jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dazi Li | Jianxun Liu | Jun Liu
Source: Macromolecular Theory and Simulations (article, 2021-05-02)
Field: Materials Science — Materials Chemistry

NNI‐SMOTE‐XGBoost: A Novel Small Sample Analysis Method for Properties Prediction of Polymer Materials Abstract Despite the usage of machine learning accelerating the properties prediction of polymer materials, obtaining a large number of samples to achieve accurate and fast predictions remains a challenge because of the complex and lengthy experimental process. In this work, an advanced prediction model for the small sample analysis is presented by an ensemble learning algorithm called extreme gradient boosting (XGBoost) based on nearest neighbor interpolation (NNI) and synthetic minority oversampling technique (SMOTE). Different from directly using small sample prediction algorithms, a brand‐new idea based on feature engineering is proposed. NNI algorithm is used to interpolate the original data set to solve the data insufficiency. SMOTE algorithm is used to solve the data imbalance problem by increasing minority samples. The expanded data set is used to build an XGBoost prediction model. A model for predicting Akron abrasion of rubber through mechanical properties is established via the proposed method. The original data set is expanded to 710 samples through two different interpolations. Experimental results show that better prediction accuracy and generalization ability are obtained than traditional algorithms. The Akron abrasion is found to be the most related to the elongation at break of polymer materials. Oversampling ; Computer science ; Algorithm ; Interpolation (computer graphics) ; Generalization ; Boosting (machine learning) ; Sample (material) ; Process (computing) ; Data set ; Set (abstract data type) ; Data mining ; Artificial intelligence ; Machine learning ; Mathematics Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Lubricants and Their Additives
Authors: Wuji Huang | Avik Samanta | Yong Chen | Stephen Baek | Scott K. Shaw | Hongtao Ding
Source: Journal of Manufacturing Processes (article, 2021-08-17)
Field: Materials Science — Surfaces, Coatings and Films

Machine learning model for understanding laser superhydrophobic surface functionalization A general machine learning (ML) framework of surface wetting is proposed by considering a broad range of factors, including solid surface topography, solid surface chemistry, liquid properties, and environmental conditions. In particular, an XGBoost-based ML model is demonstrated for learning the surface wetting behaviors processed by a laser-based surface functionalization process, namely nanosecond laser-based high-throughput surface nanostructuring (nHSN). This is the first known attempt to apply machine learning to surface wetting by considering both surface topography and surface chemistry properties. Novel microscale and nanoscale topography parameters viz., roughness, fractal, entropy, feature periodicity are defined with suitable computer algorithms to comprehensively describe the surface topography. A novel set of surface chemistry parameters such as polarity, volume, and amount of functional groups are also used as the machine learning model input. Upon analyzing the importance of each parameter for the nHSN process, surface chemistry shows the greatest importance in determination of surface wettability, while surface morphology also plays a part in influencing the wettability. Wetting ; Microscale chemistry ; Materials science ; Surface modification ; Surface roughness ; Surface (topology) ; Nanotechnology ; Surface finish ; Artificial intelligence ; Machine learning ; Computer science ; Mechanical engineering ; Composite material ; Geometry ; Mathematics ; Engineering Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Surface Roughness and Optical Measurements
Authors: Mohammad-Reza Mohammadi | Abdolhossein Hemmati‐Sarapardeh | Mahin Schaffie | Maen M. Husein | Milad Karimian | Mohammad Ranjbar
Source: Combustion Theory and Modelling (article, 2021-09-16)
Field: Chemistry — Analytical Chemistry

On the evaluation of crude oil oxidation during thermogravimetry by generalised regression neural network and gene expression programming: application to thermal enhanced oil recovery Enhancing oil recovery using in-situ combustion (ISC) is an attractive alternative, especially for heavy crudes. During ISC, part of the hydrocarbon is pyrolysed/oxidised, which generates heat and deposits fuel in the combustion front. In this study, crude reactions during ISC are modelled after their thermogravimetry thermo-oxidative profiles using advanced machine learning systems. The model inputs include the weight per cent of asphaltenes, resins, and °API gravity of the oil as well as the heating rate and the temperature. Four types of artificial neural networks (ANNs); namely multilayer perceptron (MLP), generalised regression neural network (GRNN), cascade-forward neural network (CFNN), and radial basis function (RBF) neural network, were employed to develop models for accurate prediction of the weight per cent of residual crude oil based on 2289 experimental data points. Moreover, three optimisation algorithms; including Bayesian Regularisation (BR), Levenberg–Marquardt (LM), and Scaled Conjugate Gradient (SCG) were applied in the training step of MLP and CFNN to improve the prediction ability. GRNN provided the most accurate prediction with ∼2.3% overall average absolute per cent relative error and coefficient of determination of 0.9983. GRNN model is reliable for crude oils with °API gravity of 5–35 and up to 820°C. Lastly, a mathematical correlation was developed to estimate the residual crude oil from thermogravimetry analysis using gene expression programming (GEP). GEP also predicted the thermo-oxidative profile with high accuracy. On the basis of sensitivity analysis, residue formation during crude oil oxidation was impacted the most by the temperature, oil °API gravity, and asphaltenes content, respectively. The Leverage approach identified 2.9% of the data points as doubtful. Thermogravimetry ; Artificial neural network ; Multilayer perceptron ; API gravity ; Combustion ; Biological system ; Computer science ; Mathematics ; Chemistry ; Crude oil ; Petroleum engineering ; Artificial intelligence ; Engineering ; Organic chemistry Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Zhiwei Nie | Yuanji Liu | Luyi Yang | Shunning Li | Feng Pan
Source: Advanced Energy Materials (article, 2021-03-11)
Field: Materials Science — Materials Chemistry

Construction and Application of Materials Knowledge Graph Based on Author Disambiguation: Revisiting the Evolution of LiFePO<sub>4</sub> Abstract Due to the recent innovations in computer technology, the emerging field of materials informatics has now become a catalyst for a revolution of the research paradigm in materials science. Knowledge graphs, which provide support for knowledge management, are able to collectively capture the scientific knowledge from the vast collection of research articles and accomplish the automatic recognition of the relationships between entities. In this work, a materials knowledge graph, named MatKG, is constructed, which establishes a unique correspondence between subjects and objects in the materials science area. An emphasis is placed on the disambiguation of authors, addressed by a deduplication model based on machine learning and matching dependencies algorithms. Specifically, MatKG is applied to perform tracking on research trends in the study of LiFePO 4 and to automatically chronicle the milestones achieved so far. It is believed that MatKG can serve as a versatile research platform for amalgamating and refining the scientific knowledge of materials in a variety of subfields and intersectional domains. Computer science ; Knowledge graph ; Data science ; Informatics ; Graph ; Field (mathematics) ; Knowledge extraction ; Variety (cybernetics) ; Artificial intelligence ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Data Quality and Management ; X-ray Diffraction in Crystallography
Authors: Yuekuan Zhou
Source: iScience (review, 2021-11-10)
Field: Chemistry — Spectroscopy

Artificial neural network-based smart aerogel glazing in low-energy buildings: A state-of-the-art review Aerogel materials with super-insulating, visual-penetrable, and sound-proof properties are promising in buildings, whereas the coupling effect of various parameters in complex porous aerogels proposes challenges for thermal/visual performance prediction. Traditional physics-based models face challenges such as modeling complexity, heavy computational load, and inadaptability for long-term validation (owing to boundary condition change, degradation of thermophysical properties, and so on). In this study, a holistic review is conducted on aerogel production, components prefabrication, modeling development, single-, and multi-objective optimizations. Methodologies to quantify parameter uncertainties are reviewed, including interface energy balance, Rosseland approximation and Monte Carlo method. Novel aerogel integrated glazing systems with synergistic functions are demonstrated. Originalities include an innovative modeling approach, enhanced computational efficiency, and user-friendly interface for non-professionals or multidisciplinary research. In addition, human knowledge-based machine learning can reduce abundant data requirement, increase performance prediction reliability, and improve model interpretability, so as to promote advanced aerogel materials in smart and energy-efficient buildings. Aerogel ; Glazing ; Interpretability ; Computer science ; Efficient energy use ; Artificial neural network ; Multidisciplinary approach ; Interface (matter) ; Materials science ; Artificial intelligence ; Simulation ; Mechanical engineering ; Architectural engineering ; Nanotechnology ; Engineering ; Composite material Aerogels and thermal insulation
Authors: Jack B. Greisman | Kevin M. Dalton | Doeke R. Hekstra
Source: Journal of Applied Crystallography (article, 2021-09-04)
Field: Materials Science — Materials Chemistry

