jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joohyouck Park | Nayoung Kim | Sang Yeong Han | Su Yeon Rhee | Duc Tai Nguyen | Hojae Lee | Insung S. Choi
Source: Advanced Science (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

A Micrometric Transformer: Compositional Nanoshell Transformation of Fe<sup>3+</sup>‐Trimesic‐Acid Complex with Concomitant Payload Release in Cell‐in‐Catalytic‐Shell Nanobiohybrids Nanoencapsulation of living cells within artificial shells is a powerful approach for augmenting the inherent capacity of cells and enabling the acquisition of extrinsic functions. However, the current state of the field requires the development of nanoshells that can dynamically sense and adapt to environmental changes by undergoing transformations in form and composition. This paper reports the compositional transformation of an enzyme-embedded nanoshell of Fe<sup>3+</sup> -trimesic acid complex to an iron phosphate shell in phosphate-containing media. The cytocompatible transformation allows the nanoshells to release functional molecules without loss of activities and biorecognition, while preserving the initial shell properties, such as cytoprotection. Demonstrations include the lysis and killing of Escherichia coli by lysozyme, and the secretion of interleukin-2 by Jurkat T cells in response to paracrine stimulation by antibodies. This work on micrometric Transformers will benefit the creation of cell-in-shell nanobiohybrids that can interact with their surroundings in active and adaptive ways. Nanoshell ; Nanotechnology ; Materials science ; Biophysics ; Chemistry ; Chemical engineering ; Nanoparticle ; Biology Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Jong Jin Park | Jeong‐Seok Cho | Gyuseok Lee | Dae-Yong Yun | Seulki Park | Kee Jai Park | Jeong‐Ho Lim
Source: Foods (article, 2023-09-18)
Field: Chemistry — Analytical Chemistry

Detection of Red Pepper Powder Adulteration with Allura Red and Red Pepper Seeds Using Hyperspectral Imaging This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), or 0:100 (P0), and Allura Red was added to the red pepper pericarp/seed mixture at 0.05% (A), 0.1% (B), and 0.15% (C). The results of principal component analysis (PCA) using the L, a, and b values; hue angle; and chroma showed that the pure pericarp powder (P100) was not easily distinguished from some adulterated samples (P50A-C, P75A-C, and P100B,C). Adulterated red pepper powder was detected by applying machine learning techniques, including linear discriminant analysis (LDA), linear support vector machine (LSVM), and k-nearest neighbor (KNN), based on spectra obtained from SWIR (1,000-1,700 nm). Linear discriminant analysis determined adulteration with 100% accuracy when the samples were divided into four categories (acceptable, adulterated by Allura Red, adulterated by seeds, and adulterated by seeds and Allura Red). The application of SWIR technology and machine learning detects adulteration with Allura Red and seeds in red pepper powder. Pepper ; Food science ; Hue ; Red soil ; Chemistry ; Horticulture ; Mathematics ; Biology ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Xiaoyue Zhu | Chao Qian | Jie Zhang | Yuetian Jia | Yaxiong Xu | Mingmin Zhao | Minjian Zhao | Fengzhong Qu | Hongsheng Chen
Source: Nanophotonics (article, 2023-11-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On‐demand Doppler‐offset beamforming with intelligent spatiotemporal metasurfaces Recently, significant efforts have been devoted to guaranteeing high-quality communication services in fast-moving scenes, such as high-speed trains. The challenges lie in the Doppler effect that shifts the frequency of the transmitted signal. To this end, the recent emergence of spatiotemporal metasurfaces offers a promising solution, which can manipulate electromagnetic waves in time and space domain while being lightweight and cost-effective. Here we introduce deep learning-assisted spatiotemporal metasurfaces to automatically and adaptively neutralize Doppler effect in fast-moving situations. A tandem neural network is used to establish a rapid connection between on-site targets and time-varying series of spatiotemporal metasurfaces, endowing the capability of on-demand beamforming with Doppler effects offset. Moreover, oblique incidence problems are also studied in practice, which can be used for relieving multipath effect. In the microwave experiment, we fabricate the intelligent spatiotemporal metasurfaces and demonstrate the potential to fulfill Doppler-offset beamforming under oblique incidence. Beamforming ; Computer science ; Offset (computer science) ; Multipath propagation ; Doppler effect ; Electronic engineering ; Acoustics ; Telecommunications ; Channel (broadcasting) ; Engineering ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Mukhtar Lawan Adam | Oyawale Adetunji Moses | Jonathan P. Mailoa | Chang‐Yu Hsieh | Xue‐Feng Yu | Hao Li | Haitao Zhao
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Navigating Materials Chemical Space to Discover New Battery Electrodes Using Machine Learning Battery (electricity) ; Chemical space ; Space (punctuation) ; Computer science ; Artificial intelligence ; Chemistry ; Physics ; Operating system Machine Learning in Materials Science ; Text and Document Classification Technologies ; Fuel Cells and Related Materials
Authors: Kazuya Hiraide | Yutaka Oya | Misato Suzuki | Mayu Muramatsu
Source: Materials Today Communications (article, 2023-10-07)
Field: Materials Science — Materials Chemistry

