jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Po‐Yu Chen | Iwona Jasiuk
Source: Journal of Materials Research and Technology (article, 2024-05-01)
Field: Materials Science — Biomaterials

Biological and bio-inspired materials: Multi-scale modeling, artificial intelligence approaches, and experiments Biological materials often possess remarkable properties and functionalities owing to their complex hierarchical and composite structures. Learning from nature can lead to revolutionary breakthroughs in materials science and innovative new technologies. This Special Issue titled “Biological and Bio-inspired Materials: Multi-scale Modeling and Artificial Intelligence Approaches” is a collection of research articles and comprehensive reviews utilizing multi-scale modeling, artificial intelligence approaches, and experiments to elucidate the characteristics of biological materials and design and optimize bio-inspired materials. The computational approaches of interest include but are not limited to molecular dynamics, lattice spring models, finite element analysis, genetic algorithms, neural networks, generative adversarial networks, and other modeling and artificial intelligence approaches for better understanding the structure-property relationships and underlying mechanisms of biological (natural) materials, and reproducing, designing, and optimizing bio-inspired materials. Novel experimental results, fabrication strategies, and applications of biological, bio-inspired and biomedical materials are also collected in this special issue. Scale (ratio) ; Artificial intelligence ; Computer science ; Biochemical engineering ; Machine learning ; Engineering ; Geography ; Cartography Diatoms and Algae Research
Authors: Nguyen Van Hong
Source: Silicon (article, 2024-09-19)
Field: Materials Science — Ceramics and Composites

Structure and Density Heterogeneities of Silica Glass: Insight from Datamining Techniques Materials science ; Silica glass ; Composite material ; Nanotechnology Glass properties and applications ; Material Dynamics and Properties ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Radhakrishnan Anbarasan | Duckjong Kim | Jae Hyun Park
Source: Computational Materials Science (article, 2024-11-16)
Field: Materials Science — Materials Chemistry

Ternary transition-metal nitride halide monolayers MNI (M = Zr, Hf) with low thermal conductivity and high thermoelectric figure of merit Ternary operation ; Halide ; Materials science ; Figure of merit ; Nitride ; Thermal conductivity ; Thermoelectric effect ; Condensed matter physics ; Transition metal ; Monolayer ; Thermal ; Chemistry ; Optoelectronics ; Thermodynamics ; Nanotechnology ; Composite material ; Inorganic chemistry ; Physics ; Layer (electronics) MXene and MAX Phase Materials ; Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications
Authors: M Pavan | Surabhi Mahajan | L. Ammayappan | T. Nageshkumar | Vinay Kumar Midha | B. Manjunatha | Sumeet Grewal
Source: Industrial Crops and Products (article, 2024-02-27)
Field: Materials Science — Polymers and Plastics

Optimization of an alkali modification protocol on Crotalaria juncea fibre and its characterization for technical textile applications Textile ; Crotalaria juncea ; Protocol (science) ; Characterization (materials science) ; Alkali metal ; Pulp and paper industry ; Biochemical engineering ; Chemistry ; Biotechnology ; Materials science ; Agronomy ; Biology ; Nanotechnology ; Engineering ; Composite material ; Organic chemistry ; Medicine Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging
Authors: Yiling Duan | Shangchao Lin | Yunfei Bai | Shichao Liu | Yang Liu
Source: International Journal of Heat and Mass Transfer (article, 2024-05-22)
Field: Materials Science — Materials Chemistry

Experiment-Validated multiphysics modeling, generalizable deep learning and interpretable global sensitivity analyses for thermoelectric generators Multiphysics ; Sensitivity (control systems) ; Artificial intelligence ; Machine learning ; Computer science ; Engineering ; Electronic engineering ; Finite element method Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Advanced Thermodynamics and Statistical Mechanics
Authors: Nikhil Khatavkar | Abhishek K. Singh
Source: Physical Review Materials (article, 2024-05-07)
Field: Materials Science — Materials Chemistry

Combined approach to capture the evolution of oxidation of Nickel based superalloys using data driven approaches Nickel-based superalloys are an exceptional class of materials that are indispensable for high-temperature applications in the aerospace and power sector industries worldwide. The prolonged application of these materials in a demanding environment is hindered by the increased oxidation rates and deformation due to mass gain at high temperatures and the presence of corrosive agents. Calculating the oxidation properties using experimental techniques is laborious and highly cost/time intensive, which presents a considerable challenge to reducing the oxidation in these materials. In this work, we establish an extensive database consisting of the specific mass gain due to oxidation ($\mathrm{\ensuremath{\Delta}}m$) and the parabolic oxidation rates (${\mathrm{k}}_{\mathrm{p}}$) of nickel-based superalloys spanning all the superalloy generations. Highly accurate machine learning (ML) models are developed to predict ($\mathrm{\ensuremath{\Delta}}m$) using artificial neural networks and tree-based XGBoost. The ML models are extended by unsupervised $k$ means clustering to improve the accuracy of the models and generate insights on the composition-property linkages. Additionally, the ML model for ${\mathrm{k}}_{\mathrm{p}}$ developed utilizing XGBoost yields unprecedented results with errors of 0.04. The ML model is analyzed using the SHapely Additive exPlanations parameters to determine the effect of individual features on the model. Further, we employ a genetic algorithm-based approach utilizing the developed ML models to minimize the ${\mathrm{k}}_{\mathrm{p}}$ to improve the performance of the superalloys at high temperatures. The genetic algorithm-assisted optimization successfully yields several compositions for new Ni superalloys with up to 20% reduction in the ${\mathrm{k}}_{\mathrm{p}}$. This work presents essential advances for accelerating the targeted discovery of new materials for highly specialized and demanding applications. Superalloy ; Materials science ; Cluster analysis ; Nickel ; Artificial neural network ; Genetic algorithm ; Artificial intelligence ; Computer science ; Machine learning ; Metallurgy ; Alloy Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep
Authors: Ankur Chatterjee | D. Dziczek | Peng Song | Junyan Liu | Andreas D. Wieck | Michael Pawlak
Source: Journal of Applied Physics (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

Effect of amplitude measurements on the precision of thermal parameters’ determination in GaAs using frequency-resolved thermoreflectance Non-contact photothermal pump-probe methodologies such as Frequency-Domain Thermo-Reflectance (FDTR) systems facilitate the examination of thermal characteristics spanning semiconductor materials and their associated interfaces. We underscore the significance of meticulous measurements and precise error estimation attained through the analysis of both amplitude and phase data in Thermo-Reflectance (TR). The precision of the analytical estimation hinges greatly on the assumptions made before implementing the method and notably showcases a decrease in errors when both the amplitude and phase are incorporated as input parameters. We demonstrate that frequency-domain calculations can attain high precision in measurements, with error estimations in thermal conductivity (k), thermal boundary resistance (Rth), and thermal diffusivity (α) as low as approximately 2.4%, 2.5%, and 3.0%, respectively. At the outset, we evaluate the uncertainty arising from the existence of local minima when analyzing data acquired via FDTR, wherein both the phase and amplitude are concurrently utilized for the assessment of cross-plane thermal transport properties. Expanding upon data analysis techniques, particularly through advanced deep learning approaches, can significantly enhance the accuracy and precision of predictions when analyzing TR data across a spectrum of modulation frequencies. Deep learning models enhance the quality of fitting and improve the accuracy and precision of uncertainty estimation compared to traditional Monte Carlo simulations. This is achieved by providing suitable initial guesses for data fitting, thereby enhancing the overall performance of the analysis process. Amplitude ; Monte Carlo method ; Thermal diffusivity ; Thermal conductivity ; Phase (matter) ; Frequency domain ; Observational error ; Accuracy and precision ; Thermal ; Maxima and minima ; Materials science ; Computer science ; Optics ; Computational physics ; Algorithm ; Physics ; Statistics ; Mathematics ; Mathematical analysis Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Advanced Thermoelectric Materials and Devices
Authors: Wissal Ghabi | Kamel Landolsi | Fraj Echouchene | Abdullah Bajahzar | Moncef Msaddek | Hafedh Belmabrouk
Source: Inorganica Chimica Acta (article, 2024-10-25)
Field: Chemistry — Inorganic Chemistry

Optimizing synthesis and catalytic performance of novel β-keto-enamine complexes of Ni (II) using BBD design and PSO-ANN Chemistry ; Enamine ; Catalysis ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Reactions ; Synthesis and Catalytic Reactions
Authors: Chenxin Zhang | Jie Sun | Jiewei Cheng | Qian Wang
Source: Physical review. B./Physical review. B (article, 2024-11-25)
Field: Materials Science — Materials Chemistry

Ultralow lattice thermal conductivity in quasi-one-dimensional <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Bi</mml:mi><mml:msub><mml:mi mathvariant="normal">I</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> with suppressed phonon coherence The enhanced coherence thermal transport in complex materials poses challenges in achieving ultralow lattice thermal conductivity in crystalline materials. In this work, based on a global structure search, we find a stable quasi-one-dimensional crystal, $q\text{\ensuremath{-}}1\mathrm{D} \mathrm{Bi}{\mathrm{I}}_{3}$, with a simple unit cell containing eight atoms. Using the Wigner thermal transport theory and machine-learning potential, we derive Pearson correlation coefficient matrices based on the calculations of over one million coherent modes, and find that the phonon coherent thermal conductivity is mainly correlated with the generalized group velocity, the frequency interval of coherent phonon modes, and the generalized coherent lifetime. Especially, the unique geometry and hierarchical bonding features of $q\text{\ensuremath{-}}1\mathrm{D} \mathrm{Bi}{\mathrm{I}}_{3}$ induce significant anharmonicity, while the small number of atoms in the unit cell reduces the Wigner time limit for the wavelike tunneling of phonons, suppressing the coherence thermal transport. Thus, the synergistic effect of enhanced phonon scattering and reduced phonon coherence leads to an ultralow lattice thermal conductivity of 0.088 (0.099)$\phantom{\rule{0.16em}{0ex}}\mathrm{W}\phantom{\rule{0.16em}{0ex}}{\mathrm{m}}^{\ensuremath{-}1}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{\ensuremath{-}1}$ in the $x(y)$ direction. Our work demonstrates the significance of suppressing phonon coherence for achieving ultralow lattice thermal conductivity of crystal materials. Algorithm ; Mathematics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Yunzhen Du | Changwei Hao | Zhaocang Meng | Canglong Wang | Kunling Peng | Yuan Tian | Wen-Shan Duan | Lei Yang | Ping Lin | Sheng Zhang
Source: Computational Materials Science (article, 2024-05-14)
Field: Materials Science — Ceramics and Composites

Construction of a neural network potential for SiC and its application in uniaxial tension simulations Uniaxial tension ; Tension (geology) ; Materials science ; Artificial neural network ; Composite material ; Computer science ; Condensed matter physics ; Nanotechnology ; Physics ; Artificial intelligence ; Compression (physics) Advanced ceramic materials synthesis ; Silicon Carbide Semiconductor Technologies ; Advanced Surface Polishing Techniques
Authors: Anastassios Skarlatos | Roberto Miorelli | Christophe Reboud | Frenk Van Den Berg
Source: Inverse Problems (article, 2024-02-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic characterization of steel strips using transient field measurements: global sensitivity analysis and regression from a machine-learning perspective Abstract In this contribution, the magnetic characterization of steel strips is studied using synthetic data of field-gradient transients, which have been produced via the finite integration technique. The material law is described and parameterized using the Jiles–Atherton model. The sensitivity of relevant magnetic indicators with respect to the material parameters is then analyzed using two global methods: Sobol’ indices and δ -sensitivity indices. In order to accelerate the evaluation of these quantities, a fast metamodel is built using machine learning techniques from a simulated dataset. The solution of the inverse problem based on a tailored learning framework is tested for the different proposed identifiers, and their suitability for the magnetic characterization of the material in question is finally discussed. Sobol sequence ; Sensitivity (control systems) ; Parameterized complexity ; Characterization (materials science) ; Transient (computer programming) ; Inverse ; Magnetic field ; Inverse problem ; Field (mathematics) ; Mathematics ; STRIPS ; Kriging ; Perspective (graphical) ; Applied mathematics ; Algorithm ; Artificial intelligence ; Computer science ; Mathematical analysis ; Engineering ; Materials science ; Statistics ; Physics ; Electronic engineering ; Geometry Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Christos E. Athanasiou | Bolei Deng
Source:  (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

Integrating Experiments, Simulations, and Artificial Intelligence to Accelerate the Discovery of High-Performance Green Composites The imperative for incorporating greener materials into the aerospace industry necessitates addressing significant challenges associated with the microstructural variability exhibited by recycled and sustainable feedstocks. In this study, we propose an integrated methodology that combines experimental investigations, finite element analysis, and artificial intelligence to develop sustainable composites with consistent properties. Our approach utilizes a pipeline comprising an automated mechanical tester, a finite element method simulator, and a convolutional neural network predictor to identify and optimize fabrication parameters for achieving desired mechanical characteristics in composites. By employing a nested-loop pipeline, our methodology improves sample efficiency, accuracy, and effectively bridges the gap between simulations and real-world performance. This unique methodology offers a promising avenue for facilitating the adoption of aerospace-appropriate green composites. Materials science ; Computer science ; Composite material ; Artificial intelligence Material Selection and Properties ; Additive Manufacturing and 3D Printing Technologies ; Innovations in Concrete and Construction Materials
Authors: Joe Pitfield | Ned Thaddeus Taylor | Steven Paul Hepplestone
Source: Physical Review Letters (article, 2024-02-07)
Field: Materials Science — Materials Chemistry

Predicting Phase Stability at Interfaces We present the RAFFLE methodology for structural prediction of the interface between two materials and demonstrate its effectiveness by applying it to MgO encapsulated by two layers of graphene. To address the challenge of interface structure prediction, our methodology combines physical insights derived from morphological features observed in related systems with an iterative machine learning technique. This employs physical-based methods, including void-filling and n-body distribution functions to predict interface structures. For the carbon-MgO encapsulated system, we have shown the rocksalt and hexagonal phases of MgO to be the two most energetically stable in the few-layer regime. We demonstrate that monolayer rocksalt is heavily stabilized by interfacing with graphene, becoming more energetically favorable than the graphenelike monolayer hexagonal MgO. The RAFFLE methodology provides valuable insights into interface behavior, and a route to finding new materials at interfaces. Stability (learning theory) ; Phase (matter) ; Materials science ; Computer science ; Physics ; Quantum mechanics ; Machine learning Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Rui Wang | Hongyu Yu | Yang Zhong | Hongjun Xiang
Source: The Journal of Physical Chemistry C (article, 2024-07-24)
Field: Materials Science — Materials Chemistry

Identifying Direct Bandgap Silicon Structures with High-Throughput Search and Machine Learning Methods Utilizations of silicon-based luminescent devices are restricted by the indirect-gap nature of diamond silicon. In this study, a high-throughput method is employed to expedite discoveries of direct-gap silicon crystals. The machine learning (ML) potential is utilized to construct a data set comprising 2637 silicon allotropes, which is subsequently screened using an ML Hamiltonian model and density functional theory calculations, resulting in identification of 47 direct-gap Si structures. We calculate transition dipole moments (TDM), energies, and phonon bandstructures of these structures to validate their performance. Additionally, we recalculate bandgaps of these structures employing the HSE06 functional. Twenty-two silicon allotropes are identified as potential photovoltaic materials. Among them, the energy per atom of Si22-Pm, which has a direct bandgap of 1.27 eV, is 0.026 eV/atom higher than that of diamond silicon. Si18-C2/m, which has a direct bandgap of 0.796 eV, exhibits the highest TDM among the identified structures. Si16-P21/c, which has a direct bandgap of 0.907 eV, has a mass density of 2.316 g/cm3, which is the highest among the identified structures and higher than that of diamond silicon. The structure Si12-P1, which possesses a direct bandgap of 1.69 eV, exhibits the highest spectroscopic limited maximum efficiency (SLME) among identified structures at 32.28%, surpassing that of diamond silicon. This study offers insights into the properties of silicon crystals while presenting a systematic high-throughput method for material discovery. Throughput ; Silicon ; Computer science ; Materials science ; Band gap ; Optoelectronics ; Nanotechnology ; Telecommunications Machine Learning in Materials Science ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Eric R. Beyerle | Pratyush Tiwary
Source: The Journal of Physical Chemistry B (article, 2024-01-11)
Field: Materials Science — Materials Chemistry

Thermodynamically Optimized Machine-Learned Reaction Coordinates for Hydrophobic Ligand Dissociation Ligand unbinding is mediated by its free energy change, which has intertwined contributions from both energy and entropy. It is important, but not easy, to quantify their individual contributions to the free energy profile. We model hydrophobic ligand unbinding for two systems, a methane particle and a C60 fullerene, both unbinding from hydrophobic pockets in all-atom water. Using a modified deep learning framework, we learn a thermodynamically optimized reaction coordinate to describe the hydrophobic ligand dissociation for both systems. Interpretation of these reaction coordinates reveals the roles of entropic and enthalpic forces as the ligand and pocket sizes change. In both cases, we observe that the free-energy barrier to unbinding is dominated by entropy considerations. Furthermore, the process of methane unbinding is driven by methane solvation, while fullerene unbinding is driven first by pocket wetting and then fullerene wetting. For both solutes, the direct importance of the distance from the binding pocket to the learned reaction coordinate is present, but low. Our framework and subsequent feature important analysis thus give useful thermodynamic insight into hydrophobic ligand dissociation problems that are otherwise difficult to glean. Dissociation (chemistry) ; Solvation ; Chemistry ; Ligand (biochemistry) ; Chemical physics ; Reaction coordinate ; Hydrophobic effect ; Entropy (arrow of time) ; Energy landscape ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Molecule ; Physics ; Organic chemistry Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: Masaki Imamura | Kazutoshi Takahashi
Source: Scientific Reports (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Unsupervised learning of spatially-resolved ARPES spectra for epitaxially grown graphene via non-negative matrix factorization This study proposed an unsupervised machine-learning approach for analyzing spatially-resolved ARPES. A combination of non-negative matrix factorization (NMF) and k-means clustering was applied to spatially-resolved ARPES spectra of the graphene epitaxially grown on a SiC substrate. The Dirac cones of graphene were decomposed and reproduced fairly well using NMF. The base and activation matrices obtained from the NMF results reflected the detailed spectral features derived from the number of graphene layers and growth directions. The spatial distribution of graphene thickness on the substrate was clearly visualized by the clustering using the activation matrices acquired via NMF. Integration with k-means clustering enables clear visualization of spatial variations. Our method efficiently handles large datasets, extracting spectral features without manual inspection. It offers broad applicability beyond graphene studies to analyze ARPES spectra in various materials. Angle-resolved photoemission spectroscopy ; Non-negative matrix factorization ; Graphene ; Epitaxy ; Spectral line ; Matrix decomposition ; Unsupervised learning ; Materials science ; Matrix (chemical analysis) ; Chemical physics ; Physics ; Computer science ; Chemistry ; Nanotechnology ; Artificial intelligence ; Quantum mechanics ; Eigenvalues and eigenvectors Graphene research and applications ; Electron and X-Ray Spectroscopy Techniques ; Surface and Thin Film Phenomena
Authors: Surjan Sheik | Raffi Mohammed | Kiran Teeparthi | Y. Raghuvamsi
Source: Journal of The Institution of Engineers (India) Series D (article, 2024-03-17)
Field: Materials Science — Metals and Alloys

Machine Learning-Based Prediction of Intergranular Corrosion Resistance in Austenitic Stainless Steels Exposed to Various Heat Treatments Intergranular corrosion ; Austenite ; Metallurgy ; Materials science ; Corrosion ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Microstructure and Mechanical Properties of Steels