jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nitant Gupta | Arthi Jayaraman
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Computational approach for structure generation of anisotropic particles (CASGAP) with targeted distributions of particle design and orientational order The macroscopic properties of materials are governed by their microscopic structure which depends on the materials' composition (<i>i.e.</i>, building blocks) and processing conditions. In many classes of synthetic, bioinspired, or natural soft and/or nanomaterials, one can find structural anisotropy in the microscopic structure due to anisotropic building blocks and/or anisotropic domains formed through the processing conditions. Experimental characterization and complementary physics-based or data-driven modeling of materials' structural anisotropy are critical for understanding structure-property relationships and enabling targeted design of materials with desired macroscopic properties. In this pursuit, to interpret experimentally obtained characterization results (<i>e.g.</i>, scattering profiles) of soft materials with structural anisotropy using data-driven computational approaches, there is a need for creating real space three-dimensional structures of the designer soft materials with realistic physical features (<i>e.g.</i>, dispersity in building block sizes) and anisotropy (<i>i.e.</i>, aspect ratios of the building blocks, their orientational and positional order). These real space structures can then be used to compute and complement experimentally obtained characterization results or be used as initial configurations for physics-based simulations/calculations that can then provide training data for machine learning models. To address this need, we present a new computational approach called CASGAP - Computational Approach for Structure Generation of Anisotropic Particles - for generating any desired three dimensional real-space structure of anisotropic building blocks (modeled as particles) adhering to target distributions of particle shape, size, and positional and orientational order. Anisotropy ; Characterization (materials science) ; Particle (ecology) ; Materials science ; Statistical physics ; Block (permutation group theory) ; Biological system ; Computer science ; Nanotechnology ; Physics ; Geometry ; Optics ; Mathematics Pickering emulsions and particle stabilization ; Diatoms and Algae Research ; Micro and Nano Robotics
Authors: Wei Li | Yuanchuang Lin | Song Li | Tiangui Liang | Zhiliang Cai | Weixiong Wu
Source: ACS Applied Nano Materials (article, 2023-07-12)
Field: Materials Science — Materials Chemistry

Sorption Thermal Energy Storage Performance of Nanoporous Metal–Organic Frameworks and Covalent Organic Frameworks by Grand Canonical Monte Carlo Simulations Developing materials with outstanding performance for sorption thermal energy storage (STES) is vital in utilizing renewable energy. Metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) have attracted much interest for application in STES due to their excellent adsorption properties, including large capacities and stepwise adsorption isotherms. However, the energy density (Qed), an essential property to look at when choosing a suitable material for STES, is still elusive due to the different composition methods in the experiment. This work evaluated and compared the material-based Qed’s of MOFs and COFs for STES via grand canonical Monte Carlo simulations. It was demonstrated that most MOFs exhibited larger Qed than COFs since MOFs tend to have high loading during the charging process. Nevertheless, it was found that one COF exhibited the highest Qed ascribed to the low density and complete desorption during the discharging process, which suggested that COFs can possess excellent performance as long as they achieve sufficient capacity during the charging process. Moreover, the structure–property relationship indicated that large pore volume, relatively small density, suitable carbon atom ratio, and isotropic 3D cage were favorable for large-Qed structures. The successful implementation of data mining and machine learning algorithms paves the way for rational design and speeds up the assessment of the Qed of nanoporous materials. Nanoporous ; Grand canonical ensemble ; Sorption ; Adsorption ; Monte Carlo method ; Materials science ; Isotropy ; Desorption ; Rational design ; Covalent bond ; Nanotechnology ; Thermodynamics ; Chemistry ; Physics ; Physical chemistry ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and Cooling Systems
Authors: Chirag Sedani | Paritosh Chaudhuri | Manoj Kumar Gupta
Source: Nuclear Materials and Energy (article, 2023-05-04)
Field: Materials Science — Materials Chemistry

Heat transfer and fluid flow analysis of pebble bed and its verification with artificial neural network The advancement of sophisticated packed beds has significant implications for the development of new equipment for associated industries. Determining the heat transfer and fluid flow properties of the functional material in the form of a pebble bed is crucial during the design phase of a solid-type ceramic breeding blanket in a fusion reactor. In order to efficiently construct and operate the breeder blanket, the goal of this study is to explore the features of heat transmission and fluid flow. Initially, the heat transfer and fluid flow analyses were carried out independently to benchmark the results using models and experiments using a stainless steel pebble bed with a diameter of 2 mm. Following that, a combined simulation analysis of heat transfer and fluid flow was carried out to demonstrate the system's effective operation for Li2TiO3. A model of an artificial neural network (ANN) has also been employed to forecast the results. The results of simulations are within 5% of the expected values made using ANN. Blanket ; Heat transfer ; Pebble ; Breeder (animal) ; Flow (mathematics) ; Materials science ; Fluid dynamics ; Artificial neural network ; Nuclear engineering ; Mechanical engineering ; Mechanics ; Computer science ; Engineering ; Geology ; Composite material ; Physics ; Artificial intelligence Fusion materials and technologies ; Metallurgical Processes and Thermodynamics ; Granular flow and fluidized beds
Authors: Linyuan Wen | Shiqun Shan | Weipeng Lai | Jinwen Shi | Mingtao Li | Yingzhe Liu | Maochang Liu | Zhaohui Zhou
Source: Molecules (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

Accelerating the Design of High-Energy-Density Hydrocarbon Fuels by Learning from the Data In the ZINC20 database, with the aid of maximum substructure searches, common substructures were obtained from molecules with high-strain-energy and combustion heat values, and further provided domain knowledge on how to design high-energy-density hydrocarbon (HEDH) fuels. Notably, quadricyclane and syntin could be topologically assembled through these substructures, and the corresponding assembled schemes guided the design of 20 fuel molecules (ZD-1 to ZD-20). The fuel properties of the molecules were evaluated by using group-contribution methods and density functional theory (DFT) calculations, where ZD-6 stood out due to the high volumetric net heat of combustion, high specific impulse, low melting point, and acceptable flash point. Based on the neural network model for evaluating the synthetic complexity (SCScore), the estimated value of ZD-6 was close to that of syntin, indicating that the synthetic complexity of ZD-6 was comparable to that of syntin. This work not only provides ZD-6 as a potential HEDH fuel, but also illustrates the superiority of learning design strategies from the data in increasing the understanding of structure and performance relationships and accelerating the development of novel HEDH fuels. Combustion ; Density functional theory ; Hydrocarbon ; Heat of combustion ; Substructure ; Molecule ; Melting point ; Chemistry ; Thermodynamics ; Computational chemistry ; Physics ; Physical chemistry ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Zih-Ying Fu | Fusheng Yang | Min-Ru Wu | Yen-Hung Hung | Liang-Chia Chen
Source:  (article, 2023-08-15)
Field: Materials Science — Surfaces, Coatings and Films

AI-guided numerical-aperture-controlled scatterometry for measurement of deep HAR and thin-film structures with a large depth variation This research means to solve the challenges in measuring deep high-aspect-ratio (HAR) and thin-film structures in 3D integrated circuits. As the semiconductor industry reaches its physical limitations in device scaling, advanced technologies such as advanced lithography and packaging have become crucial in extending Moore's law. This has led to the use of denser nano-to-sub-micron structures in three-dimensional integrated circuits (3D-IC), resulting in smaller, more functional devices. However, measuring these complex and deep HAR and thin-film structures with a large depth range from a few nanometers to a few hundred micrometers using a single optical system is challenging. To address this need, this article presents an AI-guided scatterometry method using numerical aperture control to achieve a large measurement range. The system uses broadband light to generate multi-wavelength reflection responses from the samples. With the help of an electromagnetic simulation tool and an artificial neural network model, the depth resolution can be improved through inverse modeling. The results demonstrate the ability to measure a wide range of samples with depths ranging from nanometers to micrometers scale, including sub-micron HAR openings and ultra-thin films, as long as the measurement bias is controlled within acceptable limits. Materials science ; Nanometre ; Numerical aperture ; Ranging ; Lithography ; Aperture (computer memory) ; Electronic circuit ; Optics ; Integrated circuit ; Thin film ; Reflection (computer programming) ; Optoelectronics ; Computer science ; Wavelength ; Nanotechnology ; Engineering ; Electrical engineering ; Physics ; Telecommunications ; Mechanical engineering Optical Coatings and Gratings ; Advanced Surface Polishing Techniques ; Near-Field Optical Microscopy
Authors: Esmat Mohammadi | Soumil Y. Joshi | Sanket A. Deshmukh
Source: Biomacromolecules (article, 2023-08-21)
Field: Materials Science — Biomaterials

Development, Validation, and Applications of Nonbonded Interaction Parameters between Coarse-Grained Amino Acid and Water Models Interactions between amino acids and water play an important role in determining the stability and folding/unfolding, in aqueous solution, of many biological macromolecules, which affects their function. Thus, understanding the molecular-level interactions between water and amino acids is crucial to tune their function in aqueous solutions. Herein, we have developed nonbonded interaction parameters between the coarse-grained (CG) models of 20 amino acids and the one-site CG water model. The nonbonded parameters, represented using the 12-6 Lennard Jones (LJ) potential form, have been optimized using an artificial neural network (ANN)-assisted particle swarm optimization (PSO) (ANN-assisted PSO) method. All-atom (AA) molecular dynamics (MD) simulations of dipeptides in TIP3P water molecules were performed to calculate the Gibbs hydration free energies. The nonbonded force-field (FF) parameters between CG amino acids and the one-site CG water model were developed to accurately reproduce these energies. Furthermore, to test the transferability of these newly developed parameters, we calculated the hydration free energies of the analogues of the amino acid side chains, which showed good agreement with reported experimental data. Additionally, we show the applicability of these models by performing self-assembly simulations of peptide amphiphiles. Overall, these models are transferable and can be used to study the self-assembly of various biomaterials and biomolecules to develop a mechanistic understanding of these processes. Chemistry ; Molecular dynamics ; Amino acid ; Aqueous solution ; Force field (fiction) ; Biomolecule ; Macromolecule ; Dipeptide ; Molecule ; Water model ; Folding (DSP implementation) ; Computational chemistry ; Chemical physics ; Biological system ; Physical chemistry ; Organic chemistry ; Physics Supramolecular Self-Assembly in Materials ; Protein Structure and Dynamics ; Lipid Membrane Structure and Behavior
Authors: Amit Kumar Choudhary | Anoop Kini | Dominic Hohs | Andreas Jansche | Timo Bernthaler | Orsolya Csiszár | D. Goll | Gerhard Schneider
Source: AIP Advances (article, 2023-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-based Curie temperature prediction for magnetic 14:2:1 phases The TM14RE2B-based phases (TM = transition metal, RE = rare earth metal; hereafter called 14:2:1) enable permanent magnets with outstanding magnetic properties. Novel chemical compositions that represent new 14:2:1 phases necessitate that they do not demagnetize at application-specific operating temperatures. Therefore, an accurate knowledge of the Curie temperature (Tc) is important. For magnetic 14:2:1 phases, we present a machine learning model that predicts Tc by using merely chemical compositional features. Hyperparameter tuning on bagging and boosting models, as well as averaging predictions from individual models using the voting regressor, enables a low mean-absolute-error of 16 K on an unseen test set. The training set and a test set have been constructed by randomly splitting, in an 80:20 ratio, of a database that contains 449 phases (270 compositionally unique) mapped with their Tc, taken from distinct publications. The model correctly identifies the relative importance of key substitutional elements that influence Tc, especially in an Fe base such as Co, Mn, and Al. This paper is expected to serve as a basis for accurate Curie temperature predictions in the sought-after 14:2:1 permanent magnet family, particularly for transition metal substitution of within 20% in an Fe or Co base. Curie temperature ; Test set ; Hyperparameter ; Magnet ; Materials science ; Boosting (machine learning) ; Curie ; Machine learning ; Artificial intelligence ; Computer science ; Condensed matter physics ; Thermodynamics ; Ferromagnetism ; Physics Magnetic Properties of Alloys ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Xin Li | Ziqi Li | Yang Chen | Chao Zhang
Source: Engineering Fracture Mechanics (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

