jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiandong Guo | Yu Gao | Mengyao Pan | Xiaobai Li | Fanwei Kong | Mingyang Wu | Lijia Zhang | Zhiyong Cheng | Zhao Rui-yan | Hongwei Ma
Source: ACS Applied Materials & Interfaces (article, 2024-04-29)
Field: Materials Science — Materials Chemistry

Photorewriting, Time-Resolved Encryption, and Unclonable Anticounterfeiting with Artificial Intelligence Authentication via a Reversible Photoswitchable System In the fields of photolithographic patterning, optical anticounterfeiting, and information encryption, reversible photochromic materials with solid-state fluorescence are emerging as a potential class of systems. A design strategy for reversible photochromic materials has been proposed and synthesized through the introduction of photoactive thiophene groups into the molecular backbone of aryl vinyls, compounds with unique aggregation-induced emission properties, and solid-state reversible photocontrollable fluorescence and color-changing properties. This work develops novel photochromic inks, films, and cellulose hydrogels for enhancing the security of information encryption and anticounterfeiting technologies. They achieve rapid and reversible color change under ultraviolet light irradiation. Dependent upon the rate of color change, higher levels of time-resolved security can be achieved. This feature is important for enhancing the confidentiality of encrypted information and the reliability of security labels. Color-changing cellulose hydrogels, inks, and films consisting of three photochromic fluorescent molecules have quick photoactivity, great photoreversibility and photostability, and good processability, making them ideal for time-delayed anticounterfeiting and smart encryption. Furthermore, specialized algorithms are used to construct convolutional neural networks, and image analysis is performed on these systems, thus solving the current problem of the time-consuming information decryption process. This artificial intelligence method offers new opportunities for enhanced data encryption. Photochromism ; Encryption ; Materials science ; Naked eye ; Computer science ; Fluorescence ; Photochemistry ; Nanotechnology ; Computer security ; Chemistry ; Optics Photochromic and Fluorescence Chemistry ; Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection
Authors: Mani Jayakumar | Venkatesa Prabhu Sundramurthy | Kaleab Bizuneh Gebeyehu | Kuppusamy Vaithilingam Selvakumar | Abdi Nemera Emana | Subramanian Manivannan | Sugumar Mohanasundaram | Suresh Sagadevan | G. Baskar
Source: Desalination and Water Treatment (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Artificial neural network guided optimization of limiting factors for enhancing photocatalytic treatment of textile wastewater using UV/TiO₂ and kinetic studies Wastewater effluents discharged from textile industries are characterized by excessive chemical and biochemical oxygen demands with significant amounts of harmful synthetic dyes that spoil the healthy environment. The present investigation focused on process optimization to develop an effective strategy for degrading and decolorizing wastewater from textile industries through an advanced photocatalysis oxidation process. The synergistic effect of Titanium dioxide (TiO 2 ) nanoparticles as a photocatalyst with ultraviolet irradiation was applied as a novel technique to detoxify wastewater effluents from textile industries. In addition, the process was modeled and optimized using response surface methodology (RSM) and artificial neural networks (ANN). The kinetics of the photocatalytic degradation of wastewater effluents from textile industries were analyzed. The optimal condition predicted by the RSM was similar to the experimental outcomes, further, the predicted values from the ANN model had confirmed the prediction. The most significant limiting parameters, such as catalyst dosage, pH, and irradiation time, were optimized systematically as TiO 2 dosage of 429.31 mg/l, pH of 8.89, and irradiation time of 5 h, respectively. Under these optimal conditions, COD reduction and decolorization of 90 and 88%, respectively, were achieved. Limiting ; Photocatalysis ; Textile ; Artificial neural network ; Wastewater ; Kinetic energy ; Sewage treatment ; Materials science ; Pulp and paper industry ; Biochemical engineering ; Process engineering ; Waste management ; Chemistry ; Computer science ; Engineering ; Composite material ; Catalysis ; Artificial intelligence ; Organic chemistry ; Physics ; Mechanical engineering Nanoparticles: synthesis and applications ; TiO2 Photocatalysis and Solar Cells ; Advanced Nanomaterials in Catalysis
Authors: Hwanseok Chang | Sung‐Joo Kwon | Gwangmin Bae | Seokwoo Jeon
Source: Nanophotonics (article, 2024-02-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rational design of arbitrary topology in three‐dimensional space <i>via</i> inverse calculation of phase modulation Recent advances in nanotechnology have led to the emergence of metamaterials with unprecedented properties through precisely controlled topologies. To explore metamaterials with nanoscale topologies, interest in three-dimensional nanofabrication methods has grown and led to rapid production of target nanostructures over large areas. Additionally, inverse design methods have revolutionized materials science, enabling the optimization of microstructures and topologies to achieve the desired properties without extensive experimental cycles. This review highlights the recent progress in inverse design methods applied in proximity-field nanopatterning. It introduces novel approaches, such as adjoint methods and particle swarm optimization, to achieve target topologies and high-resolution nanostructures. Furthermore, machine learning algorithms for inverse design are explored, demonstrating the potential efficacy of the phase-mask design. This comprehensive review offers insights into the progress of inverse design using phase modulation to realize target topologies of nanostructures. Network topology ; Computer science ; Metamaterial ; Topology optimization ; Inverse ; Topology (electrical circuits) ; Inverse problem ; Nanotechnology ; Electronic engineering ; Materials science ; Mathematics ; Physics ; Engineering ; Electrical engineering ; Finite element method Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Polydiacetylene-based materials and applications
Authors: Zhicheng Li | Jing Zhong | Jinliang Zhang | Bo Song | Bing Liu | Yusheng Shi | Lijun Zhang
Source: Additive manufacturing (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Toward additive manufactured crack-free NiTiCu shape memory alloys with low-hysteresis and tailorable phase transition behavior supported by theoretical design Materials science ; Hysteresis ; Phase transition ; Shape-memory alloy ; Composite material ; Phase (matter) ; Condensed matter physics ; Metallurgy Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Creativity in Education and Neuroscience
Authors: Luca Rettenberger | Nathan J. Szymanski | Yan Zeng | Jan Schuetzke | Shilong Wang | Gerbrand Ceder | Markus Reischl
Source: npj Computational Materials (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

Uncertainty-aware particle segmentation for electron microscopy at varied length scales Abstract Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization. Segmentation ; Computer science ; Artificial intelligence ; Characterization (materials science) ; Scale (ratio) ; Workflow ; Particle (ecology) ; Image segmentation ; Process (computing) ; Magnification ; Pattern recognition (psychology) ; Materials science ; Nanotechnology ; Cartography ; Geography ; Geology ; Database Machine Learning in Materials Science ; Mineral Processing and Grinding ; Electron and X-Ray Spectroscopy Techniques
Authors: Chunyu Yang | Kai Zhu | Bing Yan
Source: ACS Applied Materials & Interfaces (article, 2024-04-10)
Field: Chemistry — Inorganic Chemistry

Efficient Multi-stimulus-Responsive Luminescent Eu(III)-Modified HOFs Materials: Detecting Thiram and Caffeic Acid and Constructing a Flexible Substrate Anti-counterfeiting Platform The powerful capability of multi-stimulus-responsive luminescent hydrogen-bonded organic frameworks (HOFs) to respond to external chemical or physical stimuli in various manners makes them appealing in the luminescence anti-counterfeiting field. Herein, a novel Eu<sup>3+</sup>-functionalized HOF (Eu@GC-2) that combines the emission of HOFs with the characteristic emission of Eu<sup>3+</sup> ions has been successfully synthesized, which can generate various fluorescence at different excitation wavelengths. Eu@GC-2 has enormous potential as a raw material for a paper-based sensor that is designed for detecting the pesticides thiram and caffeic acid in crops with favorable selectivity, anti-interference, and high efficiency. Based on the above excellent properties, Ln<sup>3+</sup>-functionalized HOFs (Ln@GC-2) were then employed to produce four luminescent anti-counterfeiting inks. With the incorporation of back-propagation neural network and Gray code conversion functions, a multi-stimulus-responsive luminescent anti-counterfeiting platform, coregulated by the excitation light and the chemical reagent, has been constructed. This approach can not only achieve multiple encryptions and fast information identification but also enhance the code-breaking complexity, making it an efficient strategy for information encryption and decryption. Materials science ; Luminescence ; Nanotechnology ; Stimulus (psychology) ; Optoelectronics Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Lanthanide and Transition Metal Complexes
Authors: María Judit Montes de Oca‐Estévez | Álvaro Valdés | Rita Prosmiti
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A kernel-based machine learning potential and quantum vibrational state analysis of the cationic Ar hydride (Ar<sub>2</sub>H<sup>+</sup>) One of the most fascinating discoveries in recent years, in the cold and low pressure regions of the universe, was the detection of ArH<sup>+</sup> and HeH<sup>+</sup> species. The identification of such noble gas-containing molecules in space is the key to understanding noble gas chemistry. In the present work, we discuss the possibility of [Ar<sub>2</sub>H]<sup>+</sup> existence as a potentially detectable molecule in the interstellar medium, providing new data on possible astronomical pathways and energetics of this compound. As a first step, a data-driven approach is proposed to construct a full 3D machine-learning potential energy surface (ML-PES) <i>via</i> the reproducing kernel Hilbert space (RKHS) method. The training and testing data sets are generated from CCSD(T)/CBS[56] computations, while a validation protocol is introduced to ensure the quality of the potential. In turn, the resulting ML-PES is employed to compute vibrational levels and molecular spectroscopic constants for the cation. In this way, the most common isotopologue in ISM, [<sup>36</sup>Ar<sub>2</sub>H]<sup>+</sup>, was characterized for the first time, while simultaneously, comparisons with previously reported values available for [<sup>40</sup>Ar<sub>2</sub>H]<sup>+</sup> are discussed. Our present data could serve as a benchmark for future studies on this system, as well as on higher-order cationic Ar-hydrides of astrophysical interest. Flowchart ; Cationic polymerization ; Kernel (algebra) ; Quantum chemical ; Quantum ; Chemistry ; Hydride ; State (computer science) ; Computer science ; Computational chemistry ; Atomic physics ; Artificial intelligence ; Physics ; Algorithm ; Mathematics ; Quantum mechanics ; Discrete mathematics ; Organic chemistry ; Molecule ; Programming language Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Shuyang Zhang | Jin Xia | Keyu Chen | Dawei Zhang
Source: Journal of Building Engineering (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

A hybrid model based on LSTM-CNN combined with attention mechanism for MPC concrete strength prediction Mechanism (biology) ; Computer science ; Artificial intelligence ; Physics Magnesium Oxide Properties and Applications ; Concrete and Cement Materials Research ; Innovative concrete reinforcement materials
Authors: Wei Yang | Jisheng Zou | Xuan Zhang | Yaowen Chen | Hanjing Tang | Gang Xiao | Xiaolei Zhang
Source: Frontiers in Neuroscience (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

An end-to-end LSTM-Attention based framework for quasi-steady-state CEST prediction Chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) often takes prolonged saturation duration (Ts) and relaxation delay (Td) to reach the steady state, and yet the insufficiently long Ts and Td in actual experiments may underestimate the CEST measurement. In this study, we aimed to develop a deep learning-based model for quasi-steady-state (QUASS) prediction from non-steady-state CEST acquired in experiments, therefore overcoming the limitation of the CEST effect which needs prolonged saturation time to reach a steady state. To support network training, a multi-pool Bloch-McConnell equation was designed to derive wide-ranging simulated Z-spectra, so as to solve the problem of time and labor consumption in manual annotation work. Following this, we formulated a hybrid architecture of long short-term memory (LSTM)-Attention to improve the predictive ability. The multilayer perceptron, recurrent neural network, LSTM, gated recurrent unit, BiLSTM, and LSTM-Attention were included in comparative experiments of QUASS CEST prediction, and the best performance was obtained by the proposed LSTM-Attention model. In terms of the linear regression analysis, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE), the results of LSTM-Attention demonstrate that the coefficient of determination in the linear regression analysis was at least <i>R</i><sup>2</sup> = 0.9748 for six different representative frequency offsets, the mean values of prediction accuracies in terms of SSIM, PSNR and MSE were 0.9991, 49.6714, and 1.68 × 10<sup>-4</sup> for all frequency offsets. It was concluded that the LSTM-Attention model enabled high-quality QUASS CEST prediction. Computer science ; Mean squared error ; Artificial intelligence ; Linear regression ; Multilayer perceptron ; Pattern recognition (psychology) ; Recurrent neural network ; Artificial neural network ; Perceptron ; Regression ; Machine learning ; Statistics ; Mathematics Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Ru He | J. Zhao | Jesper Byggmästar | Huan He | Flyura Djurabekova
Source: Physical Review Materials (preprint, 2024-08-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrahigh stability of oxygen sublattice in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>β</mml:mi><mml:mtext>−</mml:mtext><mml:msub><mml:mi>Ga</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> Recently reported remarkably high radiation tolerance of $\ensuremath{\gamma}/\ensuremath{\beta}\text{\ensuremath{-}}{\mathrm{Ga}}_{2}{\mathrm{O}}_{3}$ double-polymorphic structure brings this ultrawide-band-gap semiconductor to the frontiers of power electronics applications that are able to operate in challenging environments. Understanding the mechanism of radiation tolerance is crucial for further material modification and tailoring of the desired properties. In this study, we employ machine-learning-enhanced atomistic simulations to assess the stability of both the gallium ($\mathrm{Ga}$) and oxygen ($\mathrm{O}$) sublattices under various levels of damage. Our study uncovers the remarkable resilience and stability of the -sublattice, attributing this property to the strong tendency of recovery of the $\mathrm{O}$ defects, especially within the more strongly disordered regions. Interestingly, we observe the opposite behavior of the $\mathrm{Ga}$ defects that display enhanced stability in the same regions of increased disorder. Moreover, we observe that highly defective $\ensuremath{\beta}\text{\ensuremath{-}}{\mathrm{Ga}}_{2}{\mathrm{O}}_{3}$ is able to transform into $\ensuremath{\gamma}\text{\ensuremath{-}}{\mathrm{Ga}}_{2}{\mathrm{O}}_{3}$ upon annealing due to preserved lattice organization of the $\mathrm{O}$ sublattice. This result clearly manifests that the ultrahigh stability of the $\mathrm{O}$ sublattice provides the backbone for the exceptional radiation tolerance of the $\ensuremath{\gamma}/\ensuremath{\beta}$ double-polymorphic structure. These computational insights closely align with experimental observations, opening avenues for further exploration of polymorphism in ${\mathrm{Ga}}_{2}{\mathrm{O}}_{3}$ and potentially in analogous polymorphic families spanning a broad range of diverse materials of complex polymorphic nature. BETA (programming language) ; Stability (learning theory) ; Chemistry ; Materials science ; Crystallography ; Mathematics ; Computer science ; Programming language Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; Magnesium Oxide Properties and Applications
Authors: Changhwan Oh | Aditya Nandy | Shuwen Yue | Heather J. Kulik
Source: ACS Applied Materials & Interfaces (article, 2024-10-04)
Field: Chemistry — Inorganic Chemistry

MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules Metal-organic frameworks (MOFs) have been widely studied for their ability to capture and store greenhouse gases. However, most computational discovery efforts study hypothetical MOFs without consideration of their stability, limiting the practical application of novel materials. We overcome this limitation by screening hypothetical ultrastable MOFs that have predicted high thermal and activation stability, as judged by machine learning (ML) models trained on experimental measures of stability. We enhance this set by computing the bulk modulus as a measure of mechanical stability and filter 1102 mechanically robust hypothetical MOFs from a database of ultrastable MOFs (USMOF DB). Grand Canonical Monte Carlo simulations are then employed to predict the gas adsorption properties of these hypothetical MOFs, alongside a database of experimental MOFs. We identify privileged building blocks that lead MOFs in USMOF DB to show exceptional working capacities compared to the experimental MOFs. We interpret these differences by training ML models on CO<sub>2</sub> and CH<sub>4</sub> adsorption in these databases, showing how poor model transferability between data sets indicates that novel design rules can be derived from USMOF DB that would not have been gathered through assessment of structurally characterized MOFs. We identify geometric features and node chemistry that will enable the rational design of MOFs with enhanced gas adsorption properties in synthetically realizable MOFs. Materials science ; Adsorption ; Nanotechnology ; Stability (learning theory) ; Computer science ; Physical chemistry ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Industrial Gas Emission Control
Authors: Thibault Charpentier
Source: Faraday Discussions (article, 2024-06-26)
Field: Materials Science — Ceramics and Composites

First-principles NMR of oxide glasses boosted by machine learning Solid-state NMR has established itself as a cutting-edge spectroscopy for elucidating the structure of oxide glasses thanks to several decades of methodological and instrumental progress. First-principles calculations of NMR properties combined with molecular-dynamics (MD) simulations provides a powerful complementary approach for the interpretation of NMR data, although they still suffer from limitations in terms of size, time and high consumption of computational resources. We address this challenge by developing a machine-learning framework to boost predictive modelling of NMR spectra. We use kernel ridge regression techniques (least-squares support vector regression and linear ridge regression) combined with smooth overlap of atomic position (SOAP) atom-centered descriptors to efficiently predict NMR interactions: the isotropic magnetic shielding and the electric field gradient (EFG) tensor. As illustrated in this work, this approach enables the simulation of magic-angle spinning (MAS) and multiple-quantum magic-angle spinning (MQMAS) NMR spectra of very large models (more than 10 000 atoms) and an efficient averaging of NMR properties over MD trajectories of nanoseconds for incorporating finite-temperature effects, at the computational cost of classical MD simulations. We illustrate these advances for sodium silicate glasses (SiO<sub>2</sub>-Na<sub>2</sub>O). NMR parameters (isotropic chemical shift and electric field gradient) could be predicted with an accuracy of 1 to 2% in terms of the total span of the NMR parameter values. To include vibrational effects, an approach is proposed of scaling the EFG tensor in NMR simulations with a factor obtained from the time auto-correlation functions computed on MD trajectories. Oxide ; Materials science ; Chemistry ; Nanotechnology ; Metallurgy Glass properties and applications ; Catalysis and Oxidation Reactions ; Advanced NMR Techniques and Applications