jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ravi G. Patel | Indu Manickam | Myoungkyu Lee | Mamikon Gulian
Source: arXiv (Cornell University) (preprint, 2022-04-22)
Field: Materials Science — Materials Chemistry

Error-in-variables modelling for operator learning Deep operator learning has emerged as a promising tool for reduced-order modelling and PDE model discovery. Leveraging the expressive power of deep neural networks, especially in high dimensions, such methods learn the mapping between functional state variables. While proposed methods have assumed noise only in the dependent variables, experimental and numerical data for operator learning typically exhibit noise in the independent variables as well, since both variables represent signals that are subject to measurement error. In regression on scalar data, failure to account for noisy independent variables can lead to biased parameter estimates. With noisy independent variables, linear models fitted via ordinary least squares (OLS) will show attenuation bias, wherein the slope will be underestimated. In this work, we derive an analogue of attenuation bias for linear operator regression with white noise in both the independent and dependent variables. In the nonlinear setting, we computationally demonstrate underprediction of the action of the Burgers operator in the presence of noise in the independent variable. We propose error-in-variables (EiV) models for two operator regression methods, MOR-Physics and DeepONet, and demonstrate that these new models reduce bias in the presence of noisy independent variables for a variety of operator learning problems. Considering the Burgers operator in 1D and 2D, we demonstrate that EiV operator learning robustly recovers operators in high-noise regimes that defeat OLS operator learning. We also introduce an EiV model for time-evolving PDE discovery and show that OLS and EiV perform similarly in learning the Kuramoto-Sivashinsky evolution operator from corrupted data, suggesting that the effect of bias in OLS operator learning depends on the regularity of the target operator. Operator (biology) ; Errors-in-variables models ; Mathematics ; State variable ; Linear regression ; Computer science ; Variables ; Artificial intelligence ; Applied mathematics ; Algorithm ; Machine learning ; Physics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Neural Networks and Applications
Authors: Aaron E. Tallman | Reeju Pokharel | Darshan Bamney | Douglas E. Spearot | B. Clausen | Ricardo A. Lebensohn | Donald W. Brown | Laurent Capolungo
Source: Scientific Reports (article, 2022-04-04)
Field: Materials Science — Materials Chemistry

Data-driven analysis of neutron diffraction line profiles: application to plastically deformed Ta Non-destructive evaluation of plastically deformed metals, particularly diffraction line profile analysis (DLPA), is valuable both to estimate dislocation densities and arrangements and to validate microstructure-aware constitutive models. To date, the interpretation of whole line diffraction profiles relies on the use of semi-analytical models such as the extended convolutional multiple whole profile (eCMWP) method. This study introduces and validates two data-driven DLPA models to extract dislocation densities from experimentally gathered whole line diffraction profiles. Using two distinct virtual diffraction models accounting for both strain and instrument induced broadening, a database of virtual diffraction whole line profiles of Ta single crystals is generated using discrete dislocation dynamics. The databases are mined to create Gaussian process regression-based surrogate models, allowing dislocation densities to be extracted from experimental profiles. The method is validated against 11 experimentally gathered whole line diffraction profiles from plastically deformed Ta polycrystals. The newly proposed model predicts dislocation densities consistent with estimates from eCMWP. Advantageously, this data driven LPA model can distinguish broadening originating from the instrument and from the dislocation content even at low dislocation densities. Finally, the data-driven model is used to explore the effect of heterogeneous dislocation densities in microstructures containing grains, which may lead to more accurate data-driven predictions of dislocation density in plastically deformed polycrystals. Dislocation ; Diffraction ; Materials science ; Line (geometry) ; Neutron diffraction ; X-ray crystallography ; Crystallography ; Optics ; Physics ; Composite material ; Chemistry ; Geometry ; Mathematics Microstructure and mechanical properties ; Machine Learning in Materials Science ; Nuclear Materials and Properties
Authors: Cristina Russo | Lynda Wyld | Márjory Da Costa‐Abreu | Charles S. Bury | Cameron Heaton | Laura M. Cole | Simona Francese
Source: Research Square (preprint, 2022-07-14)
Field: Chemistry — Spectroscopy

Non-invasive screening of breast cancer from fingertip smears - a proof of concept study Abstract Breast cancer is a global health issue affecting 2.3 million women per year, causing death in over 600,000. Mammography (and biopsy) is the gold standard for screening and diagnosis. Whilst effective, this test exposes individuals to radiation, has limitations to its sensitivity and specificity and may cause moderate to severe discomfort. Some women may also find this test culturally unacceptable. This proof-of-concept study, combining bottom-up proteomics with Matrix Assisted Laser Desorption Ionisation Mass Spectrometry (MALDI MS) detection, explores the potential for a non-invasive technique for the early detection of breast cancer from fingertip smears. A cohort of 15 women with either benign breast disease (n = 5), early breast cancer (n = 5) or metastatic breast cancer (n = 5) were recruited from a single UK breast unit. Fingertips smears were taken from each patient and from each of the ten digits, either at the time of diagnosis or, for metastatic patients, during active treatment. A number of statistical analyses and machine learning approaches were investigated and applied to the resulting mass spectral dataset. The highest performing predictive method, a 3-class Multilayer Perceptron (MLP) neural network, yielded an accuracy score of 97.8% when categorising unseen MALDI MS spectra as either the benign, early or metastatic cancer classes. These findings support the need for further research into the use of sweat deposits (in the form of fingertip smears or fingerprints) for non-invasive screening of breast cancer. Breast cancer ; Medicine ; Mammography ; Cancer ; Cohort ; Breast cancer screening ; Gold standard (test) ; Oncology ; Internal medicine ; Radiology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: M. H. Hamza | K. H. KHAFAGY | Aniket Chattopadhyay
Source: American Society for Composites 2022 (article, 2022-09-19)
Field: Materials Science — Ceramics and Composites

DEEP NEURAL NETWORK ALGORITHM FOR CMC MICROSTRUCTURE CHARACTERIZATION AND VARIABILITY QUANTIFICATION Microstructure characterization and variability quantification are crucial for understanding ceramic matrix composites (CMCs) mechanical behavior and deformation mechanisms across length scales. Traditionally, analyses of the micrographs obtained from microscopy are labor-intensive. However, with the vast improvement in computer vision (CV) and deep learning (DL), an automated algorithm can be designed to extract essential microstructure variability from micrographs which can then be used to construct a statistically representative volume element (SRVE). The DL-based algorithm spans the taxonomy of microstructure analyses, including 1semantic segmentation of microstructure constituents, secondary phases, matrix/fiber interface, and defects, and quantifying the microstructure variability in terms of probability distributions. In this work, C/SiNC and SiC/SiNC CMCs microstructures are semantically segmented through a deep convolutional neural network, followed by variability quantification through the implementation of a fully connected regression layer, hence forming a deep regression network. The deep regression network operates in a feedforward regime, in which the neuron output signal traverses through the network in a unidirectional manner. The weight tensor associated with each layer is updated through a backpropagation stochastic gradient descent approach. The input gray-scale image obtained through in-house scanning electron microscope and confocal microscope micrographs is augmented through affine transformations to increase the training set size, which is then processed through four strided convolutional layers. This compresses the image resolution by half at each layer while increasing the image depth by applying different filters (image encoding). The class activation maps (CAMs) corresponding to the applied filters highlight the key architectural features and assist with the semantic segmentation of the microstructure. Artificial intelligence ; Convolutional neural network ; Computer science ; Conjugate gradient method ; Microstructure ; Algorithm ; Deep learning ; Feed forward ; Backpropagation ; Pattern recognition (psychology) ; Artificial neural network ; Materials science ; Composite material Advanced ceramic materials synthesis ; Non-Destructive Testing Techniques ; Advanced Machining and Optimization Techniques
Authors: Sai Gokul Subraveti | Zukui Li | Vinay Prasad | Arvind Rajendran
Source: ChemRxiv (preprint, 2022-11-14)
Field: Chemistry — Spectroscopy

Can a computer “learn” non-linear chromatography?: Experimental validation of physics-based deep neural networks for the simulation of chromatographic processes This article presents the capabilities of machine learning in addressing the challenges related to the accurate description of adsorption equilibria in the design of chromatographic processes. Our previously developed physics-based artificial neural network framework for adsorption and chromatography emulation (PANACHE) approach is extended to simulate the dynamics of chromatographic columns without using adsorption isotherms. The incorporation of underlying conservation laws in the form of a physics-constrained loss function during training enables PANACHE models to infer the complex dynamics of chromatographic columns, even without the knowledge of adsorption isotherms. The isotherm-agnostic abilities of PANACHE models are tested by considering two binary systems with complex adsorption equilibria: 1) mixed Langmuir M1-type binary system, and 2) Tröger's base enantiomers on Chiralpak AD that show inflection in the isotherm. The results show that PANACHE models are successful in predicting the dynamics of binary solute mixtures in chromatographic columns even in the absence of adsorption isotherms. The ability of PANACHE models to infer the adsorption isotherms is also demonstrated. Adsorption ; Binary number ; Artificial neural network ; Chromatography ; Langmuir adsorption model ; Physics ; Materials science ; Chemistry ; Computer science ; Artificial intelligence ; Mathematics ; Organic chemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Microfluidic and Capillary Electrophoresis Applications
Authors: Masanao Ochi | Masanori Shiro | Junichiro Mori | Ichiro Sakata
Source: PLoS ONE (article, 2022-09-14)
Field: Materials Science — Materials Chemistry

