jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amir Abbas Kazemzadeh Farizhandi | Mahmood Mamivand
Source: Computational Materials Science (article, 2023-03-16)
Field: Materials Science — Materials Chemistry

Spatiotemporal prediction of microstructure evolution with predictive recurrent neural network Microstructure ; Computer science ; Spinodal decomposition ; Artificial neural network ; Materials science ; Phase (matter) ; Function (biology) ; Process (computing) ; Artificial intelligence ; Metallurgy ; Physics Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yasmeen AlFaraj | Somesh Mohapatra | Peyton Shieh | Keith E. L. Husted | Douglass G. Ivanoff | Evan M. Lloyd | Julian C. Cooper | Yutong Dai | Avni Singhal | Jeffrey S. Moore | Nancy R. Sottos | Rafael Gómez‐Bombarelli | Jeremiah A. Johnson
Source: ACS Central Science (article, 2023-09-14)
Field: Materials Science — Materials Chemistry

A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low-Data Regime Thermosets present sustainability challenges that could potentially be addressed through the design of deconstructable variants with tunable properties; however, the combinatorial space of possible thermoset molecular building blocks (e.g., monomers, cross-linkers, and additives) and manufacturing conditions is vast, and predictive knowledge for how combinations of these molecular components translate to bulk thermoset properties is lacking. Data science could overcome these problems, but computational methods are difficult to apply to multicomponent, amorphous, statistical copolymer materials for which little data exist. Here, leveraging a data set with 101 examples, we introduce a closed-loop experimental, machine learning (ML), and virtual screening strategy to enable predictions of the glass transition temperature (<i>T</i><sub>g</sub>) of polydicyclopentadiene (pDCPD) thermosets containing cleavable bifunctional silyl ether (BSE) comonomers and/or cross-linkers with varied compositions and loadings. Molecular features and formulation variables are used as model inputs, and uncertainty is quantified through model ensembling, which together with heavy regularization helps to avoid overfitting and ultimately achieves predictions within <15 °C for thermosets with compositionally diverse BSEs. This work offers a path to predicting the properties of thermosets based on their molecular building blocks, which may accelerate the discovery of promising plastics, rubbers, and composites with improved functionality and controlled deconstructability. Thermosetting polymer ; Overfitting ; Computer science ; Materials science ; Data-driven ; Biological system ; Monomer ; Artificial intelligence ; Polymer ; Composite material ; Artificial neural network Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties
Authors: Mayanak K. Gupta | S. Kumar | R. Mittal | S. K. Mishra | S. Rols | Olivier Delaire | Arumugum Thamizhavel | P. U. Sastry | S. L. Chaplot
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Distinct anharmonic characteristics of phonon-driven lattice thermal conductivity and thermal expansion in bulk MoSe<sub>2</sub> and WSe<sub>2</sub> Machine-learning molecular dynamics simulations pave the way to completely treat the anharmonicity of phonons. Low-energy anharmonic modes in transition-metal dichalcogenides drive the thermal and transport properties. Anharmonicity ; Phonon ; Condensed matter physics ; Thermal conductivity ; Lattice (music) ; Materials science ; Thermal ; Thermal expansion ; Physics ; Thermodynamics ; Composite material Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Valentine Chikaodili Anadebe | Vitalis Ikenna Chukwuike | Maduabuchi Arinzechukwu Chidiebere | Rakesh Chandra Barik
Source: The Journal of Physical Chemistry C (article, 2023-05-15)
Field: Materials Science — Materials Chemistry

Synthesis, Characterization, and Evaluation of Co-MOF Based ZIF-67 for CO<sub>2</sub> Corrosion Inhibition of X65 Steel: Insights from Electrochemical Studies and a Machine Learning Algorithm Co-MOF based metal organic framework was synthesized by reacting a metal ion (cobalt nitrate hexahydrate) with an organic ligand (2-methylimidazole) via a wet chemical method. The resulting material was characterized using detailed analytical methods and further was used as a self-assembly corrosion inhibitor in sweet corrosive environment. The empirical data set via electrochemical studies was modeled using adaptive neuro fuzzy inference system (ANFIS). The observed results showed that Co-MOF could significantly impede the corrosion rate of X65 steel and protect it from CO2 corrosion. Increasing the concentration of Co-MOF in the test solution increased the inhibition efficiency up to 97% at 0.1 wt % Co-MOF with a mixed-type inhibition mechanism. In addition, the DFT/MD-simulation approach evidenced the adsorption disposition of Co-MOF in aqueous and gas phase which complement with the empirical findings. Also, the prognostic capability of the proposed algorithm based on the statistical parameters such as root-mean-square error (RMSE), chi square (χ2), model predictive error (MPE) and coefficient of determination (R2) were appraised. From the viewpoint of statistics, the explanatory model aligned credibly with the ANFIS algorithm. The overall findings confirmed a dense hybrid coating of the synthesized Co-MOF on X65 steel as responsible for the inhibition of the sweet corrosion. Corrosion ; Electrochemistry ; Materials science ; Aqueous solution ; Adsorption ; Metal ; Chemical engineering ; Chemistry ; Metallurgy ; Physical chemistry ; Electrode Corrosion Behavior and Inhibition ; Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Yashwrdhan Pathaare | AR Reddy | Pavitra Sangrulkar | Balasubramanian Kandasubramanian | Anwesha Satapathy
Source: Hybrid Advances (article, 2023-05-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Carbon hybrid nano-architectures as an efficient electrode material for supercapacitor applications In the present world, the depletion of the chief resources of energy, like fossil fuels, has prompted researchers all over the world to produce energy storage devices that dispense electronic properties superior to existing technology like rechargeable batteries. The morphology of electrodes of such devices has been a critical factor in establishing a ceiling on storage capacities, evoking within researchers a strong interest in nano materials for the reason that they possess remarkably high surface area, and ion and electron conduction properties. Carbon in this aspect is found to be a particular case, owing to its supreme conductivity values and excellent mechanical stability in various forms. In this review, an analysis of all modifications performed on carbon nanostructures to increase their storage capacity has been reported, with special emphasis on the gravimetric capacitance, cyclic stability, and densities (energy and power) of the subsequently formed supercapacitors (SCs). Towards the end, a discussion is initiated upon newer computational methods based on machine learning and artificial intelligence, for discovering novel 2D materials which could be potential candidates in compositing with carbon to reveal enhanced storage performance of SCs. Supercapacitor ; Materials science ; Energy storage ; Nanotechnology ; Capacitance ; Carbon fibers ; Gravimetric analysis ; Electrode ; Power (physics) ; Composite material ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advancements in Battery Materials
Authors: Wabi Demeke | Byungki Ryu | Seunghwa Ryu
Source: Applied Energy (article, 2023-11-18)
Field: Materials Science — Materials Chemistry

Machine learning-based optimization of segmented thermoelectric power generators using temperature-dependent performance properties Segmented thermoelectric generators (STEGs) provide an excellent platform for thermal energy harvesting devices because they improve power generation performance across a broad range of operating temperatures. Despite the benefit of direct thermal energy-to-electricity conversion, conventional STEG optimization approaches are unable to provide a systematic method for selecting the optimal multiple stacks of p- and-n-type thermoelectric materials (TEs) legs from a set of numerous TE materials. In this study, we propose a systematic optimization method based on machine learning to find the optimal STEG for performance maximization. A deep neural network (DNN) is trained using an initial dataset generated via the Finite Element Method (FEM), with inputs including the temperature-dependent properties of p- and n-type TE materials, the lengths of each segment, and external loads, as well as the corresponding performance outputs. The trained DNN captures the inherent nonlinear relationship between these inputs and outputs. The combination of a trained DNN and a genetic algorithm (GA) efficiently navigates a vast design space using 88 p-type and 70 n-type TE materials along with device factors. It formulates four stacked segment pairs in p- and n-leg TEGs, targeting new superior designs with enhanced power, efficiency, or both. The DNN is iteratively refined via active learning (AL) by incorporating new superior STEG designs to enhance prediction accuracy. The optimized STEGs exhibit power and efficiency that are 1.91 and 1.5 times higher, respectively, than the top designs from the initial training dataset composed of 157.916 STEGs. Furthermore, compared to conventional TEG designs without segmentation, our method discovered numerous high-performing STEG designs. Thermoelectric generator ; Artificial neural network ; Maximization ; Power (physics) ; Computer science ; Range (aeronautics) ; Finite element method ; Electricity generation ; Maximum power principle ; Materials science ; Thermoelectric effect ; Artificial intelligence ; Mathematical optimization ; Mathematics ; Engineering ; Structural engineering ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Innovative Energy Harvesting Technologies
Authors: V E Sathishkumar | A.G. Ramu | Jaehyuk Cho
Source: Alexandria Engineering Journal (article, 2023-04-27)
Field: Chemistry — Organic Chemistry

Machine learning algorithms to predict the catalytic reduction performance of eco-toxic nitrophenols and azo dyes contaminants (Invited Article) Removing hazardous substances like azo dyes and nitrophenols from drinking water is essential for maintaining human health since these substances occur naturally in the environment. This research study used machine learning techniques to estimate the catalytic reduction performance of environmentally hazardous nitrophenols and azodyes pollutants. The catalyst PdO-NiO was used to eliminate contaminants in the water, including 4-nitrophenol (4-NP), 2,4-dinitrophenol (DNP), 2,4,6-trinitrophenol (TNP), methylene blue (MB), Rhodamine B (RHB), and Methyl Orange (MO). We conducted the experiments at different timings, and machine learning algorithms, including Linear Regression (LR), Support Vector Machines (SVM), Gradient boosted machines (GBM), Random forest (RF), and XGBTree (XGB), were used to predict the catalytic activity. The performance of these algorithms was measured using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results showed that the XGB algorithm performs best with NP and DNP. RF algorithm performs best with TNP, MB, and RHB, and the SVM algorithm performs best with MO. PdO-NiO bimetallic catalyst showed 98% reduction efficiency of azo compounds mixture within 8 min. Hence, we found PdO-NiO to be an efficient catalyst for real-site applications. Mean absolute error ; Mean squared error ; Support vector machine ; Catalysis ; Methyl orange ; Mean absolute percentage error ; Rhodamine B ; Algorithm ; Chemistry ; Non-blocking I/O ; Computer science ; Photocatalysis ; Environmental science ; Machine learning ; Mathematics ; Organic chemistry ; Statistics Nanomaterials for catalytic reactions
Authors: Zhiming Zhang | Hongjian Tang | Mao Wang | Bohui Lyu | Zhongyi Jiang | Jianwen Jiang
Source: ACS Sustainable Chemistry & Engineering (article, 2023-05-19)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening Atmospheric water harvesting based on metal–organic frameworks (MOFs) is an emerging technology to potentially mitigate water scarcity. Because of the tremendously large number of existing MOFs, it is challenging to find suitable candidates. In this context, a data-driven approach to identify top-performing MOFs represents an important direction. Herein, we develop a machine learning (ML) method to predict water adsorption in MOFs and screen out top-performing MOFs for water harvesting. First, experimental water adsorption isotherms in MOFs are collected and water adsorption properties are extracted. Quantitative structure–property relationships are analyzed in terms of pore structure and framework chemistry, providing task-specific design principles. Then, ML models are trained and interpreted to predict water adsorption properties by using structural and chemical features, as well as operating conditions as descriptors. The transferability of the ML models is validated by out-of-sample predictions in seven newly reported MOFs. Finally, the ML models are applied to screen ∼8000 “Computation-Ready, Experimental” (CoRE) MOFs. Top-performing candidates are identified including 149 MOFs with the maximum adsorption capacity ≥35 mmol/g, 39 MOFs with working capacity ≥10 mmol/g in a relative pressure window 0.1–0.3, and 139 MOFs with working capacity ≥8.7 mmol/g in a relative pressure window 0.6–0.9. The developed ML-based method would advance task-oriented design and rapid discovery of reticular materials for energy and environmental applications. Metal-organic framework ; Context (archaeology) ; Adsorption ; Computer science ; Transferability ; Process engineering ; Materials science ; Machine learning ; Chemistry ; Engineering ; Geology ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials
Authors: Shih-Yu Chen | Shih‐Hsun Hsu | Chih-Yi Ko | Kai-Hsun Hsu
Source: Food Control (article, 2023-03-15)
Field: Chemistry — Analytical Chemistry

Real-time defect and freshness inspection on chicken eggs using hyperspectral imaging Convolutional neural network ; Hyperspectral imaging ; Artificial intelligence ; Computer science ; Factory (object-oriented programming) ; Pattern recognition (psychology) ; Residual ; Artificial neural network ; Environmental science ; Computer vision Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality
Authors: Yiwen Zhu | Siyuan Chen | Hanzhe Yin | Xihao Han | Menghan Xu | Wenli Wang | Yin Zhang | Xiaoxiao Feng | Yuan Liu
Source: Journal of the Science of Food and Agriculture (article, 2023-10-16)
Field: Chemistry — Analytical Chemistry

Classification of oolong tea varieties based on computer vision and convolutional neural networks The study elucidates that the integration of computer vision with machine learning algorithms constitutes a promising, non-invasive approach for the quick and accurate categorization of oolong tea varieties. The findings have significant ramifications for process monitoring, quality assurance, authenticity validation and adulteration detection within the tea industry. © 2023 Society of Chemical Industry. Artificial intelligence ; Support vector machine ; Convolutional neural network ; Categorization ; Machine learning ; Computer science ; Robustness (evolution) ; Machine vision ; Traceability ; Artificial neural network ; Pattern recognition (psychology) ; Chemistry Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Advanced Chemical Sensor Technologies
Authors: Yican Huang | Zhengguang Chen | Jinming Liu
Source: Computers and Electronics in Agriculture (article, 2023-11-08)
Field: Chemistry — Analytical Chemistry

Limited agricultural spectral dataset expansion based on generative adversarial networks Artificial intelligence ; Mahalanobis distance ; Computer science ; Deep learning ; Machine learning ; Boundary (topology) ; Mathematics ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Clayton Cooper | Jianjing Zhang | Joshua Huang | Jennifer Bennett | Jian Cao | Robert X. Gao
Source: Journal of Materials Processing Technology (article, 2023-02-10)
Field: Materials Science — Materials Chemistry

Tensile strength prediction in directed energy deposition through physics-informed machine learning and Shapley additive explanations Interpretability ; Ultimate tensile strength ; Feature (linguistics) ; Artificial neural network ; Computer science ; Artificial intelligence ; Property (philosophy) ; Characterization (materials science) ; Metric (unit) ; Machine learning ; Energy (signal processing) ; Materials science ; Mathematics ; Composite material ; Engineering ; Statistics Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Sulagna Sarkar | Anqi Ji | Zachary Jermain | Robert Lipton | Mark L. Brongersma | Kaushik Dayal | Hae Young Noh
Source: Advanced Photonics Research (article, 2023-10-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill‐posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics‐informed deep learning (DL) framework is used. A tandem DL architecture with physics‐based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics‐based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%. Metamaterial ; Computation ; Inverse ; Inverse problem ; Optics ; Physics ; Wavelength ; Computer science ; Electromagnetic spectrum ; Focus (optics) ; Algorithm ; Mathematics ; Mathematical analysis ; Geometry Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Photonic Crystals and Applications
Authors: Navyanth Kusampudi | Martin Diehl
Source: International Journal of Plasticity (article, 2023-10-16)
Field: Materials Science — Materials Chemistry

Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization Autoencoder ; Microstructure ; Materials science ; Artificial intelligence ; Bayesian inference ; Bayesian optimization ; Generative model ; Machine learning ; Computer science ; Algorithm ; Bayesian probability ; Pattern recognition (psychology) ; Mathematical optimization ; Generative grammar ; Artificial neural network ; Mathematics ; Composite material Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Afsaneh Orouji | Forough Ghasemi | M. Reza Hormozi‐Nezhad
Source: Analytical Chemistry (article, 2023-06-20)
Field: Chemistry — Electrochemistry

Machine Learning-Assisted Colorimetric Assay Based on Au@Ag Nanorods for Chromium Speciation The oxidation state of an element significantly controls its toxicological impacts on biological ecosystems. Therefore, design of robust sensing strategies for multiplex detection of species with respect to their oxidation states or bonding conditions, i.e., chemical speciation, is quite consequential. Chromium (Cr) species are known as the most abundant inorganic groundwater pollutants and can be quite harmful to human health depending on their oxidation states. In the present study, a multicolorimetric probe based on silver-deposition-induced color variations of gold nanorods (AuNRs) was designed for identification and quantification of Cr species including Cr (III) and Cr (VI) (i.e., CrO<sub>4</sub><sup>2-</sup> and Cr<sub>2</sub>O<sub>7</sub><sup>2-</sup>) in water samples. In fact, the presence of Cr species leads to inhibition of the silver metallization of AuNRs to various degrees depending on the concentration and identity of the analyte. This process is accompanied by the blue shift of the longitudinal peak which results in sharp-contrast rainbow-like color variations, thereby providing great opportunity for highly accurate visual detection. The gathered dataset was then statistically analyzed using two pattern recognition and regression machine learning techniques. In particular, linear discriminant analysis was used as a classification method to discriminate the unicomponent and mixture samples of Cr species with 100% accuracy. Then, a well-known multivariate calibration technique called partial least-squares regression was employed for quantitative analysis of Cr species. Responses were linearly related to Cr species concentrations over a wide range of 10.0-1000.0, 1.0-200.0, and 1.0-200.0 μmol L<sup>-1</sup> with detection limits of 37.7, 8.7, and 2.9 μmol L<sup>-1</sup> for Cr<sup>3+</sup>, CrO<sub>4</sub><sup>2-</sup>, and Cr<sub>2</sub>O<sub>7</sub><sup>2-</sup>, respectively. The practical applicability of the multicolorimetric probe was successfully evaluated by analyzing Cr species in several water specimens comprising tap water, mineral water, river water, and seawater. Above all, the vivid rainbow color tonality of the proposed assay further improves the accuracy of the naked eye detection, making it a practical platform for on-site monitoring of Cr contamination. Chemistry ; Chromium ; Analyte ; Genetic algorithm ; Partial least squares regression ; Nanorod ; Analytical Chemistry (journal) ; Environmental chemistry ; Linear discriminant analysis ; Biological system ; Artificial intelligence ; Nanotechnology ; Chromatography ; Ecology ; Machine learning Electrochemical Analysis and Applications ; Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors