jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zsolt Divéki | Balázs Nagyillés | Gergely Nagy | Bálint Kiss | E. Cormier | Péter Földi | Katalin Varjú | Subhendu Kahaly | Mousumi Upadhyay Kahaly
Source:  (preprint, 2024-07-19)
Field: Chemistry — Spectroscopy

MIR laser CEP estimation using machine learning concepts in bulk high harmonic generation Monitoring the carrier-envelope phase (CEP) is of paramount importance for experiments involving few cycle intense laser fields. Common measurement techniques include f-2f interferometry or stereo-ATI setups. These approaches are adequate, but are challenging to implement on demand, at different locations as additional metrology tools, in intense few cycle laser-matter interaction experiments, such as those prevalent in sophisticated user beamlines. In addition there are inherent difficulties for CEP measured at non-conventional laser wavelengths (like e.g. mid infrared) and measurements above 10 kHz laser repetition rates, on single shot basis. Here we demonstrate both by simulations and by experiments a machine learning (ML) driven method for CEP estimation in the mid infrared, which is readily generalizable for any laser wavelength and possibly up to MHz repetition rates. The concept relies on the observation of the spectrum of high harmonic generation (HHG) in bulk material and the use of ML techniques to estimate the CEP of the laser. Once the ML model is trained, the method provides a way for cheap and compact real-time CEP tagging. This technique can complement the otherwise sophisticated monitoring of CEP, and is able to capture the complex correlation between the CEP and the observable HHG spectra. Laser ; High harmonic generation ; Estimation ; Harmonic ; Computer science ; Artificial intelligence ; Physics ; Machine learning ; Materials science ; Optics ; Engineering ; Acoustics ; Systems engineering Spectroscopy and Laser Applications ; Laser Design and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zied Hosni | Sofiene Achour | Fatma Saâdi | Yangfan Chen | Mohammed M. Al Qaraghuli
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Nanoparticle Toxicity Toxicity ; Nanoparticle ; Artificial intelligence ; Computer science ; Nanotechnology ; Chemistry ; Materials science Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanoparticle-Based Drug Delivery
Authors: Yu 宇 Meng 蒙 | Shuya 淑雅 Wang 王 | Xibiao 锡标 Ren 任 | Han 涵 Xue 薛 | Xuejun 学军 Yue 岳 | Chuanhong 传洪 Jin 金 | Shanggang 上港 Lin 林 | Fang 芳 Lin 林
Source: Chinese Physics B (article, 2024-12-09)
Field: Materials Science — Materials Chemistry

Combining machine learning algorithms with traditional methods for resolving the atomic-scale dynamic structure of monolayer MoS<sub>2</sub> in high-resolution transmission electron microscopy Abstract High-resolution transmission electron microscopy (HRTEM) promises rapid atomic-scale dynamic structure imaging. Yet, the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs -corrected transmission electron microscopy constrain resolution. A machine learning algorithm is developed to determine the aberration parameters with higher precision from small, lattice-periodic crystal images. The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide (MoS 2 ) monolayer with 25 variables (14 aberrations) resolved in wide ranges. Using these measured parameters, the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS 2 monolayers. The images were acquired due to the unexpected movement of the specimen holder. Four-dimensional data extraction reveals time-varying atomic structures and ripple. In particular, the atomic evolution of the sulfur-vacancy point and line defects, as well as the edge structure near the amorphous, is visualized as the resolution has been improved from about 1.75 Å to 0.9 Å. This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability. Atomic units ; Transmission electron microscopy ; Monolayer ; High-resolution transmission electron microscopy ; Resolution (logic) ; Scale (ratio) ; Computer science ; Algorithm ; Electron tomography ; Materials science ; Electron ; Transmission (telecommunications) ; Energy filtered transmission electron microscopy ; Scanning transmission electron microscopy ; Optics ; Computational science ; Nanotechnology ; Artificial intelligence ; Physics 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Yong Lu | Fawei Zheng
Source: arXiv (Cornell University) (preprint, 2024-01-08)
Field: Materials Science — Materials Chemistry

Lattice thermal conductivity of ZrSe2 based on the anharmonic phonon approach and on-the-fly machine learning force fields The lattice thermal conductivity (LTC) of ZrSe$_2$, a typical layered transition metal disulfide, has been calculated using a hybrid approach that combines force field molecular dynamics (MD) simulation and Boltzmann transport equation (BTE). In this approach, the phonon quasiparticle picture of each normal mode can be obtained directly by the velocity autocorrelation function and its power spectrum projected in $q$-space. By employing the retarded one phonon Green's function method, the phonon quasiparticle frequency and lifetime of each independent normal mode are effectively determined. On-the-fly machine learning force fields combine the precision of quantum mechanics and the scale of classical MD to analyze sizable supercells with long-wavelength phonons. This yields accurate LTC by using sufficient $q$-samples in the Brillouin zone, which cannot be achieved at the \emph{ab initio} molecular dynamics scale. The convergent LTC tensor of bulk and monolayer ZrSe$_2$ decays faster with increasing temperature than the well-known $\frac{1}{T}$ scale, which is typically observed when considering only three-phonon scattering. The phonon lifetime and mean free path exhibit significant dependence on temperature. MD simulations encompass all orders of anharmonic effects, thereby enabling an accurate description of anharmonic interactions between phonons at finite temperatures. Moreover, this approach respects the contribution of each normal mode to the LTC based on the BTE, which facilitates the quantitative analysis of phonon anharmonic properties and the role of specific normal modes. Phonon ; Anharmonicity ; Condensed matter physics ; Brillouin zone ; Quasiparticle ; Thermal conductivity ; Physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Tamim M. Al-Hasan | Xiaojun Zhai | Klaus D. McDonald-Maier | Fayçal Bensaali | A. Cryer
Source:  (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Evaluating Lightweight GAN- and Adapted CTGAN-Based Data Synthesis for Predictive Maintenance in High-Radiation Environments This paper presents a comparative analysis of two developed Generative Adversarial Network (GAN) architectures for synthesizing sensor data in predictive maintenance (PdM) applications within high-radiation environments. The study ad-dresses the challenge of data scarcity in such settings, where experimental runs are constrained by the risk of device failure and economic considerations. The two GAN models: GAN-1 uses the Conditional Tabular GAN (CTGAN) architecture, and GAN-2 employs a custom network. These models generated synthetic datasets that were used to train and evaluate three machine learning algorithms: Random Forest, k-Nearest Neighbours, and eXtreme Gradient Boosting. The performance of these PdM models trained on synthetic data was compared against models trained on the original limited dataset. Results demonstrate that GAN-1 produced synthetic data closely mirroring the characteristics of the original dataset, enabling PdM models to achieve comparable performance levels. This study highlights the potential of GAN-based data synthesis in enhancing PdM model development for high-radiation environments, offering a viable solution to the challenges of limited data availability in such harsh settings. The findings have significant implications for improving operational reliability and safety in nuclear and other extreme environments where electronic systems are deployed. Computer science Graphite, nuclear technology, radiation studies ; Medical Imaging Techniques and Applications ; Nuclear reactor physics and engineering
Authors: Yenan Song | Gang Zhao | Feifei Wang | Ming‐Cui Ding | Xuguang Zhao | Dehua Wang
Source: Physical review. B./Physical review. B (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Pressure-induced change in the local Peierls distortion structure of molten selenium It was recently reported that molten sulfur exhibits a first-order liquid-liquid transition (LLT) with a critical point. The phase diagrams of selenium (Se) and sulfur are very similar, and experiments have suggested that LLT may also happen in molten Se. Here, complete ab initio molecular dynamics simulations (AIMD) and a combination of AIMD and machine learning are used to study the pressure-induced structural change of molten Se. Here the simulation density range is expanded, the number of atoms and simulation time are significantly increased, and the van der Waals correction (VDW) is considered. According to our findings, the liquid structure undergoes two changes along the 1000 K isotherm. Nevertheless, neither change is first order. The significance of the VDW interaction is demonstrated by the perfect agreement of the transition pressure with the experiments after considering the VDW correction. We contend that the increase in conductivity seen in the experiments is connected to the first structural change, which results from the effect of temperature on the weakly Peierls distorted structures. The maximum of the melting curve of crystalline Se-I is associated with the second structural change, which is the pressure-induced Peierls transition. van der Waals force ; Thermodynamics ; Molecular dynamics ; Phase transition ; Melting point ; Distortion (music) ; Chemistry ; Structural change ; Ab initio ; Chemical physics ; Critical point (mathematics) ; Range (aeronautics) ; Sulfur ; Materials science ; Computational chemistry ; Molecule ; Physics ; Organic chemistry Phase-change materials and chalcogenides ; Material Dynamics and Properties ; Chemical Thermodynamics and Molecular Structure
Authors: Anastasia Yu. Sholokhova | Dmitriy D. Matyushin
Source: Journal of Separation Science (article, 2024-11-01)
Field: Chemistry — Spectroscopy

Ready‐to‐use Models Built Using a Diverse Set of 266 Aroma Compounds for the Estimation of Gas Chromatographic Retention Indices for the 50%‐Cyanopropylphenyl‐50%‐Dimethylpolysiloxane Stationary Phase Retention index prediction based on the molecule structure is not often used in practice due to low accuracy, the need to use paid software to calculate molecular descriptors (MD), and the narrow applicability domain of many models. In recent years, relatively accurate and versatile deep learning (DL)-based models have emerged. These models are now used in practice as an additional criterion in gas chromatography-mass spectrometry identification. The DB-225ms stationary phase (usually described as 50%-cyanopropylphenyl-50%-dimethylpolysiloxane in available sources) is widely used, but ready-to-use retention index estimation models are not available for it. This study presents such models. The models are linear and use simple constitutional MD and retention indices predicted by DL for the DB-WAX and DB-624 stationary phases as MD (we show that it is their use that allows us to achieve satisfactory accuracy). The accuracy obtained for a completely unseen hold-out test set: root mean square error 73.2; mean absolute error 45.7; median absolute error 22.0. The models were trained using a retention data set of 266 volatile compounds. All calculations can be performed using the convenient open-source software CHERESHNYA. The final equations are implemented as a spreadsheet and a code snippet and are available online: https://doi.org/10.6084/m9.figshare.26800789. Kovats retention index ; Mean squared error ; Set (abstract data type) ; Software ; Test set ; Aroma ; Chromatography ; Chemistry ; Computer science ; Algorithm ; Gas chromatography ; Mathematics ; Artificial intelligence ; Statistics Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Zhen 珍 Yue 岳 | Jun 君 Li 李 | Lisheng 立胜 Liu 刘 | Hai 海 Mei 梅
Source: Chinese Physics B (article, 2024-05-10)
Field: Materials Science — Materials Chemistry

Quasi-plastic deformation mechanisms and inverse Hall–Petch relationship in nanocrystalline boron carbide under compression Abstract Grain boundaries (GBs) play a significant role in the deformation behaviors of nanocrystalline ceramics. Here, we investigate the compression behaviors of nanocrystalline boron carbide (nB 4 C) with varying grain sizes using molecular dynamics simulations with a machine-learning force field. The results reveal quasi-plastic deformation mechanisms in nB 4 C: GB sliding, intergranular amorphization and intragranular amorphization. GB sliding arises from the presence of soft GBs, leading to intergranular amorphization. Intragranular amorphization arises from the interaction between grains with unfavorable orientations and the softened amorphous GBs, and finally causes structural failure. Furthermore, nB 4 C models with varying grain sizes from 4.07 nm to 10.86 nm display an inverse Hall–Petch relationship due to the GB sliding mechanism. A higher strain rate in nB 4 C often leads to a higher yield strength, following a 2/3 power relationship. These deformation mechanisms are critical for the design of ceramics with superior mechanical properties. Materials science ; Nanocrystalline material ; Boron carbide ; Deformation (meteorology) ; Intergranular corrosion ; Grain size ; Deformation mechanism ; Ceramic ; Amorphous solid ; Grain boundary ; Carbide ; Composite material ; Condensed matter physics ; Metallurgy ; Microstructure ; Crystallography ; Nanotechnology Diamond and Carbon-based Materials Research ; Boron and Carbon Nanomaterials Research ; Microstructure and mechanical properties
Authors: Ziwen Zhou | Chengkang Zhang | Ting Fang | Meiling Tian | Binxiong Liu | Xinrong Zhong | Xinting Zhu | Changcheng Li
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Assessment of Salmon Sashimi Processing Conditions for Listeria Monocytogenes Cross-Contamination and Effectiveness of Clpso-Bp Neural Network Model Constructed for Predicting Microbial Transfer Listeria monocytogenes ; Contamination ; Artificial neural network ; Listeria ; Food science ; Environmental science ; Microbiology ; Chemistry ; Biology ; Artificial intelligence ; Computer science ; Bacteria ; Ecology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Dewei Wang | Jie Bao | Christopher Coyle | Olga A. Marina
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Multi-Physics Modeling and the Sensitivity Analysis for the Critical Factors in Solid Oxide CO<sub>2</sub>-Steam Co-Electrolysis System Performances A multi-physics modeling framework, which includes electrochemical and chemical reactions, mass transfer, and energy balance, has been developed and validated against experiment measurements to investigate the performance of solid oxide CO 2 -steam co-electrolysis (SOEC) under various operating conditions and cell designs. However, multi-physics modeling for complex SOEC systems can be computationally expensive and not tractable for general system design and optimization. Reduced-order models (ROMs) have been proven as a powerful tool for reducing computational costs, closely mimic the high-fidelity SOEC stack models and identify the operational condition parameters to which the SOEC stack is sensitive and needs appropriate controls for stable operation. In this study, the deep neural networks (DNN) algorithm is employed to construct ROMs according to multi-physics simulations for SOECs to systematically investigate the SOECs’ electrochemical performance and rank the contributions of operational condition parameters for both 2 cm 2 button cell and 300 cm 2 planar cells. Sensitivity analysis reveals that cell voltage, fuel composition (denoted by CO 2 /H 2 O ratio), and the operating temperature impact the cell performance most. The product ratio ΔCO/ΔH 2 has a stronger dependence on the CO 2 /H 2 O ratio in the fuel compositions than on other operating condition parameters. The large 300 cm 2 planar cells were modeled under both adiabatic and furnace environment. The simulation and sensitivity analysis indicate that the large cell operating in adiabatic environment usually provides relatively lower performances than the one in furnace environment. Additionally, the adiabatic environment also causes the cell’s performances are more sensitive to the operation conditions, such as external voltage, fuel flow rate, and fuel compositions. The large cell in adiabatic environment also leads to higher internal temperature variation, which may impact the structure reliability. Sensitivity (control systems) ; Electrolysis ; Oxide ; Nuclear engineering ; Materials science ; Process engineering ; Chemical engineering ; Chemistry ; Engineering ; Metallurgy ; Physical chemistry ; Electrode ; Electronic engineering Advancements in Solid Oxide Fuel Cells ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Kuo Zhan | Anemar Bruno Kanj | Lars Heinke
Source: Chemistry - A European Journal (article, 2024-06-19)
Field: Chemistry — Spectroscopy

Cover Feature: Classification and Identification of Perfume Scents by an Enantioselective Colorimetric Sensor Array of Chiral Metal‐Organic‐Framework‐Based Fabry‐Pérot Films (Chem. Eur. J. 39/2024) A colorimetric sensor array based on homochiral metal–organic-framework films in Fabry–Pérot cavities is presented. The sensor read-out is generated by recording the RGB values with a common smartphone camera, and the data are analyzed with a simple machine-learning algorithm. In addition to the enantioselective detection of chiral odor molecules, the sensor system enables the discrimination of a large number of commercial perfume scents. More information can be found in the Research Article by L. Heinke and co-workers (DOI: 10.1002/chem.202400798). Picture by Kuo Zhan. Enantioselective synthesis ; Fabry–Pérot interferometer ; Identification (biology) ; Metal ; Chemistry ; Materials science ; Organic chemistry ; Optoelectronics ; Botany ; Biology Molecular Sensors and Ion Detection ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: Olaiyan Alolaiyan | Shahad Albawardi | Sarah Alsaggaf | Thamer Tabbakh | Frank W. DelRio‬ | Moh. R. Amer
Source: ACS Applied Materials & Interfaces (article, 2024-07-26)
Field: Materials Science — Materials Chemistry

Unlocking High-Performance, Ultra-Low Power van der Waals Photo-Transistors: Toward Back-End-of-Line in-Sensor Machine Vision Applications Recent reports on machine learning and machine vision (MV) devices have demonstrated the potential of two-dimensional (2D) materials and devices. Yet, scalable 2D devices are being challenged by contact resistance and Fermi level pinning (FLP), power consumption, and low-cost CMOS compatible lithography processes. To enable CMOS + 2D, it is essential to find a proper lithography strategy that can fulfill these requirements. Here, we explored a modified van der Waals (vdW) deposition lithography and demonstrated a relatively new class of van der Waals field effect transistors (vdW-FETs) based on 2D materials. This lithography strategy enabled us to unlock high-performance devices evident by high current on-off ratio (<i>I</i><sub>on</sub>/<i>I</i><sub><u>off</u></sub>), high turn-on current density (<i>I</i><sub>on</sub>), and weak FLP. We utilized this approach to demonstrate a gate-tunable near-ideal diode using a MoS<sub>2</sub>/WSe<sub>2</sub> heterojunction with an ideality factor of ∼1.65 and current rectification of 10<sup>2</sup>. We finally demonstrated a highly sensitive, scalable, and ultralow power phototransistor using a MoS<sub>2</sub>/WSe<sub>2</sub> vdW-FET for back-end-of-line integration. Our phototransistor exhibited the highest gate-tunable photoresponsivity achieved to date for white light detection with ultralow power dissipation, enabling ultrasensitive optoelectronic applications such as in-sensor MV. Our approach showed the great potential of modified vdW deposition lithography for back-end-of-line CMOS + 2D applications. Materials science ; Optoelectronics ; Transistor ; van der Waals force ; CMOS ; Rectification ; Photodiode ; Photolithography ; Lithography ; Back end of line ; Nanotechnology ; Power (physics) ; Electrical engineering ; Voltage ; Physics 2D Materials and Applications ; Advanced Memory and Neural Computing ; Nanowire Synthesis and Applications
Authors: Andres Perez Fadon | Gino Cassella | Halvard Sutterud | W. M. C. Foulkes
Source: arXiv (Cornell University) (preprint, 2024-10-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interaction-Induced Symmetry Breaking in Circular Quantum Dots This paper investigates interaction-induced symmetry breaking in circular quantum dots. We explain that the anisotropic static Wigner molecule ground states frequently observed in simulations are created by interference effects that occur even in the non-interacting limit. They have nothing in common with the interaction-driven crystallization of the uniform electron gas described by Wigner. This leads us to define the term Wigner molecule more carefully, via a finite analog of the spontaneous symmetry breaking that arises in the homogeneous electron gas when the interactions are strong. According to this definition, the charge density patterns characteristic of true interaction-induced Wigner molecules can only be seen if a small symmetry-breaking perturbation is applied to a strongly interacting quantum dot. A simple argument based on separation of variables into center-of-mas and internal coordinates shows that the strength of the perturbation required to produce a finite effect on the density tends to zero in the limit as the strength of the interaction tends to infinity. We confirm computationally that interaction-induced Wigner molecules satisfying these two criteria exist. The neural-network variational Monte Carlo method used in our simulations proves more accurate than the coupled cluster and diffusion Monte Carlo methods employed in previous benchmark calculations of quantum dots. Quantum dot ; Physics ; Symmetry breaking ; Symmetry (geometry) ; Spontaneous symmetry breaking ; Quantum ; Quantum mechanics ; Explicit symmetry breaking ; Theoretical physics ; Chiral symmetry breaking ; Global symmetry ; Quantum electrodynamics ; Condensed matter physics ; Geometry ; Mathematics Metamaterials and Metasurfaces Applications ; Quantum and electron transport phenomena ; Microwave and Dielectric Measurement Techniques