Predictive analysis of multiple future scientific impacts by embedding a heterogeneous network Identifying promising research as early as possible is vital to determine which research deserves investment. Additionally, developing a technology for automatically predicting future research trends is necessary because of increasing digital publications and research fragmentation. In previous studies, many researchers have performed the prediction of scientific indices using specially designed features for each index. However, this does not capture real research trends. It is necessary to develop a more integrated method to capture actual research trends from various directions. Recent deep learning technology integrates different individual models and makes it easier to construct more general-purpose models. The purpose of this paper is to show the possibility of integrating multiple prediction models for scientific indices by network-based representation learning. This paper will conduct predictive analysis of multiple future scientific impacts by embedding a heterogeneous network and showing that a network embedding method is a promising tool for capturing and expressing scientific trends. Experimental results show that the multiple heterogeneous network embedding improved 1.6 points than a single citation network embedding. Experimental results show better results than baseline for the number of indices, including the author h-index, the journal impact factor (JIF), and the Nature Index after three years from publication. These results suggest that distributed representations of a heterogeneous network for scientific papers are the basis for the automatic prediction of scientific trends. Network analysis ; Computer science ; Data science ; Computational biology ; Biology ; Physics Machine Learning in Materials Science ; Complex Network Analysis Techniques ; scientometrics and bibliometrics research
Authors: Cai Jiangfeng | Dong Yikai
Source: Информатика Экономика Управление - Informatics Economics Management (article, 2022-11-30)
Field: Chemistry — Organic Chemistry

Extraction of plant parenchyma by computer image processing technology People are increasingly using different kinds of plant products, such as wood, but there are many kinds of wood and it is difficult to analyze and identify them, so how to use auxiliary equipment to analyze wood and achieve the goal of accurate wood identification without damaging the product itself has become one of the important problems to be solved in the field of wood research. The axial thin-walled tissue has important wood grain information and it is one of the important features for wood identification. In this paper, we studied the microscopic images of broadleaf wood, and obtained the microstructure images of wood cross-section by photographing, and extracted the complete axial thin-walled tissue morphology of wood by using computer image processing technology and other ways about computer vision. Firstly, the axial thin-walled wood images were de-noised to eliminate some noise effects, so as to facilitate the separation of the axial thin-walled wood; then the images were processed by mathematical morphology to successfully extract the axial thin-walled wood and duct morphology from the cross-sectional images of broadleaf wood; finally, the axial thin-walled wood was separated from the duct by calculating the area of the closed area. Image processing ; Wood processing ; Mathematical morphology ; Duct (anatomy) ; Materials science ; Thin section ; Computer science ; Composite material ; Artificial intelligence ; Mechanical engineering ; Engineering ; Image (mathematics) ; Biology ; Geology ; Mineralogy Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Remote Sensing and Land Use
Authors: Jesse B. Walters
Source:  (preprint, 2022-08-03)
Field: Materials Science — Surfaces, Coatings and Films

MinPlot: A mineral formula recalculation and plotting program for electron probe microanalysis MinPlot is a MATLAB®-based mineral formula recalculation and compositional plotting program for electron microprobe analyses (EPMA). The program offers recalculation and structural formula assignment for 15 different mineral groups: Garnet, pyroxene, olivine, amphibole, feldspar, mica, staurolite, cordierite, chlorite, chloritoid, talc, epidote, titanite, spinel, and sulfides. MinPlot is a fast and easy to use command line program and requires no prior computer programming knowledge. Percent mass fractions of oxides are loaded from datafiles and the user answers simple prompts to select mineral type, normalization scheme, and plotting options. Recalculated mineral formulas are automatically saved as output files and plots may be further manually customized by the user prior to saving. MinPlot can perform thousands of calculations in seconds and the modular nature of the program makes it simple to add new calculation routines in future releases. Finally, MinPlot utilizes simple matrix algebra and linear programing steps, and is thus anticipated to be forwards compatible with future releases of MATLAB®. Combined, these features make MinPlot a powerful and useful program for the processing of EPMA data. Electron microprobe ; Mineral ; Computer science ; Epidote ; Amphibole ; MATLAB ; Talc ; Algorithm ; Mineralogy ; Computational science ; Geology ; Chlorite ; Programming language ; Materials science ; Quartz ; Metallurgy Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Advanced Materials Characterization Techniques
Authors: Dinesh Kumar Meena | Brij Sharma | Raghuraj Parihar
Source: GSC Biological and Pharmaceutical Sciences (article, 2022-09-11)
Field: Chemistry — Organic Chemistry

Quantitative structure-activity relationship study on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines QSAR study has been carried out on the CDK2 inhibitory activity of 6-substituted 2-arylaminopurines in 0D- to 2D-Dragon descriptors. The derived QSAR models have revealed that the reciprocal hyper-detour index (descriptor Rww) and path/walk 5 Randic shape index (descriptor PW5) played a pivotal role in rationalization of CDK2 inhibition activity of titled compounds. Molecular weight (MW), mean atomic volume scaled on Carbon atom (Mv) and atomic properties such as mass and atomic Sanderson electronegativity in terms of atomic properties weighted descriptors MATS1m, MATS3e, MATS4e, GATS3e and GATS8e, certain atom centred fragments such as H attached to C0(sp3) no X attached to next C (descriptor H-046),R--CH--X (descriptor C-027) and R--CX--X (descriptor C-029) are also predominant to explain CDK2 inhibition actions of 6-substituted 2-arylaminopurines. PLS analysis has also corroborated the dominance of CP-MLR identified descriptors. Applicability domain analysis revealed that the suggested model matches the high quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly. Electronegativity ; Quantitative structure–activity relationship ; Chemistry ; Reciprocal ; Atom (system on chip) ; Molecular descriptor ; Mathematics ; Stereochemistry ; Computational chemistry ; Computer science ; Organic chemistry Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity
Authors: Conor D. Rankine | Thomas J. Penfold
Source: ChemRxiv (preprint, 2022-02-03)
Field: Materials Science — Materials Chemistry

Accurate, Affordable, and Generalisable Machine Learning Simulations of Transition Metal X-ray Absorption Spectra using the XANESNET Deep Neural Network The affordable, accurate, and reliable prediction of spectroscopic observables plays a key role in the analysis of increasingly-complex experiments. In this Article, we develop and deploy a deep neural network (DNN) – XANESNET – for predicting the lineshape of first-row transition metal K-edge X-ray absorption near-edge structure (XANES) spectra. XANESNET predicts the spectral intensities using only information about the local coordination geometry ofthe transition metal complexes encoded in a feature vector of weighted atom-centred symmetry functions (wACSF). We address in detail the calibration of the feature vector for the particularities of the problem at hand, and we explore the individual feature importances to reveal the physical insight that XANESNET obtains at the Fe K-edge. XANESNET relies on only a few judiciously-selected features – radial information on the first and second coordination shells suffices, along with angular information sufficient to separate satisfactorily key coordination geometries. The feature importance is found to reflect the XANES spectral window under consideration and is consistent with the expected underlying physics. We subsequently apply XANESNET at nine first-row transition metal (Ti–Zn) K-edges. It can be optimised in as little as a minute, predicts instantaneously, and provides K-edge XANES spectra with an average accuracy of ca. ± 2–4% in which the positions of prominent peaks are matched with a > 90% hit rate to sub-eV (ca.0.8 eV) error. XANES ; Feature (linguistics) ; Spectral line ; Artificial neural network ; Transition metal ; Enhanced Data Rates for GSM Evolution ; Absorption (acoustics) ; Artificial intelligence ; Key (lock) ; Feature vector ; Computer science ; Physics ; Chemistry ; Optics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; Electron and X-Ray Spectroscopy Techniques
Authors: Marwa A. Fouad | Enas H. Tolba | Manal A. El‐Shal | Ahmed Serag | Ahmed M. El Kerdawy
Source: Research Square (preprint, 2022-05-19)
Field: Chemistry — Spectroscopy

QSRR modeling of the chromatographic retention behavior for some quinolone and sulfonamide antibacterial agents using firefly algorithm coupled to support vector machine Abstract Quinolone and sulfonamide are two classes of antibacterial agents that have an opulent history of medicinal chemistry features responsible for their improving bacterial spectrum, efficacy, pharmacokinetics, and adverse side effect profiles. The urgent need of their use and escalating rate of their resistance provokes the necessity for developing suitable analytical methods that speed up and facilitate their analysis. In this study, advanced firefly algorithm (FFA) coupled with support vector machine (SVM) were used to select the most significant descriptors and to construct two separate quantitative structure–retention relationship (QSRR) models using a series of 11 selected quinolones and 13 sulfonamide drugs, separately, in order to predict their retention factors in HPLC. Precisely, the effect of different pH range values and acetonitrile composition in the mobile phase on the retention behavior of quinolones and sulfonamides were studied, respectively. The obtained QSRR models showed high performance in both internal and external validation indicating their robustness and predictive ability. Y-randomization validation displayed that the obtained models are not obtained by chance. Besides, the obtained results shed the light on the molecular features that influence the retention behavior of these two classes under the current chromatographic conditions. Support vector machine ; Sulfonamide ; Retention time ; Quinolone ; Acetonitrile ; High-performance liquid chromatography ; Chromatography ; Chemistry ; Robustness (evolution) ; Computer science ; Machine learning ; Antibiotics ; Stereochemistry Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Antibiotics Pharmacokinetics and Efficacy