A data-driven approach for predicting the ballistic resistance of elastoplastic materials Ballistic impact ; Ballistic limit ; Projectile ; Structural engineering ; Predictability ; Experimental data ; Materials science ; Fracture (geology) ; Constitutive equation ; Mechanics ; Torsion (gastropod) ; Finite element method ; Computer science ; Engineering ; Mathematics ; Composite material ; Physics High-Velocity Impact and Material Behavior ; Rock Mechanics and Modeling ; Fatigue and fracture mechanics
Authors: Kaveh Mollazade | Norhashila Hashim | Manuela Zude-Sasse
Source: Foods (article, 2023-08-28)
Field: Chemistry — Analytical Chemistry

Towards a Multispectral Imaging System for Spatial Mapping of Chemical Composition in Fresh-Cut Pineapple (Ananas comosus) With increasing public demand for ready-to-eat fresh-cut fruit, the postharvest industry requires the development and adaptation of monitoring technologies to provide customers with a product of consistent quality. The fresh-cut trade of pineapples (<i>Ananas comosus</i>) is on the rise, favored by the sensory quality of the product and mechanization of the cutting process. In this paper, a multispectral imaging-based approach is introduced to provide distribution maps of moisture content, soluble solids content, and carotenoids content in fresh-cut pineapple. A dataset containing hyperspectral images (380-1690 nm) and reference measurements in 10 regions of interest of 60 fruit (<i>n</i> = 600) was prepared. Ranking and uncorrelatedness (based on ReliefF algorithm) and subset selection (based on CfsSubset algorithm) approaches were applied to find the most informative wavelengths in which bandpass optical filters or light sources are commercially available. The correlation coefficient and error metrics obtained by cross-validated multilayer perceptron neural network models indicated that the superior selected wavelengths (495, 500, 505, 1215, 1240, and 1425 nm) resulted in prediction of moisture content with R = 0.56, MAPE = 1.92%, soluble solids content with R = 0.52, MAPE = 14.72%, and carotenoids content with R = 0.63, MAPE = 43.99%. Prediction of chemical composition in each pixel of the multispectral images using the calibration models yielded spatially distributed quantification of the fruit slice, spatially varying according to the maturation of single fruitlets in the whole pineapple. Calibration models provided reliable responses spatially throughout the surface of fresh-cut pineapple slices with a constant error. According to the approach to use commercially relevant wavelengths, calibration models could be applied in classifying fruit segments in the mechanized preparation of fresh-cut produce. Ananas ; Multispectral image ; Chemical composition ; Composition (language) ; Botany ; Biology ; Geography ; Remote sensing ; Chemistry ; Art Spectroscopy and Chemometric Analyses ; Pineapple and bromelain studies ; Postharvest Quality and Shelf Life Management
Authors: Justyna Grzonka | José Marqueses-Rodríguez | Susana Fernández‐Garcia | Xiaowei Chen | José J. Calvino | Miguel López‐Haro
Source: Advanced Intelligent Systems (article, 2023-01-19)
Field: Materials Science — Materials Chemistry

Combining Deep Learning and Compressed Sensing Methods for the 3D Characterization of Ultra‐Thin Epitaxial Layers Grown on Controlled‐Shape Nano‐Oxides Using a nanostructured platform (a controlled‐shape nano‐oxide) and conventional wet impregnation techniques, powder‐type materials have been prepared in which atomically thin surface layers are deposited under very mild conditions. More importantly, an advanced methodology, combining energy dispersive X‐ray spectroscopy‐scanning transmission electron tomography (STEM‐EDX ET) and deep learning denoising techniques, has been developed for the 3D compositional characterization of these unique nanosystems. The complex case of LaO x ‐coated CeO 2 nanocubes is illustrated. For these, aberration corrected 2D STEM‐EDX evidence that ceria nanocubes become covered with a 2–4 atom‐thick layer of a La, Ce‐mixed oxide with spatially varying composition. However, STEM‐EDX ET reveals that this layer distributes unevenly, patching most of the available nanocube surface. The large flexibility and spread availability of the involved synthetic techniques enables, using the tools here developed, a wide exploration of the wealth of questions and applications of these intriguing, atomically thin, surface oxide phases. Materials science ; Characterization (materials science) ; Nanotechnology ; Oxide ; Epitaxy ; Layer (electronics) ; Nano- ; Thin film ; Scanning transmission electron microscopy ; Transmission electron microscopy ; Composite material ; Metallurgy Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science ; Semiconductor materials and devices
Authors: Sivakumar Rathinavelu | Satya Sai Pavan | Senthilkumar Sivaprakasam
Source: Biotechnology and Bioprocess Engineering (article, 2023-02-01)
Field: Chemistry — Analytical Chemistry

Hybrid Model-based Framework for Soft Sensing and Forecasting Key Process Variables in the Production of Hyaluronic Acid by Streptococcus zooepidemicus Bioprocess ; Bioreactor ; Process (computing) ; Industrial and production engineering ; Computer science ; Biomass (ecology) ; Fermentation ; Hyaluronic acid ; Biochemical engineering ; Biological system ; Soft sensor ; Mean squared error ; Process engineering ; Mathematics ; Engineering ; Statistics ; Chemistry ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Polysaccharides Composition and Applications ; Fault Detection and Control Systems
Authors: Tae-Guan Kim | Dadajon Jurakuziev | M. Shaheer Akhtar | O–Bong Yang
Source: Journal of Science Advanced Materials and Devices (article, 2023-03-04)
Field: Materials Science — Materials Chemistry

Machine learning investigation to predict the relationship between photoluminescence and crystalline properties of blue phosphor Ba0.9-xSrxMgAl10O17:Eu2+ The promising optical and photophysical behavior of inorganic phosphors need to be optimized by highly accurate and trustworthy predictions. To control the wavelength converting mechanism of phosphors, the host lattice materials and crystalline morphology should be thoroughly investigated. In this work, we established a relationship between photoluminescence (PL) and crystalline properties by coupling the machine learning (ML) model with our experimental dataset from the synthesized Ba0.9-xSrxMgAl10O17:Eu0.1 (x = 0–0.9). The phosphors (Ba0.9-xSrxMgAl10O17:Eu0.1) were synthesized by the combustion solution method and extensively characterized by morphological, compositional, crystalline, and optical properties. By varying the Ba/Sr ratio, the experimental dataset was coined from the XRD parameters (FWHM, d(Å), crystallite size) and PL wavelengths, which were used to train the ML algorithms. After validating the trained models, we selected five high performing ML models, which blended to build the high performing ensemble Voting Regressor (VR) model with R2 = 0.9987 and RMSE = 0.1327. The best VR model accorded a good relation between crystalline and PL properties of phosphor by controlling the Ba/Sr ratio. These findings suggest that ML prediction models can be excellent preliminary investigations to guide the research of novel inorganic phosphors. Phosphor ; Photoluminescence ; Materials science ; Crystallite ; Wavelength ; Crystal structure ; Computer science ; Analytical Chemistry (journal) ; Optoelectronics ; Chemistry ; Crystallography Luminescence Properties of Advanced Materials ; Advanced Photocatalysis Techniques ; Perovskite Materials and Applications