Inverse design of polymer alloys using deep learning based on self-consistent field analysis and finite element analysis Finite element method ; Materials science ; Realization (probability) ; Inverse ; Phase (matter) ; Field (mathematics) ; Inverse problem ; Deep learning ; Convolutional neural network ; Computer science ; Artificial neural network ; Artificial intelligence ; Algorithm ; Structural engineering ; Mathematics ; Geometry ; Mathematical analysis ; Physics ; Engineering Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Probabilistic and Robust Engineering Design
Authors: Gabrielle Coelho Lelis | Wilson T. Fonseca | Alessandro Henrique de Lima | Anderson K. Okazaki | Eduardo Costa Figueiredo | Antônio Riul | Gabriel R. Schleder | Paolo Samorı́ | Rafael Furlan de Oliveira
Source: ACS Applied Materials & Interfaces (article, 2023-12-22)
Field: Chemistry — Analytical Chemistry

Harnessing Small-Molecule Analyte Detection in Complex Media: Combining Molecularly Imprinted Polymers, Electrolytic Transistors, and Machine Learning Small-molecule analyte detection is key for improving quality of life, particularly in health monitoring through the early detection of diseases. However, detecting specific markers in complex multicomponent media using devices compatible with point-of-care (PoC) technologies is still a major challenge. Here, we introduce a novel approach that combines molecularly imprinted polymers (MIPs), electrolyte-gated transistors (EGTs) based on 2D materials, and machine learning (ML) to detect hippuric acid (HA) in artificial urine, being a critical marker for toluene intoxication, parasitic infections, and kidney and bowel inflammation. Reduced graphene oxide (rGO) was used as the sensory material and molecularly imprinted polymer (MIP) as supramolecular receptors. Employing supervised ML techniques based on symbolic regression and compressive sensing enabled us to comprehensively analyze the EGT transfer curves, eliminating the need for arbitrary signal selection and allowing a multivariate analysis during HA detection. The resulting device displayed simultaneously low operating voltages (<0.5 V), rapid response times (≤10 s), operation across a wide range of HA concentrations (from 0.05 to 200 nmol L<sup>-1</sup>), and a low limit of detection (LoD) of 39 pmol L<sup>-1</sup>. Thanks to the ML multivariate analysis, we achieved a 2.5-fold increase in the device sensitivity (1.007 μA/nmol L<sup>-1</sup>) with respect to the human data analysis (0.388 μA/nmol L<sup>-1</sup>). Our method represents a major advance in PoC technologies, by enabling the accurate determination of small-molecule markers in complex media via the combination of ML analysis, supramolecular analyte recognition, and electrolytic transistors. Materials science ; Analyte ; Molecularly imprinted polymer ; Nanotechnology ; Polymer ; Transistor ; Small molecule ; Molecular imprinting ; Molecule ; Electrolyte ; Combinatorial chemistry ; Optoelectronics ; Chromatography ; Selectivity ; Organic chemistry ; Electrode ; Electrical engineering ; Catalysis ; Voltage ; Composite material Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications ; Electrochemical Analysis and Applications
Authors: Jian Wu | Jia Zhang | Guangren Qian | Tong‐Yi Zhang
Source: Applied Catalysis A General (article, 2023-11-01)
Field: Materials Science — Materials Chemistry

The design and discovery of catalysts for simultaneous catalysis of chlorobenzene and nitrogen oxides via domain knowledge guided machine learning Chlorobenzene ; Catalysis ; Redox ; Support vector machine ; Chemistry ; Ion ; Metal ; Metal ions in aqueous solution ; Inorganic chemistry ; Computer science ; Machine learning ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Laura Zichi | Tianci Liu | Elizabeth Drueke | Liuyan Zhao | Gongjun Xu
Source: Scientific Reports (article, 2023-04-15)
Field: Materials Science — Materials Chemistry

Physically informed machine-learning algorithms for the identification of two-dimensional atomic crystals After graphene was first exfoliated in 2004, research worldwide has focused on discovering and exploiting its distinctive electronic, mechanical, and structural properties. Application of the efficacious methodology used to fabricate graphene, mechanical exfoliation followed by optical microscopy inspection, to other analogous bulk materials has resulted in many more two-dimensional (2D) atomic crystals. Despite their fascinating physical properties, manual identification of 2D atomic crystals has the clear drawback of low-throughput and hence is impractical for any scale-up applications of 2D samples. To combat this, recent integration of high-performance machine-learning techniques, usually deep learning algorithms because of their impressive object recognition abilities, with optical microscopy have been used to accelerate and automate this traditional flake identification process. However, deep learning methods require immense datasets and rely on uninterpretable and complicated algorithms for predictions. Conversely, tree-based machine-learning algorithms represent highly transparent and accessible models. We investigate these tree-based algorithms, with features that mimic color contrast, for automating the manual inspection process of exfoliated 2D materials (e.g., MoSe<sub>2</sub>). We examine their performance in comparison to ResNet, a famous Convolutional Neural Network (CNN), in terms of accuracy and the physical nature of their decision-making process. We find that the decision trees, gradient boosted decision trees, and random forests utilize physical aspects of the images to successfully identify 2D atomic crystals without suffering from extreme overfitting and high training dataset demands. We also employ a post-hoc study that identifies the sub-regions CNNs rely on for classification and find that they regularly utilize physically insignificant image attributes when correctly identifying thin materials. Computer science ; Identification (biology) ; Machine learning ; Artificial intelligence ; Algorithm ; Biology Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Electronic and Structural Properties of Oxides
Authors: Xiuyang Fang | Jian-En Gong | Feng Zhang | Haonan Zhang | Jiahong Wu
Source: Engineering Fracture Mechanics (article, 2023-09-09)
Field: Materials Science — Materials Chemistry

Machine learning assisted materials design of high-speed railway wheel with better fatigue performance Pearlite ; Materials science ; Artificial neural network ; Microstructure ; Crystal (programming language) ; Fracture (geology) ; Structural engineering ; Composite material ; Mechanical engineering ; Austenite ; Machine learning ; Computer science ; Engineering Metal Alloys Wear and Properties ; Mechanical Engineering and Vibrations Research ; Gear and Bearing Dynamics Analysis
Authors: Vishu Gupta | Alec Peltekian | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source: Scientific Reports (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Improving deep learning model performance under parametric constraints for materials informatics applications Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. While a deep neural network comprised of fully connected layers has been widely used for materials property prediction, simply creating a deeper model with a large number of layers often faces with vanishing gradient problem, causing a degradation in the performance, thereby limiting usage. In this paper, we study and propose architectural principles to address the question of improving the performance of model training and inference under fixed parametric constraints. Here, we present a general deep-learning framework based on branched residual learning (BRNet) with fully connected layers that can work with any numerical vector-based representation as input to build accurate models to predict materials properties. We perform model training for materials properties using numerical vectors representing different composition-based attributes of the respective materials and compare the performance of the proposed models against traditional ML and existing DL architectures. We find that the proposed models are significantly more accurate than the ML/DL models for all data sizes by using different composition-based attributes as input. Further, branched learning requires fewer parameters and results in faster model training due to better convergence during the training phase than existing neural networks, thereby efficiently building accurate models for predicting materials properties. Computer science ; Machine learning ; Artificial intelligence ; Deep learning ; Parametric statistics ; Process (computing) ; Artificial neural network ; Inference ; Property (philosophy) ; Residual ; Representation (politics) ; Convergence (economics) ; Limiting ; Parametric model ; Data mining ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Hisham Alghamdi | Chika Maduabuchi | Kingsley Okoli | Abdullah Albaker | Ibrahim Alatawi | Ahmed S. Alsafran | Mohammad Alkhedher | Wei‐Hsin Chen
Source: International Journal of Energy Research (article, 2023-07-07)
Field: Materials Science — Materials Chemistry

Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks In the relentless pursuit of sustainable energy solutions, this study pioneers an innovative approach to integrating thermoelectric generators (TEGs) and photovoltaic (PV) modules within hybrid systems. Uniquely, it employs neural networks for an exhaustive analysis of a plethora of parameters, including a diverse spectrum of semiconductor materials, cooling film coefficients, TE leg dimensions, ambient temperature, wind speed, and PV emissivity. Leveraging a rich dataset, the neural network is meticulously trained, revealing intricate interdependencies among parameters and their consequential impact on power generation and the efficiencies of TEG, PV, and integrated PV-TE systems. Notably, the hybrid system witnesses a striking 23.1% augmentation in power output, escalating from 0.26 W to 0.32 W, and a 20% ascent in efficiency, from 14.68% to 17.62%. This groundbreaking research illuminates the transformative potential of integrating TEGs and PV modules and the paramountcy of multifaceted parameter optimization. Moreover, it exemplifies the deployment of machine learning as a powerful tool for enhancing hybrid energy systems. This study, thus, stands as a beacon, heralding a new chapter in sustainable energy research and propelling further innovations in hybrid system design and optimization. Through its novel approach, it contributes indispensably to the arsenal of clean energy solutions. Photovoltaic system ; Computer science ; Software deployment ; Thermoelectric generator ; Hybrid system ; Artificial neural network ; Benchmark (surveying) ; Transformative learning ; Thermoelectric effect ; Electrical engineering ; Artificial intelligence ; Engineering ; Machine learning ; Physics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Advanced Thermodynamics and Statistical Mechanics
Authors: Sherif Abdulkader Tawfik | Mahad Rashid | Sunil Gupta | Salvy P. Russo | Tiffany R. Walsh | Svetha Venkatesh
Source: npj Computational Materials (article, 2023-01-11)
Field: Materials Science — Materials Chemistry

Machine learning-based discovery of vibrationally stable materials Abstract The identification of the ground state phases of a chemical space in the convex hull analysis is a key determinant of the synthesizability of materials. Online material databases have been instrumental in exploring one aspect of the synthesizability of many materials, namely thermodynamic stability. However, the vibrational stability, which is another aspect of synthesizability, of new materials is not known. Applying first principles approaches to calculate the vibrational spectra of materials in online material databases is computationally intractable. Here, a dataset of vibrational stability for ~3100 materials is used to train a machine learning classifier that can accurately distinguish between vibrationally stable and unstable materials. This classifier has the potential to be further developed as an essential filtering tool for online material databases that can inform the material science community of the vibrational stability or instability of the materials queried in convex hulls. Convex hull ; Classifier (UML) ; Stability (learning theory) ; Computer science ; Hull ; Instability ; Regular polygon ; Chemical stability ; Materials science ; Artificial intelligence ; Machine learning ; Physics ; Mathematics ; Geometry ; Thermodynamics ; Mechanics ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Hisham Alghamdi | Chika Maduabuchi | Abdullah Albaker | Ibrahim Alatawi | Theyab R. Alsenani | Ahmed S. Alsafran | Abdulaziz Almalaq | Mohammed A. AlAqil | Mostafa A.H. Abdelmohimen | Mohammad Alkhedher
Source: Engineering Science and Technology an International Journal (article, 2023-03-21)
Field: Materials Science — Materials Chemistry

RETRACTED: A prediction model for the performance of solar photovoltaic-thermoelectric systems utilizing various semiconductors via optimal surrogate machine learning methods This research focuses on finding the best surrogate performance prediction model for a solar photovoltaic-thermoelectric (PV-TE) module with different semiconductor materials. The need for a surrogate machine learning model arises due to the inefficiency of the current numerical simulations used to assess the performance of the device. The study introduces several surrogate machine learning models, such as recurrent, time delay, and regular artificial neural networks (ANNs), that are trained using expensive finite element generated data when the operating parameters of the system are altered. These parameters include the optical concentration ratio, cooling coefficient, wind speed, air temperature, glass emissivity, semiconductor dimensions, external load resistance, and thermoelectric current. Despite the time-intensive and costly data generation method, 714 datapoints were produced and utilized to train the surrogate machine learning models for improved performance prediction and optimization. The results indicate that the optimal machine learning model for solar PV-TE performance modelling was the ANN architecture with two hidden layers and five neurons per layer. Furthermore, lithium nitride oxide PV-TE showed a 65% improvement over the popular bismuth telluride PV-TE when tested under 25 Suns. The surrogate ANN also outperformed the conventional numerical simulations by 10,000 times using the same computing resources. Finally, the study suggests the potential of recurrent and time delay neural networks for modelling time series PV-TE data in the future. Photovoltaic system ; Surrogate model ; Computer science ; Artificial neural network ; Thermoelectric effect ; Performance prediction ; Machine learning ; Artificial intelligence ; Materials science ; Electronic engineering ; Engineering ; Simulation ; Electrical engineering Advanced Thermoelectric Materials and Devices ; Solar Radiation and Photovoltaics ; Thermal Radiation and Cooling Technologies
Authors: Esteban A. Marques | Stefan De Gendt | Geoffrey Pourtois | Michiel J. van Setten
Source: Journal of Chemical Information and Modeling (article, 2023-03-03)
Field: Materials Science — Materials Chemistry

Improving Accuracy and Transferability of Machine Learning Chemical Activation Energies by Adding Electronic Structure Information Predicting chemical activation energies is one of the longstanding and important challenges in computational chemistry. Recent advances have shown that machine learning can be used to create tools to predict them. Such tools can significantly decrease the computational cost for these predictions compared to traditional methods, which require an optimal path search along a high-dimensional potential energy surface. To enable this new route, we need both large and accurate datasets and a compact yet complete description of the reactions. Although data for chemical reactions is becoming increasingly available, the key step of encoding the reaction as an efficient descriptor remains a big challenge. In this paper, we demonstrate that including electronic energy levels in the description of the reaction significantly improves the prediction accuracy and transferability. Feature importance analysis further demonstrates that electronic energy levels have a higher importance than some structural information and typically require less space in the reaction encoding vector. In general, we observe that the results of the feature importance analysis relate well to the domain knowledge of fundamental chemical principles. This work can help to build better chemical reaction encodings for machine learning and thus improve the predictions of machine learning models for reaction activation energies. These models could ultimately be used to recognize reaction limiting steps in large reaction systems, allowing to account for bottlenecks at the design stage. Computer science ; Chemical space ; Machine learning ; Artificial intelligence ; Transferability ; Encoding (memory) ; Domain (mathematical analysis) ; Limiting ; Feature (linguistics) ; Energy (signal processing) ; Chemistry ; Drug discovery ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics