jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jae-Seon Yu | Serang Jung | Jin-Woo Cho | Geon‐Tae Park | Mikhail A. Kats | Sun‐Kyung Kim | Eungkyu Lee
Source: Nanophotonics (article, 2024-08-27)
Field: Materials Science — Surfaces, Coatings and Films

Ultrathin Ge‐YF <sub>3</sub> antireflective coating with 0.5 % reflectivity on high‐index substrate for long‐wavelength infrared cameras Achieving long-wavelength infrared (LWIR) cameras with high sensitivity and shorter exposure times faces challenges due to series reflections from high-refractive index lenses within compact optical systems. However, designing effective antireflective coatings to maximize light throughput in these systems is complicated by the limited range of transparent materials available for the LWIR. This scarcity narrows the degrees of freedom in design, complicating the optimization process for a system that aims to minimize the number of physical layers and address the inherent large refractive mismatch from high-index lenses. In this study, we use discrete-to-continuous optimization to design a subwavelength-thick antireflective multilayer coating on high-refractive index Si substrate for LWIR cameras, where the coating consists of few (e.g., five) alternating stacks of high- and low-refractive-index thin films (e.g., Ge-YF<sub>3</sub>, Ge-ZnS, or ZnS-YF<sub>3</sub>). Discrete optimization efficiently reveals the configuration of physical layers through binary optimization supported by a machine learning model. Continuous optimization identifies the optimal thickness of each coating layer using the conventional gradient method. As a result, considering the responsivity of a LWIR camera, the discrete-to-continuous strategy finds the optimal design of a 2.3-μm-thick antireflective coating on Si substrate consisting of five physical layers based on the Ge-YF<sub>3</sub> high-low index pair, showing an average reflectance of 0.54 % within the wavelength range of 8-13 μm. Moreover, conventional thin-film deposition (e.g., electron-beam evaporator) techniques successfully realize the designed structure, and Fourier-transform infrared spectroscopy (FTIR) and thermography confirm the high performance of the antireflective function. Anti-reflective coating ; Materials science ; Refractive index ; Optics ; Optoelectronics ; Coating ; High-refractive-index polymer ; Substrate (aquarium) ; Thin film ; Nanotechnology Optical Coatings and Gratings ; Advanced optical system design ; Photonic and Optical Devices
Authors: Wei Tang | Shizheng Wen | Huilong Hou | Qihua Gong | Min Yi | Wanlin Guo
Source: International Journal of Mechanical Sciences (article, 2024-04-27)
Field: Materials Science — Materials Chemistry

Phase-field simulation and machine learning of low-field magneto-elastocaloric effect in a multiferroic composite Multiferroics ; Magneto ; Composite number ; Materials science ; Phase (matter) ; Field (mathematics) ; Magnetic field ; Condensed matter physics ; Mechanical engineering ; Magnet ; Composite material ; Engineering ; Physics ; Optoelectronics ; Ferroelectricity ; Mathematics Solidification and crystal growth phenomena ; Magnetic Properties and Applications ; Advanced Mathematical Modeling in Engineering
Authors: Yunsheng Liu | Yifei Mo
Source: npj Computational Materials (article, 2024-07-20)
Field: Materials Science — Materials Chemistry

Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials Abstract Machine learning interatomic potential (MLIP) has been widely adopted for atomistic simulations. While errors and discrepancies for MLIPs have been reported, a comprehensive examination of the MLIPs’ performance over a broad spectrum of material properties has been lacking. This study introduces an analysis process comprising model sampling, benchmarking, error evaluations, and multi-dimensional statistical analyses on an ensemble of MLIPs for prediction errors over a diverse range of properties. By carrying out this analysis on 2300 MLIP models based on six different MLIP types, several properties that pose challenges for the MLIPs to achieve small errors are identified. The Pareto front analyses on two or more properties reveal the trade-offs in different properties of MLIPs, underscoring the difficulties of achieving low errors for a large number of properties simultaneously. Furthermore, we propose correlation graph analyses to characterize the error performances of MLIPs and to select the representative properties for predicting other property errors. This analysis process on a large dataset of MLIP models sheds light on the underlying complexities of MLIP performance, offering crucial guidance for the future development of MLIPs with improved predictive accuracy across an array of material properties. Benchmarking ; Computer science ; Ensemble learning ; Machine learning ; Range (aeronautics) ; Process (computing) ; Artificial intelligence ; Graph ; Data mining ; Theoretical computer science ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Fuel Cells and Related Materials
Authors: Yingchao Xu | Jiayu Luo | Shudan Xue | Qingmin Jin | Jitong Zhu | Sen Lu | Qitao Meng | Hu Du | Manqin Fu | Yujuan Zhong
Source: Journal of Food Composition and Analysis (article, 2024-07-11)
Field: Chemistry — Analytical Chemistry

Development of comprehensive prediction models for pumpkin fruit sensory quality using physicochemical analysis, near-infrared spectroscopy, and machine learning Quality (philosophy) ; Near-infrared spectroscopy ; Chemistry ; Food science ; Machine learning ; Computer science ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advances in Cucurbitaceae Research ; Phytochemicals and Antioxidant Activities
Authors: Himanshu Prasad Mamgain | Rahul Pal | Sanoj Kumar | Ranjeet Brajpuriya | Jitendra Kumar Pandey
Source: The Journal of Physical Chemistry C (article, 2024-09-30)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning-Based Stability Prediction and Analysis of Polypropylene Cu-MA Superhydrophobic Coating on the Aluminum Substrate This study investigates the performance of a polypropylene myristic acid-modified copper (Cu) superhydrophobic coating under various conditions, including immersion in NaCl solution, abrasion tests, pH levels, and temperature variations. We utilized specific machine learning models, such as random forest (RF) and XGBoost, to predict the wettability behavior of materials under varying voltage and temperature conditions. Gaussian noise data augmentation was employed to improve the model accuracy and prevent overfitting. The RF model demonstrated strong generalization capabilities, with consistently low MSE values and high R2 values across training and testing data sets. In contrast, the XGBoost model showed a slight performance decline when transitioning from training data to testing data, indicating potential generalization limitations. For variables such as immersion days, abrasion cycles, pH levels, and temperatures, multiple polynomial regression models were developed to predict the contact angle, showing a high degree of alignment with experimental data. The predictions of contact angle after 122 days of immersion in the NaCl solution (3.5 wt %), 350 abrasion cycles (each cycle of 18 cm), and exposure to 400 °C indicated sustained hydrophobic properties. The predicted contact angles were 110° for immersion in NaCl, 400 °C, and for abrasion cycles it is 99°. These predictions closely match with the experimental results, which showed contact angles of 103.7° for immersion, abrasion cycles and 97° for temperature exposure. The coating’s robustness is attributed to its micro/nanostructured surface, which traps air and reduces water contact, and the chemical stability imparted by polypropylene myristic acid modification, ensuring low surface energy and durability. This study demonstrates the effectiveness of higher-degree polynomial models in accurately predicting the performance of superhydrophobic coatings, underscoring the potential of advanced modeling techniques in the development of durable, high-performance coatings. Polypropylene ; Materials science ; Coating ; Aluminium ; Substrate (aquarium) ; Superhydrophobic coating ; Composite material ; Stability (learning theory) ; Computer science ; Machine learning ; Geology Surface Modification and Superhydrophobicity ; High voltage insulation and dielectric phenomena ; Fluid Dynamics and Thin Films
Authors: Xinyu Zhang | Hao Wang | Zhenya Du | Linlin Zhuo | Xiangzheng Fu | Dongsheng Cao | Boqia Xie | Keqin Li
Source: IEEE Journal of Biomedical and Health Informatics (article, 2024-12-02)
Field: Materials Science — Materials Chemistry

CardiOT: Towards Interpretable Drug Cardiotoxicity Prediction Using Optimal Transport and Kolmogorov--Arnold Networks Investigating the inhibitory effects of compounds on cardiac ion channels is essential for assessing cardiac drug safety. Consequently, researchers have developed computational models to evaluate combined cardiotoxicity (CCT) on cardiac ion channels. However, limitations in experimental data often cause issues like uneven data distribution and scarcity. Additionally, existing models primarily emphasize atomic information flow within graph neural networks (GNNs) while overlooking chemical bonds, leading to inadequate recognition of key structures. Therefore, this study integrates optimal transport (OT), structure remapping (SR), and Kolmogorov-Arnold networks (KANs) into a GNN-based CCT prediction model, CardiOT. First, the proposed CardiOT model employs OT pooling to optimize sample-feature joint distribution using expectation maximization, identifying "important" sample-feature pairs. Additionally, SR technology is used to emphasize the role of chemical bond information in message propagation. KAN technology is integrated to greatly enhance model interpretability. In summary, the model mitigates challenges related to uneven data distribution and scarcity. Multiple experiments on public datasets confirm the model's robust performance. We anticipate that this model will provide deeper insights into compound inhibition mechanisms on cardiac ion channels and reduce toxicity risks. Cardiotoxicity ; Computer science ; Artificial intelligence ; Drug ; Medicine ; Internal medicine ; Pharmacology Machine Learning in Materials Science ; Brain Tumor Detection and Classification
Authors: Xiaobing Shen | Yu Zuo | Wilmar Martínez
Source: IEEE Transactions on Power Electronics (article, 2024-05-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Conditional Generative Adversarial Network Aided Iron Loss Prediction for High-Frequency Magnetic Components This paper tackles the complex challenge of predicting magnetic iron losses in high-frequency magnetic components, introducing a novel Conditional Generative Adversarial Network model. Diverging from traditional loss prediction methodologies that often overlook intricate interactions of factors, our Conditional Generative Adversarial Network framework is designed to comprehensively incorporate diverse aspects such as material properties, geometrical variations, and environmental conditions. To facilitate this advanced approach, a specialized fourwire measurement kit was employed, which significantly enriched the training dataset with a wide range of measurements. When benchmarked against conventional Deep Neural Network models, the Conditional Generative Adversarial Network not only achieves faster convergence but also demonstrates markedly superior accuracy in predicting iron losses. This superiority is particularly notable in scenarios that extend beyond the training data's range, underscoring the model's robustness and adaptability. Such advancements in predictive accuracy and efficiency represent a significant leap forward in the design and optimization of high-frequency magnetic components. Adversarial system ; Generative adversarial network ; Generative grammar ; Computer science ; Artificial intelligence ; Electronic engineering ; Pattern recognition (psychology) ; Engineering ; Deep learning Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Magnetic Field Sensors Techniques
Authors: Mohammad Hossein Nikzad | Mohammad Heidari‐Rarani | Amir H. Navarchian
Source: Diamond and Related Materials (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Multi-objective optimization and machine learning-based prediction of tensile properties of an armchair graphene sheet Ultimate tensile strength ; Graphene ; Materials science ; Benchmark (surveying) ; Taguchi methods ; Random forest ; Yield (engineering) ; Decision tree ; Modulus ; Composite material ; Tree (set theory) ; Computer science ; Algorithm ; Machine learning ; Nanotechnology ; Mathematics ; Mathematical analysis Graphene research and applications ; Graphene and Nanomaterials Applications ; Carbon Nanotubes in Composites
Authors: J.J. Marín-Méndez | Paula Luri Esplandiú | Miriam Alonso-Santamaría | Berta Remírez-Moreno | Leyre Urtasun Del Castillo | Jaione Echávarri-Dublhán | Eva Almiron‐Roig | María-José Sáiz-Abajo
Source: Current Research in Food Science (review, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging as a non-destructive technique for estimating the nutritional value of food Knowledge of the energy and macronutrient content of complex foods is essential for the food industry and to implement population-based dietary guidelines. However, conventional methodologies are time-consuming, require the use of chemical products and the sample cannot be recovered. We hypothesize that the nutritional value of heterogeneous food products can be readily measured instead by using hyperspectral imaging systems (NIR and VIS-NIR) combined with mathematical models previously fitted with spectral profiles.118 samples from different food products were collected for building the predictive models using their hyperspectral imaging data as predictors and their nutritional values as dependent variables. Ten different models were screened (Multivariate Linear regression, Lasso regression, Rigde regression, Elastic Net regression, K-Neighbors regression, Decision trees regression, Partial Least Square, Support Vector Machines, Gradient Boosting regression and Random Forest regression). The best results were obtained with Ridge regression for all parameters. The best performance was for estimating the protein content with a RMSE of 1.02 and a R<sup>2</sup> equal to 0.88 in a test set, following by moisture (RMSE of 2.21 and R<sup>2</sup> equal to 0.85), energy value (RMSE of 21.84 and R<sup>2</sup> equal to 0.76) and total fat (RMSE of 2.17 and R<sup>2</sup> equal to 0.72). The performance with carbohydrates (RMSE of 2.12 and R<sup>2</sup> equal to 0.61) and ashes (RMSE of 0.25 and R<sup>2</sup> equal to 0.38) was worse. This study shows that it is possible to predict the energy and nutrient values of processed complex foods, using hyperspectral imaging systems combined with supervised machine learning methods. Mean squared error ; Hyperspectral imaging ; Mathematics ; Statistics ; Regression ; Linear regression ; Regression analysis ; Elastic net regularization ; Multivariate statistics ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Mehboob Ali | Hoang T. Nguyen | Jeffrey T. Paci | Yue Zhang | Horacio D. Espinosa
Source: Nano Letters (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

Thermomechanical Properties of Transition Metal Dichalcogenides Predicted by a Machine Learning Parameterized Force Field The mechanical and thermal properties of transition metal dichalcogenides (TMDs) are directly relevant to their applications in electronics, thermoelectric devices, and heat management systems. In this study, we use a machine learning (ML) approach to parametrize molecular dynamics (MD) force fields to predict the mechanical and thermal transport properties of a library of monolayered TMDs (MoS<sub>2</sub>, MoTe<sub>2</sub>, WSe<sub>2</sub>, WS<sub>2</sub>, and ReS<sub>2</sub>). The ML-trained force fields were then employed in equilibrium MD simulations to calculate the lattice thermal conductivities of the foregoing TMDs and to investigate how they are affected by small and large mechanical strains. Furthermore, using nonequilibrium MD, we studied thermal transport across grain boundaries. The presented approach provides a fast albeit accurate methodology to compute both mechanical and thermal properties of TMDs, especially for relatively large systems and spatially complex structures, where density functional theory computational cost is prohibitive. Transition metal ; Force field (fiction) ; Parameterized complexity ; Materials science ; Field (mathematics) ; Nanotechnology ; Chemical physics ; Condensed matter physics ; Chemistry ; Computer science ; Physics ; Mathematics ; Artificial intelligence ; Algorithm Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Ghazaleh Ramezani | Ion Stiharu | Theo G. M. van de Ven | Vahé Nerguizian
Source: Encyclopedia (review, 2024-11-29)
Field: Materials Science — Biomaterials

Optimizing Graphene Oxide Content in Cellulose Matrices: A Comprehensive Review on Enhancing the Structural and Functional Performance of Composites The incorporation of graphene into cellulose matrices has emerged as a promising strategy for enhancing the structural and functional properties of composite materials. This comprehensive review provides a critical analysis of recent advances in optimizing graphene content in cellulose matrices and its impact on composite performance. Various optimization techniques, including response surface methodology, particle swarm optimization, and artificial neural networks, have been employed to identify optimal graphene concentrations and processing conditions. Quantitative analyses demonstrate significant improvements in mechanical properties, with notable increases in tensile strength and Young’s modulus reported for graphene/microfibrillated cellulose composites. Substantial enhancements in thermal stability have been observed in lysozyme-modified graphene nanoplatelet–cellulose composites. Electrical conductivity has been achieved at low graphene loading levels. Additionally, barrier properties, biocompatibility, and functionality for applications such as energy storage and environmental remediation have been substantially improved. This review explores case studies encompassing the optimization of thermal conductivity, viscosity, durability behaviors, pollutant removal, and various other properties. Despite promising results, challenges remain, including uniform dispersion, scalability, cost-effectiveness, and long-term stability. Strategies such as surface functionalization, solvent selection, and protective coatings are discussed. Future research directions, including novel processing techniques like 3D printing and electrospinning, as well as the incorporation of additional functional materials, are outlined. This review synthesizes current knowledge, identifies emerging trends, and provides a roadmap for future research in the rapidly evolving field of graphene–cellulose composites. Graphene ; Materials science ; Cellulose ; Composite material ; Surface modification ; Composite number ; Ultimate tensile strength ; Thermal stability ; Oxide ; Nanotechnology ; Chemical engineering Advanced Cellulose Research Studies ; Graphene and Nanomaterials Applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Jianhui Yang | Fei shi | Chengji J. Zhou | Shaozheng Zhang | Qiao Sui | Liang Chen
Source: Advanced Functional Materials (article, 2024-09-06)
Field: Materials Science — Materials Chemistry

Machine Learning and Theoretical Prediction of Highly Spin‐Polarized Cr<sub>2</sub>CO<i><sub>x</sub></i> MXene with Enhanced Curie Temperature Abstract 2D magnetic materials with high spin polarization and Curie temperature are highly desirable for ultrathin spintronic devices. This study utilizes first‐principles methods to systematically investigate 225 O adsorption configurations, demonstrating that Cr 2 CO x MXene consistently maintains a long‐range‐ordered ferromagnetic arrangement with high spin polarization, irrespective of the O adsorption configuration. Most configurations also display Curie temperature ( T C ) exceeding room temperature, with the possibility of further enhancement by reducing O coverage. Machine learning models are developed to accurately predict O adsorption configurations, exchange interaction energies, and T C . A novel approach of stripping F and OH groups to create Cr 2 CO x on Cr‐based MXene surfaces is proposed to address the difficulty in achieving long‐range‐ordered magnetic structures by manipulating surface adsorbates in MXene. This approach enhances the ability to control the magnetic properties of MXenes and paves the way for their application in ultrathin spintronic devices. Materials science ; Curie temperature ; Marie curie ; Curie ; Spin (aerodynamics) ; Condensed matter physics ; Ferromagnetism ; Thermodynamics ; Physics MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Darja Cvetković | Marija Mitrović Dankulov | Aleksandar Bogojević | Saša Lazović | Darija Obradović
Source: Chemometrics and Intelligent Laboratory Systems (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Enhancing Hansen Solubility Predictions with Molecular and Graph-Based Approaches Solubility ; Graph ; Mathematics ; Chemistry ; Computer science ; Combinatorics ; Organic chemistry Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography