jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao Shen | Chao Zhou | Hui Liu | Ran Jia | Zhao Qi
Source:  (article, 2024-12-06)
Field: Materials Science — Materials Chemistry

Life prediction of composite insulator based on thermogravimetric test and support vector machine Composite insulators are affected by ultraviolet rays, electric field, humidity and temperature difference, which inevitably aging and triggering insulation failure. Through the heat loss test, the heat loss curves of silicone rubber composite insulators with inner and outer layers, different heating rates and different operating years were obtained, the activation energy was calculated, and the Arrhenius life equation was established. The optimal penalty coefficient C and kernel function parameter g in the support vector machine are searched by genetic algorithm, and a life prediction model based on GA-SVM is proposed for training and testing. The results show that the content of silicone and other macromolecules in silicone rubber materials is significantly reduced. the prediction accuracy of the GA-SVM model is improved by 5%-12% compared with the thermal life equation and the BP neural network which is easy to fall into the local optimal solution. The prediction results are accurate and valid as verified by specimen appearance and SEM, which can provide a reference for the maintenance and operation and maintenance of composite insulators. Thermogravimetric analysis ; Composite number ; Support vector machine ; Insulator (electricity) ; Test (biology) ; Test vector ; Materials science ; Computer science ; Composite material ; Engineering ; Geology ; Machine learning ; Chemical engineering ; Test set High voltage insulation and dielectric phenomena ; Advanced Sensor and Control Systems
Authors: Seyedeh Fatemeh Alavi | Yuxinxin Chen | Yi-Fan Hou | Fuchun Ge | Peikun Zheng | Pavlo O. Dral
Source: ChemRxiv (preprint, 2024-10-08)
Field: Chemistry — Spectroscopy

Towards Accurate and Efficient Anharmonic Vibrational Frequencies with the Universal Interatomic Potential ANI-1ccx-gelu and Its Fine-Tuning Calculating anharmonic vibrational modes of molecules for interpreting experimental spectra is one of the most interesting challenges of contemporary computational chemistry. However, the traditional QM methods are costly for this application. Machine learning techniques have emerged as a powerful tool for substituting the traditional QM methods. Universal interatomic potentials (UIPs) hold a particular promise to deliver accurate results at a fraction of the cost of the traditional QM methods but the performance of UIPs for calculating anharmonic vibrational frequencies remains hitherto unknown. Here we show that despite a known excellent performance of the representative UIP ANI-1ccx for thermochemical properties, it fails for the anharmonic frequencies due to the original unfortunate choice of the activation function. Hence, we recommend evaluating new UIPs on anharmonic frequencies as an additional important quality test. To remedy the shortcomings of ANI-1ccx, we introduce its reformulation ANI-1ccx-gelu with the GELU activation function which is capable of calculating IR anharmonic frequencies with reasonable accuracy (close to B3LYP/6-31G*). We also show that our new UIP can be fine-tuned to obtain very accurate anharmonic frequencies for some specific molecules but more effort is needed to improve the overall quality of UIP and its capability for fine-tuning. The new UIP will be included as part of our universal and updatable AI-enhanced QM methods (UAIQM) platform and is available together with tutorials in open-source MLatom at https://github.com/dralgroup/mlatom. The calculations can also be performed via web browser at https://XACScloud.com. Anharmonicity ; Interatomic potential ; Physics ; Statistical physics ; Molecular physics ; Condensed matter physics ; Quantum mechanics ; Molecular dynamics Molecular Spectroscopy and Structure ; Nonlinear Optical Materials Research ; Solid-state spectroscopy and crystallography
Authors: Mustafa Çoşkun Özdemir | Caner Ünlü | Şener Özönder
Source: arXiv (Cornell University) (preprint, 2024-08-10)
Field: Materials Science — Materials Chemistry

Unveiling the Design Rules for Tunable Emission in Graphene Quantum Dots: A High-Throughput TDDFT and Machine Learning Perspective We present time-dependent density functional theory (TDDFT) calculations of fluorescence emission energies for 284 distinct graphene quantum dots (GQDs) of varying shapes (square, hexagonal, and amorphous) and sizes ($\sim$1-2 nm). These GQDs are doped with one or two elements from B, N, O, S, and P at dopant percentages of 1.5%, 3%, 5%, and 7%. Our study systematically investigates the trends and patterns in emission energies as a function of shape, size, dopant type and dopant percentage. Twelve structures are identified to have emission wavelengths in the visible spectrum. The emission energies derived from our calculations can guide the formulation of specific GQD mixtures to achieve desired emission spectra within and beyond the visible range for industrial applications. Furthermore, the extensive dataset, including emission energies along with molecular structures generated in this work, creates a DFT dateset for further machine learning studies. Time-dependent density functional theory ; Graphene ; Quantum dot ; Spectrum (functional analysis) ; Throughput ; Doping ; Physics ; Nanotechnology ; Materials science ; Density functional theory ; Optoelectronics ; Computer science ; Quantum mechanics ; Telecommunications Graphene research and applications ; Carbon and Quantum Dots Applications ; GaN-based semiconductor devices and materials
Authors: Justus Detring | Abel Barreto | Anne‐Katrin Mahlein | Stefan Paulus
Source: Research Square (preprint, 2024-07-16)
Field: Chemistry — Analytical Chemistry

Quality Assurance of Hyperspectral Imaging Systems for Neural Network supported Plant Phenotyping Hyperspectral imaging ; Quality assurance ; Quality (philosophy) ; Artificial neural network ; Computer science ; Artificial intelligence ; Engineering ; Operations management ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Water Quality Monitoring and Analysis
Authors: Deng Qi-ping | Yu Xiong | Zhengchun Du | Jinping Cui | Cheng Peng | Zhiyong Luo | Jinli Xie | Hailong Qin | Zhimin Sun | Qingfeng Zeng | Kang Guan
Source: Applied Sciences (article, 2024-11-27)
Field: Materials Science — Ceramics and Composites

Evaluating the Thermal Shock Resistance of SiC-C/CA Composites Through the Cohesive Finite Element Method and Machine Learning Silicon carbide-coated carbon fiber-reinforced carbon aerogel (SiC-C/CA) composites are ideal for high-temperature applications due to their ability to endure rapid temperature changes without losing structural integrity. However, assessing and optimizing the Thermal Shock Resistance (TSR) of these composites is challenging due to the complexities in measuring thermal and mechanical responses accurately under rapid fluctuations. Herein, we introduce a novel approach combining the cohesive finite element method (CFEM) with machine learning (ML) to address these challenges. The CFEM simulates crack initiation and propagation and captures mechanical behavior under thermal stress, while ML predicts TSR using simulation datasets, reducing the need for empirical trial-and-error processes. Our method achieves a prediction error for coating residual stress within 15.70% to 24.11% before and after thermal shock tests. Additionally, the ML model, developed to predict the average stiffness degradation factor of the SiC coating after three thermal shock cycles, achieves a coefficient of determination (R2) of 0.9171. This combined approach significantly improves the accuracy and efficiency of TSR assessment and can be extended to other coating materials, accelerating the development of high-temperature-resistant materials with optimized TSR for industrial applications. Materials science ; Thermal shock ; Composite material ; Finite element method ; Structural engineering ; Engineering Advanced ceramic materials synthesis ; Thermal properties of materials ; Mechanical Behavior of Composites
Authors: Sri Vishnuvardhan Reddy Akepati | Nitant Gupta | Arthi Jayaraman
Source: arXiv (Cornell University) (preprint, 2024-01-22)
Field: Materials Science — Materials Chemistry

Computational Reverse Engineering Analysis of Scattering Experiments Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D) Characterization of structural diversity within soft materials is key for engineering new materials for various applications. Small-angle scattering (SAS) is a widely used characterization technique that provides structural information in soft materials at varying length scales and typically outputs scattered intensity I(q) as a function of the scattered wavevector represented by its magnitude q and azimuthal angle θ. While isotropic structures can be interpreted from azimuthally averaged 1D SAS profile, to understand anisotropic spatial arrangements, one has to interpret the 2D SAS profile, I(q,θ). In this paper, we present a new method called CREASE-2D that interprets I(q,θ) as is and outputs the relevant structural features. CREASE-2D is an extension of the 'computational reverse engineering analysis for scatting experiments' (CREASE) method that has been used successfully to analyze 1D SAS profiles for a variety of soft materials. CREASE uses a genetic algorithm for optimization and a surrogate machine learning (ML) model for fast calculation of 1D 'computed' scattering profiles that are then compared to the experimental 1D scattering profiles during optimization. In CREASE-2D, which goes beyond CREASE in interpretting 2D scattering profiles, we use XGBoost as the surrogate ML model to relate structural features to the I(q,θ) profile. The CREASE-2D workflow identifies the structural features whose computed I(q,θ) profiles match the input experimental I(q,θ). We test the performance of CREASE-2D by using as input a variety of in silico 2D SAS profiles with known structural features and demonstrate that CREASE-2D converges towards their correct structural features. We expect this method will be valuable for materials' researchers who need direct interpretation of 2D scattering profiles to explore structural anisotropy. Isotropy ; Scattering ; Anisotropy ; Characterization (materials science) ; Function (biology) ; Variety (cybernetics) ; Physics ; Optics ; Algorithm ; Computer science ; Artificial intelligence X-ray Diffraction in Crystallography ; Ultrasonics and Acoustic Wave Propagation
Authors: Аlexander Kul'ment'ev
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence optimization method for nuclear fuel triso-elements in high-temperature reactor In the present era nuclear energy, has unique advantages compared to other energy sources. Now significant research and development related to TRISO-coated fuels is underway worldwide as part of the activities of the Generation IV International Forum on Very-High-Temperature Reactors. The focus is largely on extending the capabilities of the TRISO-coated fuel system for higher operating temperatures (1250°C) and higher burnups (10 – 20 %). Of greatest concern is the influence of higher fuel temperatures and burnups on fission product interactions with the SiC layer leading to the release of fission products. One of the possible solution consist in addition additional layers with special properties. For example, to prevent the corrosion of the SiC layer by fission product palladium, several types of new combinations of the coating layers have been proposed and tested. The idea is to add a layer that traps palladium by chemical reaction inside the SiC layer. Earlier several kinds of additional layers have been selected: an SiC + PyC layer and an SiC layer. For optimization of TRISO particle it is necessary to determine the number of additional layers, their thickness and composition. This is combinatorial optimization problem (continuous + discrete). Traditional methods rely on manual adjustment and human experience, which is inefficient and difficult to obtain the optimal solution. Therefore it is necessary to develop an automated design method. In the present report variant of such method is proposed based on artificial intelligence approach. There are several meta-heuristic algorithms such as genetic algorithm, neural network and particle swarm optimization algorithm (PSO) which have the ability to solve continuous, discrete and combinatorial optimization problems. Namely PSO algorithm looks especially attractive. Early this method was proven to be reliable and effective in nuclear power problems by applying it in designing a Savannah marine reactor shielding. Nuclear engineering ; Nuclear reactor ; Materials science ; Computer science ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Shuang Zhou | Thang X. Duong | Markus Hillg ̈artner | Rasul Abdusalamov | Mikhail Itskov
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Multiscale Modeling Assisted by Artificial Neural Networks: Application to Woven Fabrics Artificial neural network ; Woven fabric ; Computer science ; Artificial intelligence ; Materials science ; Composite material Textile materials and evaluations ; Composite Material Mechanics ; Mechanical Behavior of Composites
Authors: Chen-Xi Guo | Huiying Yang | Ruijie Zhang
Source: Advances in Manufacturing (article, 2024-04-15)
Field: Materials Science — Materials Chemistry

Accelerating the solving of mechanical equilibrium caused by lattice misfit through deep learning method Precipitation ; Materials science ; Stress (linguistics) ; Lattice (music) ; Thermodynamics ; Statistical physics ; Matrix (chemical analysis) ; Phase (matter) ; Internal stress ; Stress field ; Mechanics ; Physics ; Composite material ; Finite element method Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgical Processes and Thermodynamics
Authors: Alexander Lukin | Oğuz Gülseren
Source: International Journal of Energetic Materials and Chemical Propulsion (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

