mehnaz | Lihao Yang | Bo Da | Zejun Ding
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Ensemble machine learning methods: predicting electron stopping powers from a small experimental database Electron stopping power (SP) is of great importance in theoretical and applied research areas specifically for Monte Carlo simulation studies in many microanalysis and surface analysis techniques, radiation dosimetry, and the design of particle detectors. However, experimental data are available for a dozen elemental materials only. On the other hand, the Bethe analytical expression of the SP is applicable at high energies only whereas no generally accepted formula exists at lower energies. We employed ensemble machine learning (ML) methods with the available experimental database for the prediction of SPs of electrons with energies from 100 keV down to 1 eV, in elements over the entire periodic table. With a small training database for electron SPs, we applied various algorithms individually as well as their ensembles, which have the credibility to enhance the prediction accuracy in the case of a small training database. Based on the model's performance evaluation tests, we concluded that the stacked generalization is more accurate than the individual algorithms. Using this method, we were able to predict the electron SPs for 54 elements (in total) including 12 elements that were present in the training database as well as for 42 elements beyond the training database over a wide energy range (1 eV to 100 keV). Compared to other theoretical approaches, the ML predicted SPs show very good agreement with the available experimental data at all energies. Moreover, unlike other theoretical approaches, the ML model does not need dielectric function data and other physical parameters which involve complex calculations. Using our ML model, we have predicted SPs for a further 14 elements for which no theoretical SPs are available because of the lack of good dielectric function data. Ensemble learning ; Computer science ; Electron ; Database ; Artificial intelligence ; Physics ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Semiconductor materials and devices
Authors: Siyuan Li | Xuhui Xia | Bryan D. Vogt
Source: Langmuir (article, 2021-09-09)
Field: Chemistry — Organic Chemistry

Microwave-Enabled Size Control of Iron Oxide Nanoparticles on Reduced Graphene Oxide Nanoparticle-functionalized 2D material networks are promising for a wide range of applications, but in situ formation of nanoparticles is commonly challenged by rapid growth. Here, we demonstrate controlled synthesis of small and dispersed iron oxide nanoparticles on reduced graphene oxide (rGO) networks through rapid localized heating with microwaves with low-cost iron nitrate as the precursor. The strong coupling of the microwave radiation with the rGO network rapidly heats the network locally to decompose the iron nitrate and generate iron oxide nanoparticles, while cessation of microwaves leads to rapid cooling to minimize crystal growth. Small changes in the microwave reaction time (<1 min) led to very large changes in the iron oxide morphology. The solid-state microwave syntheses produced narrower nanoparticle size distribution than conventional heating. These results illustrate the potential of solid-state microwave syntheses to control the nanoparticle size on 2D materials through rapid localized heating under the microwave process conditions, which should be extendable to a variety of transition metal oxide-rGO systems. Nanoparticle ; Oxide ; Graphene ; Microwave ; Materials science ; Iron oxide ; Nanotechnology ; Iron oxide nanoparticles ; Chemical engineering ; Metallurgy Nanomaterials for catalytic reactions ; Graphene research and applications ; Iron oxide chemistry and applications