<i>reciprocalspaceship</i> : a Python library for crystallographic data analysis Crystallography uses the diffraction of X-rays, electrons or neutrons by crystals to provide invaluable data on the atomic structure of matter, from single atoms to ribosomes. Much of crystallography's success is due to the software packages developed to enable automated processing of diffraction data. However, the analysis of unconventional diffraction experiments can still pose significant challenges - many existing programs are closed source, sparsely documented, or challenging to integrate with modern libraries for scientific computing and machine learning. Described here is <i>reciprocalspaceship</i>, a Python library for exploring reciprocal space. It provides a tabular representation for reflection data from diffraction experiments that extends the widely used <i>pandas</i> library with built-in methods for handling space groups, unit cells and symmetry-based operations. As is illustrated, this library facilitates new modes of exploratory data analysis while supporting the prototyping, development and release of new methods. Python (programming language) ; Computer science ; Software ; Reciprocal lattice ; Crystallography ; Electron diffraction ; Diffraction ; Physics ; Programming language ; Chemistry ; Optics Machine Learning in Materials Science ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Anna Ściążko | Yosuke Komatsu | Takaaki Shimura | Naoki Shikazono
Source: Journal of The Electrochemical Society (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Segmentation of Solid Oxide Cell Electrodes by Patch Convolutional Neural Network A framework for the automatic segmentation of large 3-D datasets of microscopic images is designed and tested for microstructures of solid oxide cells (SOC) electrodes reconstructed using focused ion beam—scanning electron microscopy (FIB-SEM). The developed algorithm utilizes a simple, yet very effective deep neural network based on the patch-convolutional layers (patch-CNN) in the encoder-decoder configuration. The guidelines for a selection of network architecture and for preparing and minimizing the training dataset are given. The proposed methodology is tested for various SOC electrode microstructures. The analyzed FIB-SEM tomography data have different resolutions, material properties and measurement artefacts. Pixel-based accuracies of the validation datasets are over 97.5%, and the 3-D microstructural parameters calculated from the ground-truth data and CNN-data show good agreement. The proposed automatic patch-CNN microstructure reconstruction shortens the image processing time by two orders of magnitude. The proposed method can achieve high accuracy which has not been reported in previous studies. In addition, the proposed CNN can serve as an artificial pore-infiltration technique that will help to reconstruct samples without epoxy resin infiltration. The developed framework can be easily extended for other multiphase porous materials with different 3-D imagining techniques that require image processing. Convolutional neural network ; Computer science ; Artificial intelligence ; Encoder ; Microstructure ; Materials science ; Focused ion beam ; Segmentation ; Pixel ; Ground truth ; Electrode ; Artificial neural network ; Scanning electron microscope ; Pattern recognition (psychology) ; Composite material ; Ion ; Physics Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications ; Advanced X-ray and CT Imaging
Authors: Cecilia Riccioli | Dolores Pérez‐Marín | Ana Garrido‐Varo
Source: Postharvest Biology and Technology (article, 2021-02-23)
Field: Chemistry — Analytical Chemistry

Optimizing spatial data reduction in hyperspectral imaging for the prediction of quality parameters in intact oranges Hyperspectral imaging ; Mean squared error ; Mathematics ; Pattern recognition (psychology) ; Data set ; Test set ; Artificial intelligence ; Image resolution ; Root mean square ; Mean squared prediction error ; Statistics ; Computer science ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Water Quality Monitoring and Analysis
Authors: Cristiano Carlomagno | Dario Bertazioli | Alice Gualerzi | Silvia Picciolini | Michele Andrico | Francesca Rodà | Mario Meloni | Paolo Banfi | Federico Verde | Nicola Ticozzi | Vincenzo Silani | Enza Messina | Marzia Bedoni
Source: Frontiers in Neuroscience (article, 2021-10-26)
Field: Chemistry — Analytical Chemistry

Identification of the Raman Salivary Fingerprint of Parkinson’s Disease Through the Spectroscopic– Computational Combinatory Approach Despite the wide range of proposed biomarkers for Parkinson's disease (PD), there are no specific molecules or signals able to early and uniquely identify the pathology onset, progression and stratification. Saliva is a complex biofluid, containing a wide range of biological molecules shared with blood and cerebrospinal fluid. By means of an optimized Raman spectroscopy procedure, the salivary Raman signature of PD can be characterized and used to create a classification model. Raman analysis was applied to collect the global signal from the saliva of 23 PD patients and related pathological and healthy controls. The acquired spectra were computed using machine and deep learning approaches. The Raman database was used to create a classification model able to discriminate each spectrum to the correct belonging group, with accuracy, specificity, and sensitivity of more than 97% for the single spectra attribution. Similarly, each patient was correctly assigned with discriminatory power of more than 90%. Moreover, the extracted data were significantly correlated with clinical data used nowadays for the PD diagnosis and monitoring. The preliminary data reported highlight the potentialities of the proposed methodology that, once validated in larger cohorts and with multi-centered studies, could represent an innovative minimally invasive and accurate procedure to determine the PD onset, progression and to monitor therapies and rehabilitation efficacy. Fingerprint (computing) ; Identification (biology) ; Raman spectroscopy ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Speech recognition ; Medicine ; Biology ; Physics ; Optics ; Botany Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Electrochemical sensors and biosensors
Authors: Ashish Kumar Nayak | Anjali Pal
Source: Journal of environmental chemical engineering (article, 2021-04-30)
Field: Chemistry — Analytical Chemistry

Enhanced adsorption of gentian violet dye from water using lignocellulosic agricultural waste modified with di- and tri-carboxylic acids: Artificial intelligence modeling, practical comprehension, mechanistic and regeneration analyses Chemistry ; Adsorption ; Aqueous solution ; Endothermic process ; Langmuir adsorption model ; Citric acid ; Physisorption ; Oxalic acid ; Nuclear chemistry ; Chromatography ; Inorganic chemistry ; Organic chemistry Dye analysis and toxicity
Authors: Sergei V. Kalinin | Shuai Zhang | Mani Valleti | Harley Pyles | David Baker | James J. De Yoreo | Maxim Ziatdinov
Source: ACS Nano (article, 2021-04-16)
Field: Chemistry — Spectroscopy

Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods <i>via</i> Rotationally Invariant Latent Representations The dynamics of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotationally invariant variational autoencoder-based analysis of orientation and shape evolution. The latter allows for disentanglement of the particle orientation from other degrees of freedom and compensates for lateral shifts. The disentangled representations in the latent space encode the rich spectrum of local transitions that can now be visualized and explored <i>via</i> continuous variables. The time dependence of ensemble averages allows insight into the time dynamics of the system and, in particular, illustrates the presence of the potential ordering transition. Finally, analysis of the latent variables along the single-particle trajectory allows tracing these parameters on a single-particle level. The proposed approach is expected to be universally applicable for the description of the imaging data in optical, scanning probe, and electron microscopy seeking to understand the dynamics of complex systems where rotations are a significant part of the process. Nanorod ; Invariant (physics) ; Chemical physics ; Statistical physics ; Rotational invariance ; Materials science ; Scale invariance ; Molecular dynamics ; Dynamics (music) ; Nanotechnology ; Physics ; Mathematical physics ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Molecular spectroscopy and chirality ; Advanced Proteomics Techniques and Applications
Authors: Raybel Muñoz | Eva M. Santos | Carlos Andrés Galán-Vidal | José Manuel Miranda | Aroa López-Santamarina | José A. Rodrı́guez
Source: Molecules (review, 2021-05-08)
Field: Materials Science — Materials Chemistry

Ternary Quantum Dots in Chemical Analysis. Synthesis and Detection Mechanisms Ternary quantum dots (QDs) are novel nanomaterials that can be used in chemical analysis due their unique physicochemical and spectroscopic properties. These properties are size-dependent and can be adjusted in the synthetic protocol modifying the reaction medium, time, source of heat, and the ligand used for stabilization. In the last decade, several spectroscopic methods have been developed for the analysis of organic and inorganic analytes in biological, drug, environmental, and food samples, in which different sensing schemes have been applied using ternary quantum dots. This review addresses the different synthetic approaches of ternary quantum dots, the sensing mechanisms involved in the analyte detection, and the predominant areas in which these nanomaterials are used. Ternary operation ; Quantum dot ; Nanomaterials ; Analyte ; Nanotechnology ; Materials science ; Quantum chemical ; Chemistry ; Molecule ; Computer science ; Chromatography ; Organic chemistry Quantum Dots Synthesis And Properties ; Advanced biosensing and bioanalysis techniques ; Carbon and Quantum Dots Applications
Authors: Jingying Xu | Jiangang Tao | Lili Su | Jidong Wang | Tifeng Jiao
Source: Materials (review, 2021-07-16)
Field: Materials Science — Materials Chemistry

A Critical Review of Carbon Quantum Dots: From Synthesis toward Applications in Electrochemical Biosensors for the Determination of a Depression-Related Neurotransmitter Depression has become the leading cause of disability worldwide and is a global health burden. Quantitative assessment of depression-related neurotransmitter concentrations in human fluids is highly desirable for diagnosis, monitoring disease, and therapeutic interventions of depression. In this review, we focused on the latest strategies of CD-based electrochemical biosensors for detecting a depression-related neurotransmitter. We began this review with an overview of the microstructure, optical properties and cytotoxicity of CDs. Next, we introduced the development of synthetic methods of CDs, including the "Top-down" route and "Bottom-up" route. Finally, we highlighted detecting an application of CD-based electrochemical sensors in a depression-related neurotransmitter. Moreover, challenges and future perspectives on the recent progress of CD-based electrochemical sensors in depression-related neurotransmitter detection were discussed. Neurotransmitter ; Depression (economics) ; Neurotransmitter systems ; Nanotechnology ; Medicine ; Neuroscience ; Materials science ; Psychology ; Dopamine ; Central nervous system Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Ruben Van Lommel | Wim M. De Borggraeve | Frank De Proft | Mercedes Alonso
Source: Gels (review, 2021-07-09)
Field: Materials Science — Biomaterials

Computational Tools to Rationalize and Predict the Self-Assembly Behavior of Supramolecular Gels Supramolecular gels form a class of soft materials that has been heavily explored by the chemical community in the past 20 years. While a multitude of experimental techniques has demonstrated its usefulness when characterizing these materials, the potential value of computational techniques has received much less attention. This review aims to provide a complete overview of studies that employ computational tools to obtain a better fundamental understanding of the self-assembly behavior of supramolecular gels or to accelerate their development by means of prediction. As such, we hope to stimulate researchers to consider using computational tools when investigating these intriguing materials. In the concluding remarks, we address future challenges faced by the field and formulate our vision on how computational methods could help overcoming them. Supramolecular chemistry ; Multitude ; Computer science ; Nanotechnology ; Field (mathematics) ; Computational model ; Data science ; Class (philosophy) ; Management science ; Artificial intelligence ; Materials science ; Engineering ; Chemistry ; Molecule ; Epistemology Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Methane Hydrates and Related Phenomena