HARNESSING PHONON WAVE RESONANCE IN CARBYNE-ENRICHED NANO-INTERFACES TO ENHANCE ENERGY RELEASE IN NANOENERGETIC MATERIALS This paper introduces a new nanotechnology-driven approach that provides a transformative pathway to substantially enhance the energy release efficiency of nanoenergetic materials (nEMs) without altering their chemical composition. The groundbreaking concept involves strategically harnessing, self-synchronized collective atomic vibrations and phonon wave resonance phenomena within the transition domain's interconnecting nanocomponents. A key novelty is the incorporation of meticulously engineered two-dimensional-ordered linear-chain carbon-based multilayer nano-enhanced interfaces as programmable nanodevices into these transition domains, facilitated by advanced multistage processing and assembly techniques. These programmable nanodevices enable unprecedented control over the initiation, propagation, and coupling of self-synchronized collective atomic vibrations and phonon waves, unleashing powerful synergistic effects. Central to this approach is the bidirectional, self-reinforcing interaction between precisely tailored nano-architectures and phonon dynamics within the multilayer nano-enhanced interfaces. This synergistic coupling facilitates the rational programming of energy transfer pathways, granting access to previously inaccessible energy reserves inherently locked within the nEM systems. To optimally activate and harness these synergistic mechanisms, a strategic combination of cutting-edge methods is judiciously employed. These include energy-driven stimulation of allotropic phase transformations, surface acoustic wave-assisted manipulation at micro-/nanoscales, heteroatom doping, directed self-assembly driven by high-frequency electromagnetic fields, and a data-driven inverse design framework. Notably, by leveraging a data-driven inverse design strategy rooted in multifactorial neural network predictive models, we uncover previously hidden structure-property relationships governing the nano-enhanced interfaces. This novel data-driven "nanocarbon genome" approach enables rational maximization of energy release efficiency in nEM systems. Overall, this transformative nanoscale concept not only unlocks unprecedented high-energy functionalities but also ushers in significant improvements in environmental sustainability and operational safety for nEMs. Carbyne ; Phonon ; Materials science ; Nano- ; Resonance (particle physics) ; Nanotechnology ; Chemistry ; Atomic physics ; Physics ; Composite material ; Condensed matter physics Thermal properties of materials ; Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites
Authors: Maher Messaoudi | Sayed Mohammad Kameli | Shady S. Refaat | Haitham Abu‐Rub | Mohamed Trabelsi
Source:  (article, 2024-11-03)
Field: Materials Science — Materials Chemistry

Deep Learning Based Corona Discharge Severity Classification for High Voltage Equipment Discharges, such as partial discharges (PDs) and corona discharges (CDs) are the most common faults that occur in insulation materials used in high voltage (HV) equipment. A high repetition rate of discharge activity indicates the severity of the defects that shorten the lifetime of electrical equipment, leading to insulation failure. To solve this, this paper proposes an efficient classification technique for corona discharge defect intensity using features obtained from statistical parameters such as the ignition voltage of CDs. The Recurrent Neural Network (RNN) is proposed to identify the intensity of corona discharges. A comprehensive experimental evaluation is conducted, to demonstrate the capabilities of the proposed solution. The exceptional predictive abilities of the long short-term memory (LSTM) method are the primary benefit of the proposed approach presented, with a potential for enhancing the performance of CD detection systems. The obtained results demonstrate the accuracy of the proposed model, indicating its potential for deployment in practical applications. The innovative approaches utilized in this paper will help engineers and operators quickly determine the severity (sharpness and curvature) of the protrusions or surface defects that cause CDs, solely based on measurements of the ignition voltage. Corona (planetary geology) ; Corona discharge ; Electrostatic discharge ; Partial discharge ; Voltage ; Computer science ; Electrical engineering ; Materials science ; Engineering ; Physics ; Astrobiology High voltage insulation and dielectric phenomena
Authors: Abhinav S. Raman | Annabella Selloni
Source: ChemRxiv (preprint, 2024-12-12)
Field: Materials Science — Materials Chemistry

Are formic and acetic acid covered TiO2 surfaceshydrophobic? Experimental studies under ultrahigh vacuum have reported the spontaneous formation of mixed formate/acetate monolayers on rutile titanium dioxide (TiO2) surfaces previously exposed to ambient air or water. Using deep learning accelerated molecular dynamics combined with enhanced sampling, we show that this is not the case in aqueous environments, where formic and acetic acid localize in the interfacial water layers, but do not displace adsorbed water on the TiO2 surface. This preferred localization is attributed to the strong interaction between these carboxylic acids and the surface oxygens, which is often accompanied by the transfer of the acid proton to the latter. While these localized acids make the anatase TiO2 surface hydrophobic, the higher fraction of adsorbed water dissociation can reverse their effect on rutile – a finding relevant to the UV light-induced hydrophilicity of TiO2. Importantly, the localized acids do not block the undercoordinated surface Ti sites and should not decrease the photocatalytic activity. Formate ; Formic acid ; Adsorption ; Acetic acid ; Rutile ; Carboxylic acid ; Anatase ; Dissociation (chemistry) ; Aqueous solution ; Chemistry ; Monolayer ; Titanium dioxide ; Inorganic chemistry ; Photochemistry ; Photocatalysis ; Oxide ; Chemical engineering ; Organic chemistry ; Catalysis Machine Learning in Materials Science
Authors: Neda Alipour | Ted Burke | Jane Courtney
Source: IET conference proceedings. (article, 2024-10-01)
Field: Materials Science — Polymers and Plastics

Assessment of skin type measurement in image datasets Skin type measurement is crucial for assessing whether image datasets used to train deep learning models, which lack skin type information, are sufficiently diverse. Earlier studies show that skin type measurement methods face challenges in accurately determining the true skin type across all skin tones and various lighting conditions and conclude that skin type measurement is a non-trivial task. As the most widely used method of skin type measurement, individual typology angle (ITA) is investigated here. The Fitzpatrick scale, which categorizes skin based on its color and reaction to ultraviolet light, was used as a ground truth for the performance assessment of ITA. This was done using the dermatologist-labelled data in the PAD-UFES-20 dataset which was modified to create a simplified skin patch dataset. The assessment results show a significant bias toward lighter skin types, a high misclassification rate for adjacent light skin types, and very few correct classifications for darker skin types. The main contributions of this work are: Establishment of a human skin patch dataset with Fitzpatrick skin labels as a benchmark for skin classification; Assessment of ITA performance across all six Fitzpatrick skin types; Initial testing of ITA as a skin type classification method. Image (mathematics) ; Computer science ; Type (biology) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer vision ; Geology Textile materials and evaluations
Authors: Capucine Laprais | Lotfi Berguiga | Guillaume Saint‐Girons | Xavier Letartre | Sébastien Cueff
Source:  (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Programmable multilevel nanophotonics with chalcogenide phase-change materials Phase change materials (PCMs) are currently revolutionizing nanophotonics by providing ways to tune and reconfigure optical functionalities without any moving parts. Building on this phenomenon, the last decade has witnessed many exciting reports of novel devices exploiting PCMs such as for example beam-steering, tunable light emission, reflection and absorption, programmable metasurfaces and reconfigurable neural networks. A large majority of the first studies were using standard PCMs such as GeSbTe as simple binary on-off switches, in which the ON state is the amorphous phase and the OFF state the fully crystalline phase. However, PCMs present another degree of freedom for tunability: the possibility to encode multilevel non-volatile states via partial crystallization. Furthermore, recently a new class of low-loss PCMs emerged (e.g. Sb2S3 and Sb2Se3), with negligible optical absorption in the near-infrared. <br/> <br/> In this paper, we will present recent results on methods to program PCMs into various multilevel states of crystallization. We will then present nanophotonic devices leveraging this multilevel programming and conclude on the perspectives for this technology. Nanophotonics ; Chalcogenide ; Materials science ; Photonics ; Optical switch ; Optoelectronics ; Computer science ; Amorphous solid ; Nanotechnology Phase-change materials and chalcogenides ; Transition Metal Oxide Nanomaterials ; Chalcogenide Semiconductor Thin Films