jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christopher Morris | Gaurav Rattan | Petra Mutzel
Source: PolyPublie (École Polytechnique de Montréal) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings Graph kernels based on the $1$-dimensional Weisfeiler-Leman algorithm and corresponding neural architectures recently emerged as powerful tools for (supervised) learning with graphs. However, due to the purely local nature of the algorithms, they might miss essential patterns in the given data and can only handle binary relations. The $k$-dimensional Weisfeiler-Leman algorithm addresses this by considering $k$-tuples, defined over the set of vertices, and defines a suitable notion of adjacency between these vertex tuples. Hence, it accounts for the higher-order interactions between vertices. However, it does not scale and may suffer from overfitting when used in a machine learning setting. Hence, it remains an important open problem to design WL-based graph learning methods that are simultaneously expressive, scalable, and non-overfitting. Here, we propose local variants and corresponding neural architectures, which consider a subset of the original neighborhood, making them more scalable, and less prone to overfitting. The expressive power of (one of) our algorithms is strictly higher than the original algorithm, in terms of ability to distinguish non-isomorphic graphs. Our experimental study confirms that the local algorithms, both kernel and neural architectures, lead to vastly reduced computation times, and prevent overfitting. The kernel version establishes a new state-of-the-art for graph classification on a wide range of benchmark datasets, while the neural version shows promising performance on large-scale molecular regression tasks. Overfitting ; Computer science ; Scalability ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Kernel (algebra) ; Graph kernel ; Graph ; Expressive power ; Tuple ; Artificial neural network ; Kernel method ; Algorithm ; Mathematics ; Support vector machine ; Polynomial kernel ; Discrete mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Jake A. Carter | Logan M. O’Brien | Tina R. Harville | Bradley T. Jones | George L. Donati
Source: Talanta (article, 2020-09-15)
Field: Chemistry — Analytical Chemistry

Machine learning tools to estimate the severity of matrix effects and predict analyte recovery in inductively coupled plasma optical emission spectrometry Analyte ; Chemistry ; Matrix (chemical analysis) ; Inductively coupled plasma ; Partial least squares regression ; Inductively coupled plasma atomic emission spectroscopy ; Principal component analysis ; Analytical Chemistry (journal) ; Biological system ; Artificial intelligence ; Machine learning ; Chromatography ; Plasma ; Computer science Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Mass Spectrometry Techniques and Applications
Authors: Afroditi Kapourani | Vasiliki Valkanioti | Konstantinos N. Kontogiannopoulos | Panagiotis Barmpalexis
Source: International Journal of Pharmaceutics X (article, 2020-12-01)
Field: Chemistry — Analytical Chemistry

Determination of the physical state of a drug in amorphous solid dispersions using artificial neural networks and ATR-FTIR spectroscopy The objective of the present study was to evaluate the use of artificial neural networks (ANNs) in the development of a new chemometric model that will be able to simultaneously distinguish and quantify the percentage of the crystalline and the neat amorphous drug located within the drug-rich amorphous zones formed in an amorphous solid dispersion (ASD) system. Attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy was used, while Rivaroxaban (RIV, drug) and Soluplus® (SOL, matrix-carrier) were selected for the preparation of a suitable ASD model system. Adequate calibration and test sets were prepared by spiking different percentages of the crystalline and the amorphous drug in the ASDs (prepared by the melting - quench cooling approach), while a 2<sup>4</sup> full factorial experimental design was employed for the screening of ANN's structure and training parameters as well as spectra region selection and data preprocessing. Results showed increased prediction performance, measured based on the root mean squared error of prediction (RMSEp) for the test sample, for both the crystalline (RMSEp <sub>(crystal)</sub> = 0.86) and the amorphous (RMSEp <sub>(amorphous)</sub> = 2.14) drug. Comparison with traditional regression techniques, such as partial least square and principle component regressions, revealed the superiority of ANNs, indicating that in cases of high structural similarity between the investigated compounds (i.e., the crystalline and the amorphous forms of the same compound) the implementation of more powerful/sophisticated regression techniques, such as ANNs, is mandatory. Amorphous solid ; Attenuated total reflection ; Fourier transform infrared spectroscopy ; Materials science ; Spectroscopy ; Root mean square ; Chemometrics ; Analytical Chemistry (journal) ; Biological system ; Chemistry ; Chromatography ; Optics ; Crystallography ; Physics Spectroscopy and Chemometric Analyses ; Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography
Authors: Mathew G. Pelletier | Gregory Holt | John D. Wanjura
Source: AgriEngineering (article, 2020-05-20)
Field: Materials Science — Polymers and Plastics

A Cotton Module Feeder Plastic Contamination Inspection System The removal of plastic contamination in cotton lint is an issue of top priority to the U.S. cotton industry. One of the main sources of plastic contamination showing up in marketable cotton bales, at the U.S. Department of Agriculture’s classing office, is plastic from the module wrap used to wrap cotton modules produced by the new John Deere round module harvesters. Despite diligent efforts by cotton ginning personnel to remove all plastic encountered during unwrapping of the seed cotton modules, plastic still finds a way into the cotton gin’s processing system. To help mitigate plastic contamination at the gin; an inspection system was developed that utilized low-cost color cameras to see plastic on the module feeder’s dispersing cylinders, that are normally hidden from view by the incoming feed of cotton modules. This technical note presents the design of an automated intelligent machine-vision guided cotton module-feeder inspection system. The system includes a machine-learning program that automatically detects plastic contamination in order to alert the cotton gin personnel as to the presence of plastic contamination on the module feeder’s dispersing cylinders. The system was tested throughout the entire 2019 cotton ginning season at two commercial cotton gins and at one gin in the 2018 ginning season. This note describes the over-all system and mechanical design and provides an over-view and coverage of key relevant issues. Included as an attachment to this technical note are all the mechanical engineering design files as well as the bill-of-materials part source list. A discussion of the observational impact the system had on reduction of plastic contamination is also addressed. Lint ; Plastic bag ; Contamination ; Agricultural engineering ; Computer science ; Environmental science ; Engineering ; Waste management Textile materials and evaluations
Authors: Antonios Raptakis | Arezoo Dianat | Alexander Croy | Gianaurelio Cuniberti
Source: Nanoscale (article, 2020-12-22)
Field: Materials Science — Materials Chemistry

Predicting the bulk modulus of single-layer covalent organic frameworks with square-lattice topology from molecular building-block properties Two-dimensional Covalent Organic Frameworks (2D COFs) have attracted a lot of interest because of their potential for a broad range of applications. Different combinations of their molecular building blocks can lead to new materials with different physical and chemical properties. In this study, the elasticity of different single-layer tetrabenzoporphyrin (H2-TBPor) and phthalocyanine (H2-Pc) based 2D COFs is numerically investigated using a density-functional based tight-binding approach. Specifically, we calculate the 2D bulk modulus and the equivalent spring constants of the respective molecular building-blocks. Using a spring network model we are able to predict the 2D bulk modulus based on the properties of the isolated molecules. This provides a path to optimize elastic properties of 2D COFs. Block (permutation group theory) ; Materials science ; Lattice (music) ; Modulus ; Rational design ; Covalent bond ; Topology (electrical circuits) ; Square (algebra) ; Square lattice ; Bulk modulus ; Elastic modulus ; Nanotechnology ; Composite material ; Statistical physics ; Mathematics ; Physics ; Geometry ; Combinatorics ; Quantum mechanics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes
Authors: Maxim Ziatdinov | Stephen Jesse | Bobby G. Sumpter | Sergei V. Kalinin | Ondrej Dyck
Source: Nanotechnology (article, 2020-09-15)
Field: Materials Science — Surfaces, Coatings and Films

Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. The analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx. Graphene ; Materials science ; Dopant ; Atom (system on chip) ; Tracking (education) ; Motion (physics) ; Artificial intelligence ; Nanotechnology ; Atomic physics ; Physics ; Computer science ; Optoelectronics ; Doping Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices ; Ion-surface interactions and analysis
Authors: Ruixin Liang | Joanne Yip | Winnie Yu | Lihua Chen | Newman Lau
Source: International Journal of Clothing Science and Technology (article, 2020-04-30)
Field: Materials Science — Polymers and Plastics

Computational modelling methods for sports bra–body interactions Purpose The breasts are mainly fatty and connective tissues with no muscles that directly support them, so wearing sports bras is one of the most effective means of alleviating the discomfort of breast movement and potential injury during vigorous physical exercise. However, the design and development processes of traditional sports bras are time-consuming and costly. Hence, a novel method of simulating the static contact pressure between a sports bra and women’s body based on the finite element (FE) and artificial neural network (ANN) models is developed in this study to contribute to the design considerations of sports bras. Design/methodology/approach Three-dimensional FE models of a female subject and sports bras with different fabric properties are developed to determine the amount of contact pressure exerted onto the body. The FE results are then verified by measuring the amount of pressure exerted by the sports bra on the skin with pressure sensors. The Taguchi technique is used to effectively reduce the number of trials from 625 to only 25 cases. These 25 results obtained through FE modelling are then used to provide the training set for the ANNs. Finally, a comparison between the FE and ANN results is carried out. Findings A novel model of the static contact pressure between a sports bra and human subject based on the FE and ANN methods is presented in this paper. The root mean square error values show that there is only a small difference between the FE and ANN results. Originality/value The ANN function established in this study can be used to predict the mechanical behaviours of breasts and has a fundamental impact on the computer-aided design of functional garments in general. Artificial neural network ; Taguchi methods ; Engineering ; Mean squared error ; Simulation ; Structural engineering ; Computer science ; Artificial intelligence ; Mathematics ; Machine learning ; Statistics Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Optical measurement and interference techniques
Authors: Muhammad Sulaiman | Tanveer Iqbal | Saima Yasin | Hamayoun Mahmood | Ahmad Shakeel
Source: Materials (article, 2020-11-05)
Field: Materials Science — Polymers and Plastics

Study of Nano-Mechanical Performance of Pretreated Natural Fiber in LDPE Composite for Packaging Applications In this work, the effects of chemical pretreatment and different fiber loadings on mechanical properties of the composites at the sub-micron scale were studied through nanoindentation. The composites were prepared by incorporating choline chloride (ChCl) pretreated rice husk waste (RHW) in low-density polyethylene (LDPE) using melt processing, followed by a thermal press technique. Nanoindentation experiments with quasi continuous stiffness mode (QCSM) were performed on the surface of produced composites with varying content of pretreated RHW (i.e., 10, 15, and 20 wt.%). Elastic modulus, hardness, and creep properties of fabricated composites were measured as a function of contact depth. The results confirmed the appreciable changes in hardness, elastic modulus, and creep rate of the composites. Compliance curves indicated that the composite having 20 wt.% of pretreated RHW loading was harder compared to that of the pure LDPE and other composite samples. The values of elastic modulus and hardness of the composite containing 20 wt.% pretreated RHW were increased by 4.1% and 24% as compared to that of the pure LDPE, respectively. The creep rate of 42.65 nm/s and change in depth of 650.42 nm were also noted for the composite with RHW loading of 20 wt.%, which showed the substantial effect of holding time at an applied peak load of 100 mN. We believe that the developed composite could be a promising biodegradable packaging material due to its good tribo-mechanical performance. Materials science ; Composite material ; Low-density polyethylene ; Composite number ; Nanoindentation ; Creep ; Ultimate tensile strength ; Fiber ; Polyethylene Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Nanocomposite Films for Food Packaging
Authors: Florian Belviso | Antonio Cammarata | Jamil Missaoui | Tomáš Polcar
Source: Physical review. B./Physical review. B (article, 2020-10-29)
Field: Materials Science — Materials Chemistry

Effect of electric fields in low-dimensional materials: Nanofrictional response as a case study A proper control of nanoscale friction is mandatory for the fabrication and operation of optimal nanoengineered devices. In this respect, the use of electric fields looks to be promising, since they are able to alter the frictional response without imprinting permanent deformations into the structure. To this aim, we perform ab initio simulations to study the microscopic mechanisms governing friction in low-dimensional materials in the presence of electrostatic fields. We consider ${\mathrm{MX}}_{2}$ transition metal dichalcogenides as a case study. By applying an electric field along an axis orthogonal to the atom layers, we induce a transfer of charge along the same axis; this transfer modifies the interatomic forces, leading, in general, to easier relative layer motion. The reported outcomes constitute a starting point to study the effect of the field direction on the intrinsic friction in future investigations. Finally, the present results can be used to predict the preferential electronic redistribution in nanostructured devices where metal-to-insulator transitions may occur in working conditions. Electric field ; Redistribution (election) ; Materials science ; Nanoscopic scale ; Fabrication ; Insulator (electricity) ; Condensed matter physics ; Nanotechnology ; Physics ; Optoelectronics 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Maziar Montazerian | Edgar Dutra Zanotto
Source: Journal of the American Ceramic Society (article, 2020-05-22)
Field: Materials Science — Ceramics and Composites

Tough, strong, hard, and chemically durable enstatite‐zirconia glass‐ceramic Abstract Strong glass‐ceramics (GCs) have been envisaged and widely researched for various applications, including large architectural panels, ballistic impact protection, bioactive medical implants, and odontological prostheses. Here, we report on the development and characterization of a novel hard, strong and tough enstatite‐zirconia (MgSiO 3 ‐ZrO 2 ) glass‐ceramic derived from a 51SiO 2 –35MgO–6Na 2 O–4ZrO 2 –4TiO 2 (mol%) glass. The best GC was developed by treating glass samples for nucleation at 700°C for 12 hours, followed by crystal growth at 1090°C for 3 minutes. It was characterized by X‐ray fluorescence (XRF), differential scanning calorimetry (DSC), X‐ray diffraction (XRD), scanning electron microscopy (SEM), and high‐resolution transmission electron microscopy (HR‐TEM), and contained plate‐like enstatite, zirconia, and Ti‐containing crystals. We investigated the nucleating ability of ZrO 2 and TiO 2 in inducing internal nucleation. In the early stage of crystallization, enstatite spherulites were observed, which were precipitated by heterogeneous nucleation on previously nucleated ZrO 2 nano‐crystals. At more advanced stages, at high temperatures, they transformed into plate‐like crystals. The ball‐on‐three‐balls strength, elastic modulus, and Vickers micro‐hardness of the GC are 323 ± 26 MPa, 146 ± 13 GPa, and 6.9 ± 0.1 GPa (load = 5N), respectively. The indentation ( K C ), single‐edge notched beam bending ( K IC ), and crack tip ( K tip ) fracture toughness are 2.8 ± 0.6 MP.m 0.5 , 2.2 ± 0.3 MP.m 0.5 , 1.9 ± 0.3 MP.m 0.5 , respectively. The crack propagation profile after a controlled Vickers indentation was quite intricate. The enstatite and zirconia crystals enhanced crack deflection, bridging and branching, hindering crack propagation. According to the ISO 6872 for dental materials, the chemical solubility of our GC is 80 ± 5 μg/cm 2 . Due to this positive combination of high strength, toughness, hardness, and chemical durability, this new glass‐ceramic is envisioned as a candidate for several applications and could be further developed for memory disc substrates, architectural cladding and tiles, ceramic glazes, and dental materials. Materials science ; Differential scanning calorimetry ; Cubic zirconia ; Composite material ; Nucleation ; Scanning electron microscope ; Ceramic ; Enstatite ; Fracture toughness ; Mineralogy ; Chemistry Glass properties and applications ; Advanced ceramic materials synthesis ; Bone Tissue Engineering Materials
Authors: Suveen N. Mathaudhu
Source: Metallurgical and Materials Transactions A (article, 2020-10-14)
Field: Materials Science — Materials Chemistry

Building on Gleiter: The Foundations and Future of Deformation Processing of Nanocrystalline Metals In 1989, fueled by his prior reports and findings, Prof Nanocrystalline material ; Severe plastic deformation ; Materials science ; Work (physics) ; SPARK (programming language) ; Thermomechanical processing ; Materials processing ; Grain boundary ; Metallurgy ; Metastability ; Grain size ; Engineering physics ; Nanotechnology ; Mechanical engineering ; Computer science ; Engineering ; Process engineering ; Microstructure ; Physics Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Domenica Mangraviti | Francesca Rigano | Adriana Arigò | Paola Dugo | Luigi Mondello
Source: LWT (article, 2020-12-04)
Field: Chemistry — Organic Chemistry

Differentiation of Italian extra virgin olive oils by rapid evaporative ionization mass spectrometry The present work aims to differentiate Italian extra virgin olive oils through the analysis of about 100 samples for the building of a spectral database useful for the comparison beetwen top-quality oils. High-quality oils belonging to two different harvest years and coming from different Italian regions, labelled as PDO (Protected Denomination of Origin) or obtained from a single variety of olives (monocultivar), were available. Rapid Evaporative Ionization Mass Spectrometry, in combination with a monopolar handpiece as sampling device was explored for the first time on liquid and poorly conductive samples. Multivariate analyses were applied to build different chemometric models. The monocultivar oils gave positive feedback, leading to the reliable identification of each cultivar. The recognition of PDO oils resulted more challenging (failure percentage > 5%), probably due to the major intra-class variability, since oils labelled with the same PDO trademark often are produced from different cultivar in different percentages. Olive oil ; Mass spectrometry ; Cultivar ; Chemistry ; Chromatography ; Environmental science ; Horticulture ; Food science ; Biology Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Isotope Analysis in Ecology
Authors: Setareh Medghalchi | Carl F. Kusche | Ehsan Karimi | U. Kerzel | Sandra Korte‐Kerzel
Source: JOM (article, 2020-10-16)
Field: Materials Science — Metals and Alloys

Damage Analysis in Dual-Phase Steel Using Deep Learning: Transfer from Uniaxial to Biaxial Straining Conditions by Image Data Augmentation Abstract Microstructural damage can occur during metal forming, but how and where this happens vary with the local microstructure and strain path. Large-scale analysis of such damage mechanisms is particularly important in advanced steels with a heterogeneous phase distribution. In our previous work, we demonstrated that deep learning enables a mechanism-based, statistical analysis by classifying many individual damage sites. The aim of this work is to generalize this approach to different stress states, e.g., biaxial instead of uniaxial tension, without manually labeling a large new ground-truth dataset of further micrographs and to thereby assess the changes in damage behavior with respect to stress state. Data augmentation and regularization allow us to directly apply our approach to the new, biaxial loading case. Overall, the network performance could be greatly improved and an analysis of changes in damage behavior, here the martensite crack angle distribution, with stress state can now be performed. Martensite ; Materials science ; Regularization (linguistics) ; Stress (linguistics) ; Microstructure ; Phase (matter) ; Work (physics) ; Dual-phase steel ; Dual (grammatical number) ; Structural engineering ; Composite material ; Computer science ; Artificial intelligence ; Mechanical engineering ; Engineering ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Fatigue and fracture mechanics