jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zixin Liu | Jing Jin | Lijie Chen | Jiaping Fu | Hai Lin
Source: Optics Express (article, 2023-09-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metamaterial absorber optimization method based on an artificial neural network surrogate Finding the optimal design parameters for the target EM response of a metamaterial absorber is still a challenging task even if the layout of the absorber has been determined. To effectively address this issue, we introduce the idea of surrogate-based optimization into the area of metamaterial absorber design. This paper proposes a surrogate based optimization method combining artificial neural network (ANN) and trust region algorithm for metamaterial absorbers. Each optimization iteration utilizes the optimal solution from the previous iteration and the sample points surrounding it as the training dataset to build an effective ANN surrogate model. To improve the convergence of the optimization method for metamaterial absorbers based on ANN surrogate model, we incorporate a trust region algorithm. The proposed method employs a simple forward neural network architecture and requires less training data, leading to a quick convergence towards the target solution after only a few iterations. Compared to the three commonly used alternative methods, the proposed method can optimize geometric and material parameters more efficiently in the same time. The validity of the proposed method is demonstrated by two examples of electromagnetic optimizations of metamaterial absorbers. Metamaterial ; Surrogate model ; Artificial neural network ; Computer science ; Convergence (economics) ; Metamaterial absorber ; Optimization problem ; Mathematical optimization ; Algorithm ; Artificial intelligence ; Optics ; Tunable metamaterials ; Mathematics ; Machine learning ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Makoto Urushihara | Masaya Karube | Kenji Yamaguchi | Ryo Tamura
Source: Advanced Photonics Research (article, 2023-10-03)
Field: Materials Science — Materials Chemistry

Optimization of Core–Shell Nanoparticles Using a Combination of Machine Learning and Ising Machine Machine‐learning‐based optimization techniques are widely used for designing complex materials. However, an efficient search for the complex systems, where a combinatorial explosion occurs in the materials search space, is still challenging. Core–shell nanoparticles (CSNPs) are an example of a complex system with a high degree of freedom owing to their complicated structure and multiple constituent materials. In this study, a new black box optimization technique is developed. In this method, the structure of the CSNPs is optimized using an Ising machine and their constituent materials are selected using Bayesian optimization with the optical properties of the materials as the “descriptors”. Aiming for applications to i‐line photolithography, the authors search for CSNPs that are transparent to ultraviolet light of wavelength 355–375 nm and opaque to visible light of wavelength 400–830 nm. The transmittance spectra of the nanoparticles are obtained using a Mie theory calculator. The proposed nanoparticles with the best optical properties have a multilayered structure with a radius of approximately 40 nm and an outer shell composed of either Mg or Pb. The results indicate that a combination of various optimization techniques is more efficient in discovering the better complex materials. Bayesian optimization ; Materials science ; Nanoparticle ; Opacity ; Visible spectrum ; Computer science ; Ultraviolet ; Algorithm ; Nanotechnology ; Artificial intelligence ; Optics ; Physics ; Optoelectronics Machine Learning in Materials Science ; Stochastic Gradient Optimization Techniques ; Neural Networks and Applications
Authors: Wijethunga C.D | Ishanka K.C | Parindya S.D.N | Priyadarshani T.J.N | Buddika Harshanath | Samantha Rajapaksha
Source: International Journal of Engineering and Management Research (article, 2023-10-30)
Field: Chemistry — Inorganic Chemistry

Coconut Plant Disease Identified and Management for Agriculture Crops using Machine Learning This research paper introduces an innovative approach to improve the quality and sustainability of coconut farming and exports in Sri Lanka. It employs advanced image processing techniques to detect, classify, and grade pests and diseases early in coconut palms. This allows for swift interventions and reduces the need for harsh chemical treatments, promoting eco-friendly farming practices. Furthermore, the study goes beyond pest control to evaluate optimal conditions for coconut growth, considering factors like soil quality, water availability, and climate. It empowers farmers with insights to maximize coconut palm yield. Additionally, the system incorporates a growth prediction component using historical data and machine learning, enabling farmers to plan and allocate resources effectively. By combining early pest detection, pest management, growth classification, and predictive analysis, this research offers a comprehensive strategy to enhance Sri Lanka's coconut quality for export. This approach not only improves product quality but also safeguards the industry's sustainability by reducing economic losses and ecological impact. Leveraging cutting-edge tools like image processing and machine learning, this research aims to boost efficiency, economic viability, and international competitiveness in Sri Lanka's coconut farming sector. Agriculture ; Sustainability ; Agricultural engineering ; Quality (philosophy) ; Business ; Integrated pest management ; Agroforestry ; Biotechnology ; Engineering ; Environmental science ; Agronomy ; Biology ; Ecology Coconut Research and Applications ; Date Palm Research Studies ; Oil Palm Production and Sustainability
Authors: Ting Han | Jie Li | Liping Liu | Fengyu Li | Lin‐Wang Wang
Source: New Journal of Physics (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

Accuracy evaluation of different machine learning force field features Abstract Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development. Feature (linguistics) ; Gaussian ; Physics ; Artificial intelligence ; Chebyshev filter ; Mean squared error ; Pattern recognition (psychology) ; Algorithm ; Computer science ; Mathematics ; Quantum mechanics ; Statistics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Márcio S. Gomes-Filho | Alberto Torres | Alexandre Reily Rocha | Luana S. Pedroza
Source: The Journal of Physical Chemistry B (article, 2023-02-02)
Field: Materials Science — Materials Chemistry

Size and Quality of Quantum Mechanical Data Set for Training Neural Network Force Fields for Liquid Water Molecular dynamics simulations have been used in different scientific fields to investigate a broad range of physical systems. However, the accuracy of calculation is based on the model considered to describe the atomic interactions. In particular, <i>ab initio</i> molecular dynamics (AIMD) has the accuracy of density functional theory (DFT) and thus is limited to small systems and a relatively short simulation time. In this scenario, Neural Network Force Fields (NNFFs) have an important role, since they provide a way to circumvent these caveats. In this work, we investigate NNFFs designed at the level of DFT to describe liquid water, focusing on the size and quality of the training data set considered. We show that structural properties are less dependent on the size of the training data set compared to dynamical ones (such as the diffusion coefficient), and a good sampling (selecting data reference for the training process) can lead to a small sample with good precision. Artificial neural network ; Force field (fiction) ; Set (abstract data type) ; Molecular dynamics ; Quality (philosophy) ; Computer science ; Work (physics) ; Experimental data ; Sampling (signal processing) ; Process (computing) ; Data set ; Quantum ; Reference data ; Statistical physics ; Artificial intelligence ; Data mining ; Physics ; Computational chemistry ; Mathematics ; Chemistry ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Shusuke Kasamatsu | Yuichi Motoyama | Kazuyoshi Yoshimi | Tatsumi Aoyama
Source: Science and Technology of Advanced Materials Methods (article, 2023-11-20)
Field: Materials Science — Materials Chemistry

Configuration sampling in multi-component multi-sublattice systems enabled by ab Initio Configuration Sampling Toolkit (abICS) Simulation of the intermediate levels of disorder found in multi-component multi-sublattice systems in various functional materials is a challenging issue, even for state-of-the-art methodologies based on first-principles calculation. Here, we introduce our open-source package ab Initio Configuration Sampling Toolkit (abICS), which combines high-throughput first-principles calculations, machine learning, and parallel extended ensemble sampling in an active learning setting to enable such simulations. The theoretical background is reviewed in some detail followed by brief notes on usage of the software. In addition, our recent applications of abICS to multi-component ionic systems and their interfaces for energy applications are reviewed as a demonstration of the power of this approach. Component (thermodynamics) ; Computer science ; Sampling (signal processing) ; Computational science ; Ab initio ; Software ; Chemistry ; Physics ; Programming language Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Blanca L. Martínez-Vargas | Luis Mario González Rodríguez | Martín Pacheco‐Álvarez | Juan M. Peralta‐Hernández | Alain R. Picos-Benítez
Source: Journal of Chemical Technology & Biotechnology (article, 2023-01-12)
Field: Materials Science — Materials Chemistry

Use of artificial intelligence models for the reduction of nanoparticle size in the synthesis of <scp>ZnO</scp> Abstract BACKGROUND This study evaluates the effectiveness of an artificial intelligence (AI) model for predicting the best experimental conditions to reduce particle size during the synthesis of ZnO nanoparticles. Firstly, an artificial neural networks (ANN) was trained using 52 experimental data from the synthesis of ZnO nanoparticles. The selected input variables were temperature, experimental time, and NaOH concentration, and the output variable was nanoparticle size. The performance of the ANN was measured with the root mean square error and mean absolute percentage error, and the obtained values for the selected ANN were 0.67% and 9.87%, respectively. These values were calculated by using real and predicted values. RESULTS A genetic algorithm (GA) model was coupled with the ANN to find the best operational conditions for the reduced size of ZnO nanoparticles. According to the AI model, a temperature of 59 °C, an experimental time of 56 min, and a concentration of NaOH of 0.08 should be tested to obtain ZnO nanoparticles with 5.67 nm of diameter. After applying the conditions predicted by the model, ZnO nanoparticles with a mean diameter of 5.3 ± 0.4 nm were obtained. The results were confirmed by using several characterization methods, such as approximation of effective masses (5.3 nm), equation Debye–Scherrer (5.2 nm), and high‐resolution transmission electron microscope (HR‐TEM) selected micrograph (5.5 nm). The photocatalytic activity of the synthesized nanoparticles was evaluated using the synthetic dye thymol blue. The best discoloration efficiency was reached by the synthesized ZnO nanoparticles at 74%, while the commercial ZnO only achieved 58%. CONCLUSION According to the ANN‐GA model, it was possible to predict the experimental conditions needed to obtain ZnO nanoparticles with reduced sizes and excellent photocatalytic activity. © 2023 Society of Chemical Industry (SCI). Nanoparticle ; Materials science ; Particle size ; Mean squared error ; Scanning electron microscope ; Micrograph ; Artificial neural network ; Nanotechnology ; Analytical Chemistry (journal) ; Chemical engineering ; Mathematics ; Chromatography ; Composite material ; Machine learning ; Chemistry ; Statistics ; Computer science ZnO doping and properties
Authors: Maryam Abbasi Tarighat | Ameneh Behroozi | Gholamreza Abdi | Charalampos Proestos
Source: Separations (article, 2023-05-29)
Field: Chemistry — Analytical Chemistry

Multivariate Simultaneous Determination of Some PAHs in Persian Gulf Oil-Contaminated Algae and Water Samples Using Miniaturized Triton X-100-Mediated Fe3O4 Nanoadsorbent and UV-Vis Detection This research shows the development of a miniaturized solid-phase extraction method with UV-Vis detection for simultaneous determination of dibenzofuran, fluoranthene and phenanthrene using chemometrics approaches. After synthesis of Fe3O4 nanoparticles (Fe3O4 NPs), the surface of the nanoparticles was modified by Triton X100 coating. The influence of extraction solvent and volume, concentration of Triton X100, extraction time, and sample pH were studied and optimized. Due to high spectral overlapping, resolving ternary mixtures for simultaneous determination of targets with classical analytical methods is impossible. Therefore, the recorded UV-Vis spectra were transformed using continuous wavelet transform and then subjected to artificial neural networks (ANNs). The Db4 mother wavelet was used as the better mother wavelet. For simultaneous detection of analytes, a comparison of feed-forward back-propagation and radial basis function networks was accomplished. The calibration graphs showed linearity in the ranges of 2.4–250 ng mL−1, 50–3750 ng mL−1, and 48–5000 ng mL−1 with a limit of detection of 0.58, 9.5 ng mL−1, and 12.5 ng mL−1 under optimal conditions for phenanthrene, fluoranthene, and dibenzofuran, respectively. The limit of quantitation was achieved at 3.52 ng mL−1, 16.35 ng mL−1, and 31.3 ng mL−1 for phenanthrene, fluoranthene and dibenzofuran, respectively. The method involving TX-100-coated Fe3O4 NPs in a liquid sample phase for analyte extraction, followed by ethanol desorption and UV-Vis detection, was successfully applied for the determination of polycyclic aromatic hydrocarbons in oil-field water and algae samples. Detection limit ; Fluoranthene ; Chromatography ; Extraction (chemistry) ; Chemistry ; Phenanthrene ; Analytical Chemistry (journal) ; Environmental chemistry Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Water Quality Monitoring and Analysis
Authors: Yanyu Lin | Zhangyuan Yan | Chi Shing Tsang | Lok Wing Wong | Xiaodong Zheng | Fangyuan Zheng | Jiong Zhao | Ke Chen
Source: Small Science (article, 2023-06-11)
Field: Materials Science — Materials Chemistry

A Multiscale Deep‐Learning Model for Atom Identification from Low‐Signal‐to‐Noise‐Ratio Transmission Electron Microscopy Images Recent advancements in transmission electron microscopy (TEM) have enabled the study of atomic structures of materials at unprecedented scales as small as tens of picometers (pm). However, accurately detecting atomic positions from TEM images remains a challenging task. Traditional Gaussian fitting and peak-finding algorithms are effective under ideal conditions but perform poorly on images with strong background noise or contamination areas (shown as ultrabright or ultradark contrasts). Moreover, these traditional algorithms require parameter tuning for different magnifications. To overcome these challenges, AtomID-Net is presented, a deep neural network model for atomic detection from multiscale low-SNR experimental images of scanning TEM (scanning transmission electron microscopy (STEM)). The model is trained on real images, which allows the robust and efficient detection of atomic positions, even in the presence of background noise and contamination. The evaluation on a test set of 50 images with a resolution of 800 × 800 yields an average F1-Score of 0.964, which demonstrates significant improvements over existing peak-finding algorithms. Noise (video) ; Artificial intelligence ; Computer science ; Transmission (telecommunications) ; Scanning transmission electron microscopy ; Artificial neural network ; Gaussian noise ; Transmission electron microscopy ; Materials science ; Algorithm ; Optics ; Pattern recognition (psychology) ; Physics ; Image (mathematics) ; Telecommunications Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Yujia Hu | Huantong Shi | Tuan Li | Lun Cheng | Jinliang Hu | Xingwen Li | Wu Jian | Li Chen | Aici Qiu
Source: Powder Technology (article, 2023-02-09)
Field: Materials Science — Materials Chemistry

Underwater pressure waves generated by electrical exploding wire ignited energetic materials: Parametric study and formulation optimization Thermite ; Nitromethane ; Sensitivity (control systems) ; Parametric statistics ; Explosive material ; Materials science ; Ignition system ; Mechanics ; Parametric model ; Pressure wave ; Aluminium ; Acoustics ; Mechanical engineering ; Composite material ; Engineering ; Physics ; Electronic engineering ; Mathematics ; Chemistry ; Aerospace engineering High-Velocity Impact and Material Behavior ; Energetic Materials and Combustion ; Laser-Plasma Interactions and Diagnostics
Authors: Hanxun Jin | Enrui Zhang | Horacio D. Espinosa
Source: arXiv (Cornell University) (review, 2023-03-14)
Field: Materials Science — Materials Chemistry

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano- and micro-mechanics, architected materials, and 2D material. Finally, we highlight some current challenges of applying ML to multi-modality and multi-fidelity experimental datasets and propose several future research directions. This review aims to provide valuable insights into the use of ML methods as well as a variety of examples for researchers in solid mechanics to integrate into their experiments. Computer science ; Field (mathematics) ; Variety (cybernetics) ; Fidelity ; Management science ; Data science ; Artificial intelligence ; Engineering ; Mathematics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Non-Destructive Testing Techniques
Authors: Chengzhi Cai | Lifeng Li | Yafang Guan | Xiaoshan Huang | Shiqing Ke | Wenfei Wang | Yu Li | Yujuan Yang | Hong Liang | Shuhua Li | Yufang Wu | Hanyu Gao | Zhiwei Qiao
Source: Giant (article, 2023-12-14)
Field: Chemistry — Inorganic Chemistry

High-throughput computational screening and molecular fingerprint design of metal-organic framework adsorbents for separation of C3 components To decrease the consumption of energy and material resources caused by the traditional "two-step" process for separating propylene from the C3 crude components, including propane, propylene, methylacetylene and propadiene, this work utilizes the metal-organic frameworks (MOFs) involved "one-step" process to purify propylene. First, the relationship between the geometric/energy descriptors of MOFs and their performance metrics was established through univariate analysis, and results show that the top-performance MOFs can be screened by the differences of LCD, ρ, VSA and ϕ. Then, different combinations of descriptors and algorithms were used for machine learning. The combination (RF algorithm, 7 basic descriptors + PSD + MorganFPs + nodes + topologies) with the best prediction accuracy (R=0.87) for predicting the performance of MOFs was found. Finally, 4 optimal pore structures for the design of high propylene adsorption performance materials were summarized, which mainly contain cylindrical channels and spherical cavities similar in size to the target gas. The microscopic control of pore structures obtained from our bottom-up approach is useful for the development of MOFs and other nanoporous materials which can be used for "one-step" separation of propylene from C3 mixtures in various industrial situations. Metal-organic framework ; Adsorption ; Materials science ; Nanoporous ; Propane ; Fingerprint (computing) ; Process engineering ; Throughput ; Process (computing) ; Network topology ; Topology (electrical circuits) ; Computer science ; Biological system ; Chemical engineering ; Nanotechnology ; Artificial intelligence ; Chemistry ; Organic chemistry ; Mathematics ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis