jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mustafa Al-Naser | Sami Elferik | Abdullatif Al-Najim | Abrar Al-Amoudi | Rached Ben‐Mansour
Source:  (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

Intelligent Approach for Fouling Detection in Phase Change Heat Exchanger Fouling is the formation of unwanted material deposits on heat transfer equipment's inner surfaces during process heating and cooling, which leads to a reduction of the efficiency of the system and environmental effects. Because of this, fouling analysis becomes a very significant study topic for an effective and safe operation. It is challenging to analyze fouling when there is a phase change of fluids during heat transfer, such as in condensers and boilers, which are crucial units in industrial facilities. Machine learning algorithms have proven their reliability and robustness in different aspects of the industry. In this research, we propose an intelligent data-driven model to predict the location of the fouling given transient data like the rate of change of outlet temperature. Two scenarios were tested to predict fouling in a heat exchanger: one with uniform fouling in two segments and another with three fouling locations. Both models achieved a prediction accuracy of over 99%, indicating powerful prediction performance in fouling localization. Fouling ; Heat exchanger ; Materials science ; Computer science ; Chemistry ; Mechanical engineering ; Engineering Crystallization and Solubility Studies ; Metallurgy and Material Forming ; Heat Transfer and Optimization
Authors: Hajime Shimakawa | Takahiro Umemoto | Akiko Kumada | Masahiro Sato
Source: IEEJ Transactions on Fundamentals and Materials (article, 2024-06-30)
Field: Materials Science — Materials Chemistry

High-Throughput Screening of Gaseous Molecule Space for Developing SF&lt;sub&gt;6&lt;/sub&gt; Alternatives Sulfur hexafluoride (SF6) is a powerful insulating gas, yet its global warming potential (GWP) of 25,200 compels us to urgently seek eco-friendly alternatives. Various experiments and computations have been conducted to develop SF6 alternatives that fulfill requirements for dielectric breakdown strength, GWP, and boiling point. However, the efforts has been hindered by limited experimental results as well as inadequate computational performance to extrapolatively predict gas properties of unknown molecular structures. Thus, this study conducts high-throughput screening of gaseous molecule space using quantum mechanics-assisted machine learning model. This exploration has expanded the existing small-molecule space to encompass 3 × 108 molecules, a 1500-fold increase over databases, equivalent to registration efforts spanning 4.5 × 104 years. The computational results identify potential candidates that pose high breakdown strength at low-temperature and high-pressure conditions while maintaining low GWP. These findings lead to crucial insights into the next steps of computational design and experimental verification for SF6 alternatives. Sulfur hexafluoride ; Global-warming potential ; Chemical space ; Computation ; Space (punctuation) ; Throughput ; Molecule ; Dielectric strength ; Boiling point ; Computer science ; Biochemical engineering ; Nanotechnology ; Dielectric ; Chemistry ; Materials science ; Engineering ; Physics ; Greenhouse gas ; Thermodynamics ; Algorithm ; Quantum mechanics ; Electrical engineering ; Drug discovery ; Telecommunications Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Phase Equilibria and Thermodynamics
Authors: Juan I. Rodríguez | Ulises A. Vergara-Beltrán
Source: arXiv (Cornell University) (preprint, 2024-05-28)
Field: Materials Science — Materials Chemistry

A physics-inspired evolutionary machine learning method: from the Schrödinger equation to an orbital-free-DFT kinetic energy functional We introduce a machine learning (ML) supervised model function that is inspired by the variational principle of physics. This ML hypothesis evolutionary method, termed ML-Omega, allows us to go from data to differential equation(s) underlying the physical (chemical, engineering, etc.) phenomena the data are derived from. The fundamental equations of physics can be derived from this ML-Omega evolutionary method when provided the proper training data. By training the ML-Omega model function with only three hydrogen-like atom energies, the method can find Schrödinger's exact functional and, from it, Schrödinger's fundamental equation. Then, in the field of density functional theory (DFT), when the model function is trained with the energies from the known Thomas-Fermi (TF) formula E = -0.7687Z^7/3, it correctly finds the exact TF functional. Finally, the method is applied to find a local orbital-free (OF) functional expression of the independent electron kinetic energy functional Ts based on the gamma-TF-lambda-vW model. By considering the theoretical energies of only 5 atoms (He, Be, Ne, Mg, Ar) as the training set, the evolutionary ML-Omega method finds an ML-Omega-OF-DFT local Ts functional (gamma-TF-lambda-vW (0.964, 1/4)) that outperforms all the OF- DFT functionals of a representative group. Moreover, our ML-Omega-OF functional overcomes the LDA's and some local GGA-DFT's functionals' difficulty to describe the stretched bond region at the correct spin configuration of diatomic molecules. Although our evolutionary ML-Omega model function can work without an explicit prior-form functional, by using the techniques of symbolic regression, in this work we exploit prior-form functional expressions to make the training process faster in the example problems presented here. Kinetic energy ; Energy (signal processing) ; Physics ; Statistical physics ; Classical mechanics ; Theoretical physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Lu-Yun Li | Deruo Cheng | Yiqiong Shi | Bah‐Hwee Gwee
Source:  (article, 2024-10-14)
Field: Materials Science — Surfaces, Coatings and Films

Unpaired Synthesis of IC Scanning Electron Microscopy Images with Structural Constraints The analysis of Integrated Circuit (IC) Scanning Electron Microscopy (SEM) images plays a crucial role in the reliability and authenticity investigation of modern ICs. It pertains to the task of predicting masks, which indicate the pixel-level position of different circuit elements, from IC SEM images, and it is now usually achieved through deep learning models that need to be trained or finetuned over a considerably large labeled dataset of IC SEM images. Furthermore, such labeled dataset is usually not publicly available and is costly to obtain. Recently, unpaired synthesis of IC SEM images, which aims to translate masks indicating IC circuit elements into IC SEM images with corresponding circuit elements, has made it possible for semi-supervised learning models to perform comparably well in IC image analysis but with lower dependency on labeled data. However, existing methods for unpaired synthesis of IC SEM images suffer from the low consistency of circuit element patterns between masks and synthetic images. In this paper, we propose a Structural Constrained Cycle-Consistent Adversarial Network (SC-CycleGAN), that leverages on CycleGAN and novel structural constraints to synthesize IC SEM images with improved consistency of circuit element patterns. Experiments on real masks and IC SEM images have demonstrated the superior performance of our proposed SC-CycleGAN. Unpaired electron ; Scanning electron microscope ; Materials science ; Electron microscope ; Microscopy ; Computer science ; Electron ; Nanotechnology ; Physics ; Optics ; Composite material ; Nuclear physics Electron and X-Ray Spectroscopy Techniques
Authors: Yuanming Zhang | Kuo Zhan | Pei-Lin Xin | Yingqi Zhao | Shubo Wang | Aliaksandr Hubarevich | Xuejin Zhang | Jianan Huang
Source: arXiv (Cornell University) (preprint, 2024-12-11)
Field: Materials Science — Materials Chemistry

Deep learning assisted SERS detection of prolines and hydroxylated prolines using nitrilotriacetic acid functionalized gold nanopillars Proline (Pro) is one kind of proteinogenic amino acid and an important signaling molecule in the process of metabolism. Hydroxyproline (Hyp) is a product on Pro oxygen sensing post-translational modification (PTM), which is efficiently modulated tumor cells for angiogenesis. Distinguishing between Pro and Hyp is crucial for diagnosing connective tissue disorders, as elevated levels of Hyp can indicate abnormal collagen metabolism, often associated with diseases like osteogenesis imperfecta or fibrosis. However, there is a very small difference between molecular structures of Pro and Hyp, which is a big challenge for current detection technologies to distinguish them. For surface-enhanced Raman scattering (SERS) sensors, the similar molecule structure leads to similar Raman spectra that are difficult to distinguish. Furthermore, another problem is the weak affinity between amino acids sample and SERS-active substrates by physical adsorption. The selecting capturing of Pro and Hyp in the mixture of amino acids is not easy to achieve. In this work, we designed a new method for Pro and Hyp specifical detection and recognition by using gold nanopillars as the SERS substrate and combing nitrilotriacetic acid (NTA) with nickel (Ni) to form NTA-Ni structure as a specifical affinity agent. One side of NTA-Ni was attached to gold nanopillars through thiol binding. Another side captured the amino acids using reversible binding by receptor-ligand interaction between Ni and amino acids. Because of the different binding time with NTA-Ni and amino acids, the sensor can recognize Pro and Hyp from amino acids mixture. Then we used automatic peak assignment program for data analysis and machine learning model to distinguish between Pro and Hyp. The label-free SERS detection of amino acids PTM using gold nanopillars provides a potential method to further biomolecule detection and specifical capture. Nitrilotriacetic acid ; Nanopillar ; Chemistry ; Amino acid ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Nanostructure ; Biochemistry ; Organic chemistry Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Spectroscopy and Chemometric Analyses
Authors: Juan Daniel Sarmiento Abello | Diego Alexander Tibaduiza Burgos | Maribel Anaya | J.M. Montero Gómez | Maria Paula Pérez | Carlos Díaz | Johan Sebastián Eslava Garzón
Source: Encuentro Internacional de Educación en Ingeniería (article, 2024-09-24)
Field: Materials Science — Materials Chemistry

Uso de Machine Learning en la identificación y clasificación de microplásticos enfocado en el poliestireno expandido Los microplásticos representan un reto para los sistemas de saneamiento y potabilidad de los recursos hídricos debido a que no se conoce la eficiencia de los métodos de filtrado para retener estas partículas, por lo que es indispensable implementar modelos que permitan detectar estos materiales con el objetivo de desarrollar estrategias que mitiguen su impacto en los sistemas acuíferos. Por consiguiente, en el presente trabajo se muestran los resultados obtenidos en la evaluación de una metodología para la identificación y clasificación de microplásticos compuestos por poliestireno expandido (EPS) utilizando una lengua electrónica y algoritmos de Machine Learning. Se desarrolló experimentos que simulaban diferentes entornos de agua donde podrían encontrarse estas partículas. Los tamaños de microplásticos utilizados en los experimentos oscilaban entre 500, 1000 y 1500 micrómetros. La metodología evaluó diferentes métodos de preprocesado, así como diferentes técnicas de Machine Learning, para determinar cuál mostraba una mayor precisión en la detección y en la clasificación. Se obtuvieron resultados mayores al 80% en la evaluación de cuatro algoritmos: KNN, SMV, redes neuronales y Naive Bayes. Se demostró la utilidad de las lenguas electrónicas y la técnica electroquímica de la espectroscopía de impedancia (EIS) para la identificación de microplásticos en agua con diferentes niveles de contaminación. Humanities ; Philosophy Material Selection and Properties ; Knowledge Societies in the 21st Century
Authors: Gema Raposo-Hernández | Rafael R. Pappalardo | Florent Réal | Valérie Vallet | Enrique Sánchez Marcos
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2024-10-10)
Field: Chemistry — Electrochemistry

Towards a Realistic Theoretical Electronic Spectra of Metal Aqua Ions in Solution: The Case of Ce(H<sub>2</sub>O)<sub>n</sub><sup>3+</sup> Using Statistical Methods and Quantum ChemistryCalculation Ce(III) UV-vis spectrum Accurately predicting spectra for heavy elements, often open-shell systems, is a significant challenge typically addressed using a single cluster approach with a fixed coordination number. Developing a realistic model that accounts for temperature effects, variable coordination numbers, and interprets experimental data is even more demanding due to the strong solute-solvent interactions present in solutions of heavy metal cations. This study addresses these challenges by combining multiple methodologies to accurately predict realistic spectra for highly-charged metal cations in aqueous media, with a focus on the electronic absorption spectrum of Ce<sup>3+</sup> in water. Utilizing highly correlated relativistic quantum mechanical (QM) wavefunctions and structures from molecular dynamics (MD) simulations, we show that the convolution of individual vertical transitions yields an excellent agreement with experimental results without the introduction of empirical broadening. The good results are obtained for both the normalized spectrum and that of absolute intensity. The study incorporates a statistical machine learning algorithm, Gaussian Mixture Models-Nuclear Ensemble Approach (GMM-NEA), to convolute individual spectra. The microscopic distribution provided by MD simulations allows us to examine the contributions of the octa-and ennea-hydrate of Ce<sup>3+</sup> in water to the final spectrum. Additionally, the temperature dependence of the spectrum is theoretically captured by observing the changing population of these hydrate forms with temperature. We also explore an alternative method for obtaining statistically representative structures in a less demanding manner than MD simulations, derived from QM Wigner distributions.The combination of Wigner-sampling and GMM-NEA broadening shows promise for wide application in spectroscopic analysis and predictions, offering a computationally efficient alternative to traditional methods. Ion ; Spectral line ; Metal ; Metal ions in aqueous solution ; Physical chemistry ; Chemistry ; Physics ; Atomic physics ; Quantum mechanics ; Organic chemistry Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies ; Water Quality Monitoring and Analysis
Authors: Chucai Peng | Jingnan Huang | Xichen Sun | Yifei Nan | Yaohui Chen | Kun Chen | Feng Jun
Source: Materials (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Projectile Penetration into Calcareous Sand Subgrade Airport Runway Pavement with Genetic Algorithm Optimization As an important civil and military infrastructure, airport runway pavement is faced with threats from cluster munitions, since it is vulnerable to projectile impacts with internal explosions. Aiming at the damage assessment of an island airport runway pavement under impact, this work dealt with discrete modeling of rigid projectile penetration into concrete pavement and the calcareous sand subgrade multi-layer structure. First, the Discrete Element Method (DEM) is introduced to model concrete and calcareous sand granular material features, like cohesive fracture and strain hardening due to compression, with mesoscale constitutive laws governing the normal and shear interactions between adjacent particles. Second, the subsequent DEM simulations of uniaxial and triaxial compression were performed to calibrate the DEM parameters for pavement concrete, as well as subgrade calcareous sand. Prior to the multi-layer structure investigations, penetration into sole concrete or calcareous sand is validated in terms of projectile deceleration and depth of penetration (DOP) with relative error ≤ 5.6% providing a reliable numerical tool for deep penetration damage assessments. Third, projectile penetration into the airport runway structure with concrete pavement and calcareous sand subgrade was evaluated with validated DEM model. Penetration numerical simulations with various projectile weight, pavement concrete thickness as well as striking velocity, were performed to achieve the DOP. Moreover, the back-propagation (BP) neural network proxy model was constructed to predict the airport runway penetration data with good agreement realizing rapid and robust DOP forecasting. Finally, the genetic algorithm was coupled with the proxy model to realize intelligent optimization of pavement penetration, whereby the critical velocity projectile just perforates concrete pavement indicating the severest subsequent munition explosion damage. Runway ; Projectile ; Subgrade ; Geotechnical engineering ; Penetration (warfare) ; Penetration test ; Structural engineering ; Geology ; Engineering ; Materials science High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Geophysical Methods and Applications
Authors: Sheng Zhou | Yongxin Gai | Guoqiang Lan
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of Tunable Terahertz Metamaterial Absorber Based on Deep Learning Method Terahertz radiation ; Metamaterial absorber ; Materials science ; Metamaterial ; Absorptance ; Absorption (acoustics) ; Optoelectronics ; Broadband ; Finite-difference time-domain method ; Optics ; Plasmon ; Transmittance ; Tunable metamaterials ; Reflectivity ; Physics Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Energy Harvesting in Wireless Networks
Authors: Lei Peng | Zhiyong Liu | Pengyu Wang | Hao Liu | Huang Zhang | Huaqiang Yin | Xuedong He | Tao Ma
Source:  (article, 2024-08-04)
Field: Materials Science — Materials Chemistry

Reconstruction of 3D Nuclear Graphite Based on Generative Adversarial Neural Networks With Gradient Penalty Abstract Nuclear graphite is an important structural material in reactor, and it is important to study the dehumidification method to improve the economy of reactor. In order to accurately obtain various physical property parameters of nuclear graphite from the pore scale, constructing a 3D digital graphite that can accurately characterizes the pore structure of micropores and nanopores is crucial. Due to the high experimental cost or a long reconstruction time of tradigital reconstruction methods, we use the generative adversarial neural networks with gradient penalty (WGAN-GP) to reconstruct 3D digital graphite. Taking binary image of graphite as training sample for model training, the generator model that can generate 3D graphite images is obtained, and then multiple graphite images are obtained. The generated images and original images were compared and analyzed from two-point statistics, pore distribution, porosity and specific surface area. The results show the reconstructed graphites are consistent with the original graphite, which proves the reliability of this method. Adversarial system ; Generative adversarial network ; Computer science ; Artificial neural network ; Generative grammar ; Artificial intelligence ; Deep learning Graphite, nuclear technology, radiation studies ; Welding Techniques and Residual Stresses ; Machine Learning in Materials Science
Authors: Abhishek Chandra | Bram Daniels | Mitrofan Curti | Koen Tiels | E.A. Lomonova
Source: arXiv (Cornell University) (preprint, 2024-07-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic Hysteresis Modeling with Neural Operators Hysteresis modeling is crucial to comprehend the behavior of magnetic devices, facilitating optimal designs. Hitherto, deep learning-based methods employed to model hysteresis, face challenges in generalizing to novel input magnetic fields. This paper addresses the generalization challenge by proposing neural operators for modeling constitutive laws that exhibit magnetic hysteresis by learning a mapping between magnetic fields. In particular, three neural operators-deep operator network, Fourier neural operator, and wavelet neural operator-are employed to predict novel first-order reversal curves and minor loops, where novel means they are not used to train the model. In addition, a rate-independent Fourier neural operator is proposed to predict material responses at sampling rates different from those used during training to incorporate the rate-independent characteristics of magnetic hysteresis. The presented numerical experiments demonstrate that neural operators efficiently model magnetic hysteresis, outperforming the traditional neural recurrent methods on various metrics and generalizing to novel magnetic fields. The findings emphasize the advantages of using neural operators for modeling hysteresis under varying magnetic conditions, underscoring their importance in characterizing magnetic material based devices. The codes related to this paper are at github.com/chandratue/magnetic_hysteresis_neural_operator. Hysteresis ; Computer science ; Physics ; Condensed matter physics Magnetic Properties and Applications ; Neural Networks and Applications
Authors: Sebastian Lukas | Ardeshir Esteki | Nico Rademacher | Vikas Jangra | Michael Groß | Zhenxing Wang | Ha-Duong Ngo | Manuel Bäuscher | Piotr Mackowiak | K. Höppner | Dominique Wehenkel | Richard M. van Rijn | Max C. Lemme
Source: arXiv (Cornell University) (preprint, 2024-08-29)
Field: Materials Science — Materials Chemistry

High-yield large-scale suspended graphene membranes over closed cavities for sensor applications Suspended membranes of monoatomic graphene exhibit great potential for applications in electronic and nanoelectromechanical devices. In this work, a "hot and dry" transfer process is demonstrated to address the fabrication and patterning challenges of large-area graphene membranes on top of closed, sealed cavities. Here, "hot" refers to the use of high temperature during transfer, promoting the adhesion. Additionally, "dry" refers to the absence of liquids when graphene and target substrate are brought into contact. The method leads to higher yields of intact suspended monolayer CVD graphene and artificially stacked double-layer CVD graphene membranes than previously reported. The yield evaluation is performed using neural-network-based object detection in SEM images, ascertaining high yields of intact membranes with large statistical accuracy. The suspended membranes are examined by Raman tomography and AFM. The method is verified by applying the suspended graphene devices as piezoresistive pressure sensors. Our technology advances the application of suspended graphene membranes and can be extended to other two-dimensional (2D) materials. Graphene ; Membrane ; Yield (engineering) ; Scale (ratio) ; Materials science ; Nanotechnology ; Composite material ; Chemistry ; Physics Graphene research and applications