jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lawson T. Glasby | Peyman Z. Moghadam
Source: Patterns (article, 2021-07-01)
Field: Chemistry — Inorganic Chemistry

Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack In recent years, machine learning (ML) has grown exponentially within the field of structure property predictions in materials science. In this issue of <i>Patterns</i>, Ahmed and Siegel scrutinize several redeveloped ML techniques for systematic investigations of over 900,000 metal-organic framework (MOF) structures, taken from 19 databases, to discover new, potentially record-breaking, hydrogen-storage materials. Hydrogen storage ; Database ; Metal-organic framework ; Nanotechnology ; Computer science ; Materials science ; Chemistry ; Hydrogen ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Meenakshi Ahirwar | B.K. Behera
Source: Journal of the Textile Institute (review, 2021-10-06)
Field: Materials Science — Polymers and Plastics

Fabric hand research translates senses into numbers – a review Fabric hand is the quality of a fabric assessed by the reaction obtained from the sense of touch. Fabric hand is the person’s estimation when feeling fabrics between fingers and thumb. Assessment of a textile material obtained from the sense of touch is an essential consideration in fabric development and marketing. The aim of this review is to provide detailed literature on fabric hand dating back to 1930. The structure of the article consists of the evolution of fabric hand research, its definition given by various researchers, subjective and objective evaluation, artificial neural network (ANN) modeling, instruments used to evaluate low-stress mechanical properties, the relationship between physical properties and fabric hand, applications of fabric hand, the extensive literature on all the existing Kawabata equations for conventional as well as specialty textiles. The review presents a computational approach by the authors to predict the fabric hand of specialty fabrics like automotive textiles, denim, bed linen, etc. Denim ; Clothing ; Automotive industry ; Textile ; Yarn ; Fabric structure ; Woven fabric ; Computer science ; Engineering ; Artificial intelligence ; Mechanical engineering ; Composite material ; Materials science ; Operations management Textile materials and evaluations ; Color perception and design ; Industrial Vision Systems and Defect Detection
Authors: Hiromasa Kaneko
Source: Chemometrics and Intelligent Laboratory Systems (article, 2021-04-22)
Field: Materials Science — Materials Chemistry

Extended Gaussian mixture regression for forward and inverse analysis Hyperparameter ; Extrapolation ; Python (programming language) ; Computer science ; Inverse ; Bayesian probability ; Regression ; Gaussian process ; Gaussian ; Algorithm ; Data mining ; Mathematics ; Artificial intelligence ; Statistics ; Physics Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Carlo Mengucci | Davide Rabiti | Eleonora Urbinati | Gianfranco Picone | Raffaele Romano | Alessandra Aiello | Pasquale Ferranti | Francesco Capozzi
Source: Applied Sciences (article, 2021-02-05)
Field: Chemistry — Analytical Chemistry

Spotting Frozen Curd in PDO Buffalo Mozzarella Cheese Through Insights on Its Supramolecular Structure Acquired by 1H TD-NMR Relaxation Experiments The addition of frozen curd (FC) during the production process of “Mozzarella di Bufala Campana”, an Italian cheese with Protected Designation of Origin (PDO), is a common fraud not involving modifications of the chemical composition in the final product. Its detection cannot thus be easily obtained by common analytical methods, which are targeted at changes in concentrations of diagnostic chemical species. In this work, the possibility of spotting this fraud by focusing on the modifications of the supramolecular structure of the food matrix, detected by time domain nuclear magnetic resonance (TD-NMR) experiments, was investigated. Cheese samples were manufactured in triplicate, according to the PDO disciplinary of production, except for using variable amounts of FC (i.e., 0, 15, 30, and 50% w/w). Relaxation data were analysed through different approaches: (i) Discrete multi-exponential fitting, (ii) continuous Laplace inverse fitting, and (iii) chemometrics approach. The strategy that lead to best detection results was the chemometrics analysis of raw Carr-Purcell-Meiboom-Gill (CPMG) decays, allowing to discriminate between compliant and adulterated samples, with as low as 15% of FC addition. The strategy is based on the use of machine learning for projection on latent structures of raw CPMG data and classification tasks for fraud detection, using quadratic discriminant analysis. By coupling TD-NMR raw decays with machine learning, this work opens the way to set up a system for detecting common food frauds modifying the matrix structure, for which no official authentication methods are yet available. Chemometrics ; Chemistry ; Mozzarella cheese ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Food science ; Chromatography Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; NMR spectroscopy and applications
Authors: Vladimir Ladygin | I. Beniya | E. Makarov | Alexander V. Shapeev
Source: Physical review. B./Physical review. B (article, 2021-09-07)
Field: Materials Science — Materials Chemistry

Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams Accurate phase diagram calculation from molecular dynamics requires systematic treatment and convergence of statistical averages. In this work we propose a Gaussian process regression based framework for reconstructing the free-energy functions using data of various origins. Our framework allows for propagating statistical uncertainty from finite molecular dynamics trajectories to the phase diagram and automatically performing convergence with respect to simulation parameters. Furthermore, our approach provides a way for automatic optimal sampling in the simulation parameter space based on a Bayesian optimization approach. We validate our methodology by constructing phase diagrams of two model systems, the Lennard-Jones and soft-core potential, and compare the results with the existing studies and our coexistence simulations. Finally, we construct the phase diagram of lithium at temperatures above 300 K and pressures below 30 GPa from a machine-learning potential trained on ab initio data. Our approach performs well when compared to coexistence simulations and experimental results. Convergence (economics) ; Bayesian probability ; Phase diagram ; Phase (matter) ; Numerical integration ; Statistical physics ; Computer science ; Applied mathematics ; Mathematics ; Artificial intelligence ; Physics ; Mathematical analysis ; Economics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Yan Xiang | Yuhang Tang | Guang Lin | Huai Sun
Source: Journal of Chemical Information and Modeling (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural Network This work proposes a state-of-the-art hybrid kernel to calculate molecular similarity. Combined with Gaussian process models, the performance of the hybrid kernel in predicting molecular properties is comparable to that of the directed message-passing neural network (D-MPNN). The hybrid kernel consists of a marginalized graph kernel (MGK) and a radial basis function (RBF) kernel that operate on molecular graphs and global molecular features, respectively. Bayesian optimization was used to obtain the optimal hyperparameters for both models. The comparisons are performed on 11 publicly available data sets. Our results show that their performances are similar, their prediction errors are correlated, and the ensemble predictions of the two models perform better than either of them. Through principal component analysis, we found that the molecular embeddings of the hybrid kernel and the D-MPNN are also similar. The advantage of D-MPNN lies in the computational efficiency and scalability of large-scale data, while the advantage of the graph kernel models lies in the accurate uncertainty quantification. Kernel (algebra) ; Computer science ; Kernel method ; Graph kernel ; Artificial intelligence ; Kernel principal component analysis ; Artificial neural network ; Graph ; Hyperparameter ; Polynomial kernel ; Pattern recognition (psychology) ; Machine learning ; Algorithm ; Theoretical computer science ; Mathematics ; Support vector machine ; Discrete mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Nicole Creange | Ondrej Dyck | Rama K. Vasudevan | Maxim Ziatdinov | Sergei V. Kalinin
Source: Machine Learning Science and Technology (article, 2021-11-10)
Field: Materials Science — Materials Chemistry

Towards automating structural discovery in scanning transmission electron microscopy <sup>*</sup> Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows . Computer science ; Workflow ; Artificial intelligence ; Machine learning ; Robustness (evolution) ; Python (programming language) ; Autoencoder ; Deep learning ; Chemistry Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Maohua Li | Mohsen Mesbah | Alireza Fallahpour | Bahman Nasiri‐Tabrizi | Baoyu Liu
Source: Materials Letters (article, 2021-08-06)
Field: Materials Science — Biomaterials

Mechanical strength estimation of ultrafine-grained magnesium implant by neural-based predictive machine learning Support vector machine ; Adaptive neuro fuzzy inference system ; Materials science ; Gene expression programming ; Machine learning ; Artificial neural network ; Artificial intelligence ; Magnesium ; Computer science ; Composite material ; Fuzzy logic ; Fuzzy control system ; Metallurgy Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Jong-Hoon Yoon | Eunseong Choi | Kyoungmin Min
Source: The Journal of Physical Chemistry A (article, 2021-11-12)
Field: Materials Science — Materials Chemistry

Adaptive Learning Framework in Prediction and Validation of Gibbs Free Energy for Inorganic Crystalline Solids Gibbs free energy is a fundamental physical property for understanding the stability and synthesizability of materials under various thermodynamic conditions, but its accessibility and availability are still limited. In this study, we used 9880 phonon databases to construct a machine learning model to predict approximately 40 000 Inorganic Crystalline Solid Database (ICSD) materials, whose free energy information has not been fully explored. To improve the prediction accuracy, a sampling strategy was implemented by including structures with low accuracy metrics, leading to <i>R</i><sup>2</sup> and mean absolute error values of 0.99 and 18.7 kJ/mol, respectively, in the testing set. Uncertainty analysis was followed for unexplored ICSD materials by obtaining the standard deviation in predictions from 10 surrogate models with different samplings in the training set. Based on this, an optimization process was conducted: density functional calculations were performed for 50 structures with high uncertainty and the training database was updated; this loop was repeated 15 times. This demonstrates the reduction and saturation in the uncertainty, confirming that the constructed model can provide a comprehensive map of the Gibbs free energy for inorganic solid materials. This can accelerate the material screening process by providing information on thermodynamic stability. Gibbs free energy ; Computer science ; Stability (learning theory) ; Standard deviation ; Energy (signal processing) ; Set (abstract data type) ; Data mining ; Artificial intelligence ; Machine learning ; Thermodynamics ; Statistics ; Mathematics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Thermal and Kinetic Analysis
Authors: Dai Tang | Farhad Gharagheizi | David S. Sholl
Source: The Journal of Physical Chemistry B (article, 2021-01-15)
Field: Chemistry — Inorganic Chemistry

Adsorption-Based Separation of Near-Azeotropic Mixtures—A Challenging Example for High-Throughput Development of Adsorbents Adsorption of gas mixtures is central to adsorption-based gas separations, and the number of adsorbate mixture/adsorbent systems that exist is staggering. Because examples of machine learning (ML) models predicting single-component adsorption of arbitrary molecules in large libraries of crystalline adsorbents have been developed, it is interesting to determine whether these models can accurately predict mixture adsorption. Here, we use molecular simulations to generate mixture adsorption data with a set of 12 near-azeotropic molecules in a diverse set of MOFs. These data provide a challenging example for any method to rapidly predict mixture adsorption in MOFs. We combine a previous ML single-component isotherm model with ideal adsorbed solution theory (IAST) to make predictions that can be compared directly with molecular simulation data for these adsorbed mixtures. This combination of ML and IAST illustrates the scope that is available with these methods, but the accuracy of the resulting predictions is disappointing. By examining the same examples with IAST based on minimal molecular simulation data for single-component isotherms, we show that having an accurate description of adsorption in the dilute loading limit is critical to being able to accurately predict mixture adsorption. This observation points to a useful direction for future work developing robust ML models of adsorption isotherms for diverse collections of molecules and adsorbents. Adsorption ; Component (thermodynamics) ; Molecule ; Set (abstract data type) ; Chemistry ; Work (physics) ; Materials science ; Chromatography ; Biological system ; Computer science ; Chemical engineering ; Thermodynamics ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Phase Equilibria and Thermodynamics
Authors: Kohei Shimamura | Yusuke Takeshita | Shogo Fukushima | Akihide Koura | Fuyuki Shimojo
Source: Chemical Physics Letters (article, 2021-05-19)
Field: Materials Science — Materials Chemistry

Estimating thermal conductivity of α-Ag2Se using ANN potential with Chebyshev descriptor Chebyshev filter ; Thermal conductivity ; Artificial neural network ; Non-equilibrium thermodynamics ; Molecular dynamics ; Homogeneous ; Chebyshev polynomials ; Mathematics ; Statistical physics ; Virial theorem ; Thermodynamics ; Biological system ; Applied mathematics ; Materials science ; Chemistry ; Physics ; Computer science ; Artificial intelligence ; Mathematical analysis ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Lintong Zhang | Qiaoyue Man | Young Im Cho
Source: Diagnostics (article, 2021-10-03)
Field: Materials Science — Polymers and Plastics

Deep-Learning-Based Hair Damage Diagnosis Method Applying Scanning Electron Microscopy Images In recent years, with the gradual development of medicine and deep learning, many technologies have been developed. In the field of beauty services or medicine, it is particularly important to judge the degree of hair damage. Because people in modern society pay more attention to their own dressing and makeup, changes in the shape of their hair have become more frequent, e.g., owing to a perm or dyeing. Thus, the hair is severely damaged through this process. Because hair is relatively thin, a quick determination of the degree of damage has also become a major problem. Currently, there are three specific methods for this purpose. In the first method, professionals engaged in the beauty service industry make a direct judgement with the naked eye. The second way is to observe the damaged cuticle layers of the hair using a microscope, and then make a judgment. The third approach is to conduct experimental tests using physical and chemical methods. However, all of these methods entail certain limitations, inconveniences, and a high complexity and time consumption. Therefore, our proposed method is to use scanning electron microscope to collect various hair sample images, combined with deep learning to identify and judge the degree of hair damage. This method will be used for hair quality diagnosis. Experiment on the data set we made, compared with the experimental results of other lightweight networks, our method showed the highest accuracy rate of 94.8%. Judgement ; Artificial intelligence ; Cuticle (hair) ; Computer science ; Beauty ; Sample (material) ; Biomedical engineering ; Computer vision ; Materials science ; Pattern recognition (psychology) ; Nanotechnology ; Aesthetics ; Engineering ; Medicine ; Chemistry ; Anatomy ; Art ; Chromatography Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Consumer Perception and Purchasing Behavior
Authors: Michal Šídlo | Přemysl Lubal | Pavel Anzenbacher
Source: Chemosensors (article, 2021-02-18)
Field: Chemistry — Spectroscopy

Colorimetric Chemosensor Array for Determination of Halides The halide anions are essential for supporting life. Therefore, halide anion analyses are of paramount importance. For this reason, we have performed both qualitative and quantitative ana- lyses of halides (chloride, bromide, iodide) using the Tl(III) complex of azodye, 4-(2-pyridylazo)re- sorcinol (PAR), a potential new chemical reagent/sensor that utilizes the substitution reaction whereas the Tl(III)PAR complex reacts with a halide to yield a more stable thallium(III)-halide while releasing the PAR ligand in a process accompanied by color change of the solution. The experimental conditions (e.g., pH, ratio metal ion-to-ligand ratio, etc.) for the substitution reaction between the metal complex and a halide were optimized to achieve increased sensitivity and a lower limit of detection (chloride 7 mM, bromide 0.15 mM, iodide 0.05 mM). It is demonstrated that this single chemosensor can, due to release of colored PAR ligand and the associated analyte-specific changes in the UV/VIS spectra, be employed for a multicomponent analysis of mixtures of anions (chloride + bromide, chloride + iodide, bromide + iodide). The spectrophotometric data evaluated by artificial neural networks (ANNs) enable distinguishing among the halides and to determine halide species concentrations in a mixture. The Tl(III)-PAR complex was also used to construct sensor arrays utilizing a standard 96-well plate format where the output was recorded at several wavelengths (up to 7) using a conventional plate reader. It is shown that the data obtained using a digital scanner employing only three different input channels may also be successfully used for a subsequent ANN analysis. The results of all approaches utilized for data evaluation were similar. To increase the practical utility of the chemosensor, we have developed a test paper strip indicator useful for routine naked-eye visual determination of halides. This test can also be used for halide anion determination in solutions using densitometer. The methodology described in this paper can be used for a simple, inexpensive, and fast routine analysis both in a laboratory as well as in a field setting. Halide ; Iodide ; Bromide ; Chemistry ; Chloride ; Reagent ; Ligand (biochemistry) ; Inorganic chemistry ; Detection limit ; Analyte ; Metal halides ; Photochemistry ; Physical chemistry ; Organic chemistry ; Chromatography Molecular Sensors and Ion Detection ; Analytical chemistry methods development ; Electrochemical sensors and biosensors
Authors: MyPhuong T. Le | Nicolás M. Morato | Andreas Kaerner | Christopher J. Welch | R. Graham Cooks
Source: Journal of the American Society for Mass Spectrometry (article, 2021-07-19)
Field: Chemistry — Spectroscopy

Fragmentation of Polyfunctional Compounds Recorded Using Automated High-Throughput Desorption Electrospray Ionization Using desorption electrospray ionization (DESI) as part of an automated high-throughput system, tandem mass spectra of the compounds in a pharmaceutical library were recorded in the positive mode under standardized conditions. Quality control filtering yielded an MS/MS library of 16 662 spectra. Fragmentation of subsets of the compounds in the library chosen to contain a single instance of a particular functional group (amide, piperazine, sulfonamide) was predicted by experts, and the results were compared with the experimental data. Expert performance was good to excellent for all the cases evaluated. Substituents on the functional groups were found to exert important secondary control over the fragmentation, with the main effect observed being product ion stabilization by aromatic substitution, which was consistent across the different groups evaluated. These substituent effects are generally explicable in terms of standard physical organic chemistry considerations of product ion stability as controlling fragmentation. A somewhat unexpected feature was the incidence of homolytic cleavages, driven by the stability of substituted amine radical cations. The findings of this study are intended to lay the groundwork for machine learning approaches to performing MS/MS spectrum → structure and structure → MS/MS spectrum operations on the same experimental data set. The effort involved and the success achieved in computer-aided interpretation, now underway, will be compared with the expert performance as described here. Chemistry ; Fragmentation (computing) ; Tandem mass spectrometry ; Electrospray ionization ; Homolysis ; Electrospray ; Polyatomic ion ; Desorption ; Mass spectrum ; Mass spectrometry ; Analytical Chemistry (journal) ; Computational chemistry ; Ion ; Chromatography ; Organic chemistry ; Radical ; Adsorption Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies