jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yilong Zhou | Sigbjørn Løland Bore | Andrea R. Tao | Francesco Paesani | Gaurav Arya
Source: npj Computational Materials (article, 2023-12-15)
Field: Materials Science — Materials Chemistry

Many-body potential for simulating the self-assembly of polymer-grafted nanoparticles in a polymer matrix Abstract Many-body interactions between polymer-grafted nanoparticles (NPs) play a key role in promoting their assembly into low-dimensional structures within polymer melts, even when the particles are spherical and isotropically grafted. However, capturing such interactions in simulations of NP assembly is very challenging because explicit modeling of the polymer grafts and melt chains is highly computationally expensive, even using coarse-grained models. Here, we develop a many-body potential for describing the effective interactions between spherical polymer-grafted NPs in a polymer matrix through a machine-learning approach. The approach involves using permutationally invariant polynomials to fit two- and three-body interactions derived from the potential of mean force calculations. The potential developed here reduces the computational cost by several orders of magnitude, thereby, allowing us to explore assembly behavior over large length and time scales. We show that the potential not only reproduces previously known assembled phases such as 1D strings and 2D hexagonal sheets, which generally cannot be achieved using isotropic two-body potentials, but can also help discover interesting phases such as networks, clusters, and gels. We demonstrate how each of these assembly morphologies intrinsically arises from a competition between two- and three-body interactions. Our approach for deriving many-body effective potentials can be readily extended to other colloidal systems, enabling researchers to make accurate predictions of their behavior and dissect the role of individual interaction energy terms of the overall potential in the observed behavior. Polymer ; Isotropy ; Materials science ; Nanoparticle ; Nanotechnology ; Chemical physics ; Potential of mean force ; Matrix (chemical analysis) ; Statistical physics ; Biological system ; Computer science ; Molecular dynamics ; Physics ; Chemistry ; Computational chemistry Material Dynamics and Properties ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Keijin Nakaguro | Yuki Mitsuta | Shiro Koseki | Tomohiro Oshiyama | Toshio Asada
Source: Bulletin of the Chemical Society of Japan (article, 2023-08-24)
Field: Materials Science — Materials Chemistry

Computational Approach for Molecular Design of Small Organic Molecules with High Hole Mobilities in Amorphous Phase Using Random Forest Technique and Computer Simulation Method Abstract A molecular design system of small organic molecules was developed to realize high hole mobility in the amorphous phase by using the random forest (RF) technique and a computer simulation method. Since there was no accessible datasets of the hole mobilities in the amorphous phase, we have constructed a datasets of experimental hole mobilities for 321 organic molecules with calculated MO energies to utilize machine learning techniques for molecular design procedures. RF was applied to the machine learning technique using the constructed datasets. The optimized RF exhibited correlation coefficients of 0.885 and 0.764 for the training and testing datasets, respectively. The modified ChemTS to use optimized RF was adapted to design molecules with high hole mobilities. The successive conduction (SC) model that uses ab initio molecular orbital (MO) calculations and the Marcus theory was adapted to ensure the predicted mobilities. The generated molecules were verified to have high calculated hole mobility (the order of 10−2 cm2/Vs) owing to the highest transfer integral and lowest reorganization energy by the theoretical successive conduction (SC) model. The datasets and programs used in this work were publicly released on GitHub. Amorphous solid ; Mobilities ; Phase (matter) ; Chemistry ; Work (physics) ; Molecule ; Organic molecules ; Ab initio ; Electron mobility ; Molecular descriptor ; HOMO/LUMO ; Chemical physics ; Statistical physics ; Computational physics ; Thermodynamics ; Materials science ; Physics ; Optoelectronics ; Crystallography ; Organic chemistry ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Organic Electronics and Photovoltaics
Authors: Srujan Sapkal | Balasubramanian Kandasubramanian | Prashant Dixit | Himanshu Sekhar Panda
Source: Materials Today Energy (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Machine learning aided accelerated prediction and experimental validation of functional properties of K1-xNaxNbO3-based piezoelectric ceramics Electronegativity ; Materials science ; Ceramic ; Piezoelectricity ; Density functional theory ; Ionic radius ; Machine learning ; Computer science ; Ion ; Composite material ; Computational chemistry Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Microwave Dielectric Ceramics Synthesis
Authors: Haiyang Song | Yinghe Zhao | Eleanor Turner | Yu Wu | Yuan Li | Menghao Wu | Guang Feng | Huiqiao Li | Tianyou Zhai
Source: InfoMat (article, 2023-01-03)
Field: Materials Science — Materials Chemistry

Capturing <scp>2D</scp> van der Waals magnets with high probability for experimental demonstration from materials science literature Abstract 2D van der Waals (vdW) magnets have opened intriguing prospects for next‐generation spintronic nanodevices. Machine learning techniques and density functional theory calculations enable the discovery of 2D vdW magnets to be accelerated; however, current computational frameworks based on these state‐of‐the‐art approaches cannot offer probability analysis on whether a 2D vdW magnet can be experimentally demonstrated. Herein, a new framework can be established to overcome this challenge. Via the framework, 2D vdW magnets with high probability for experimental demonstration are captured from materials science literature. The key to the successful establishment is the introduction of the theory of mutual information. Historical validation of predictions substantiates the high reliability of the framework. For example, half of the 30 2D vdW magnets discovered in the literature published prior to 2017 have been experimentally demonstrated in the subsequent years. This framework has the potential to become a revolutionary force for progressing experimental discovery of 2D vdW magnets. image van der Waals force ; Magnet ; Spintronics ; Reliability (semiconductor) ; Density functional theory ; Physics ; Key (lock) ; Statistical physics ; Computer science ; Nanotechnology ; Materials science ; Condensed matter physics ; Ferromagnetism ; Quantum mechanics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Yijun Cai | Yangpeng Huang | Naixing Feng | Zhixiang Huang
Source: IEEE Transactions on Microwave Theory and Techniques (article, 2023-03-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Improved Transformer-Based Target Matching of Terahertz Broadband Reflective Metamaterials With Monolayer Graphene On-demand metamaterial designs aided by artificial intelligence have hitherto received tremendous attention recently. However, the traditional deep neural networks (DNNs) still show the limited generalization ability in the inverse design of tunable graphene-based terahertz (THz) metamaterial. In this article, we propose two kinds of DNNs based on the self-attention mechanism to implement the inverse design of tunable broadband reflectors working in the THz band. Moreover, the proposed networks have been improved, so that they could adapt to different types of input vector or matrix in terms of different kinds of on-demand design requirements. Besides, adaptive batch normalization (BN) layers are introduced in our improved networks to accelerate the converging speed with low computational consumption. It could be shown in experiments that the proposed networks exhibit higher accuracy and faster convergence speed than the traditional neural networks, such as multilayer perceptron (MLP) and convolutional neural network (CNN). Finally, this work may provide a key guide for developing THz metamaterials with 2-D materials employing DNNs based on self-attention mechanism. Metamaterial ; Terahertz radiation ; Computer science ; Broadband ; Convolutional neural network ; Bandwidth (computing) ; Artificial neural network ; Electronic engineering ; Artificial intelligence ; Telecommunications ; Materials science ; Optoelectronics ; Engineering Metamaterials and Metasurfaces Applications ; Millimeter-Wave Propagation and Modeling ; Antenna Design and Analysis
Authors: Elizabeth Stippell | Lorena Alzate-Vargas | Kashi N. Subedi | Roxanne Tutchton | M. Cooper | Sergei Tretiak | Tammie Gibson | Richard A. Messerly
Source: Artificial Intelligence Chemistry (article, 2023-12-26)
Field: Materials Science — Materials Chemistry

Building a DFT+U machine learning interatomic potential for uranium dioxide Despite uranium dioxide (UO2) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging UO2 historical operating experience. Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available. Uranium dioxide ; Density functional theory ; Computer science ; Force field (fiction) ; Nuclear fuel ; Statistical physics ; Simulation ; Uranium ; Artificial intelligence ; Chemistry ; Physics ; Computational chemistry ; Nuclear physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Bora Timurkutluk | Yelda Ciflik | Güray Sonugür | Tolga Altan | Ömer Genç | Andaç Batur Çolak
Source: Powder Technology (article, 2023-04-13)
Field: Materials Science — Materials Chemistry

Microstructural design of solid oxide fuel cell electrodes by micro-modeling coupled with artificial neural network Solid oxide fuel cell ; Backpropagation ; Artificial neural network ; Biological system ; Electrode ; Materials science ; Conjugate gradient method ; Software ; Artificial intelligence ; Computer science ; Electrolyte ; Algorithm ; Chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Machine Learning and ELM
Authors: Amanda S. Barnard | Bronwyn Fox
Source: Chemistry of Materials (article, 2023-08-25)
Field: Materials Science — Materials Chemistry

Importance of Structural Features and the Influence of Individual Structures of Graphene Oxide Using Shapley Value Analysis The application of machine learning (ML) to materials chemistry can accelerate the design process, and when coupled with a detailed explanation, can guide future research. Shapley value analysis is a complementary approach capable of providing a comprehensive analysis of the underlying reasons behind a structure/property relationship. In this study, we have used data sets of graphene oxide nanomaterials generated using electronic structure simulations to train ML models with outstanding accuracy, generalizability, and stability to predict the formation energy and the Fermi energy and applied Shapley value analysis to understand the results. Feature important profiles that rank the value of structural characteristics to each property confirmed that the underlying structure/property relationships are relatively simple and scientifically intuitive, even though the ML models need complex information to achieve high performance. We have also reported instance influence profiles that rank the value of each individual graphene oxide structure to the training process. Feature/instance interactions are also investigated to explain which structural characteristics make particular structures influential, revealing that the most influential structures typically have very high or very low concentrations of H or O. Since the range of concentrations is typically chosen by researchers based on domain knowledge at the outset, this highlights that extreme care should be taken when gathering training data as these decisions will have a very big impact on the final model once trained. In general, the reproducible workflow demonstrated here can be applied to any similar materials data set to make reliable model-agnostic predictions of how the structural characteristics and individual structures contribute to the prediction of functional properties. Property (philosophy) ; Computer science ; Graphene ; Workflow ; Process (computing) ; Shapley value ; Stability (learning theory) ; Set (abstract data type) ; Feature (linguistics) ; Generalizability theory ; Artificial intelligence ; Machine learning ; Data mining ; Nanotechnology ; Materials science ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: ChunLei Shang | Chuanjun Wang | Hong‐Hui Wu | Wenyue Liu | Yimian Chen | Guangfei Pan | Shuize Wang | Guilin Wu | Junheng Gao | Haitao Zhao | Chaolei Zhang | Xinping Mao
Source: Science China Technological Sciences (article, 2023-05-25)
Field: Materials Science — Metals and Alloys

Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel Charpy impact test ; Pipeline (software) ; Production (economics) ; Production line ; Toughness ; Computer science ; Pipeline transport ; Work (physics) ; Line (geometry) ; Engineering ; Materials science ; Mechanical engineering ; Composite material ; Mathematics Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Material Properties and Failure Mechanisms
Authors: Yong-Kyoung Kim | Jianwei Qin | Insuck Baek | Kyung-Min Lee | Sung-Youn Kim | Seyeon Kim | Diane E. Chan | Timothy J. Herrman | Namkuk Kim | Moon S. Kim
Source: Current Research in Food Science (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Detection of aflatoxins in ground maize using a compact and automated Raman spectroscopy system with machine learning Consumption of aflatoxin-contaminated food can cause severe illness when consumed by humans or livestock. Because the mycotoxin frequently occurs in cereal grains and other agricultural crops, it is crucial to develop portable devices that can be used non-destructively and in real-time to identify aflatoxin-contaminated food materials during early stages of harvesting or processing. In this study, an aflatoxin detection method was developed using a compact Raman device that can be used in the field. Data were obtained using maize samples naturally contaminated with aflatoxin, and the data were analyzed using a machine learning method. Of the multiple classification models evaluated, such as linear discriminant analysis (LDA), linear support vector machines (LSVM), quadratic discriminant analysis (QDA), and quadratic support vector machines and spectral preprocessing methods, the best classification accuracy was achieved at 95.7% using LDA in combination with Savitzky-Golay 2nd derivative (SG2) preprocessing. Partial least squares regression (PLSR) models demonstrated a close-range accuracy within the scope of standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing methods, with determination of coefficient values of R<sup>2</sup><sub>C</sub> and R<sup>2</sup><sub>V</sub> of 0.9998 and 0.8322 respectively for SNV, and 0.9916 and 0.8387 respectively for MSC. This study demonstrates the potential use of compact and automated Raman spectroscopy, coupled with chemometrics and machine learning methods, as a tool for rapidly screening food and feed for hazardous substances at on-site field processing locations. Aflatoxin ; Artificial intelligence ; Linear discriminant analysis ; Quadratic classifier ; Support vector machine ; Chemometrics ; Mathematics ; Pattern recognition (psychology) ; Computer science ; Machine learning ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Mycotoxins in Agriculture and Food
Authors: Xin Zheng | Guozhu Jia
Source: Fuel (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

Active learning based reverse design of hydrogen production from biomass fuel Hydrogen production ; Biomass (ecology) ; Computer science ; Renewable energy ; Machine learning ; Process engineering ; Production (economics) ; Environmental science ; Artificial intelligence ; Hydrogen ; Engineering ; Chemistry Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Process Optimization and Integration
Authors: Mahmoud M.A. Nassar | Khalid I. Alzebdeh | Mahmoud Alsafy | Sujan Piya
Source: Journal of the Brazilian Society of Mechanical Sciences and Engineering (article, 2023-10-31)
Field: Materials Science — Polymers and Plastics

Optimizing drilling parameters for minimizing delamination in polypropylene-date palm fiber bio-composite materials Delamination (geology) ; Drilling ; Response surface methodology ; Materials science ; Composite material ; Polypropylene ; Drill ; Machining ; Design of experiments ; Drill bit ; Composite number ; Artificial neural network ; Structural engineering ; Mechanical engineering ; Engineering ; Computer science ; Mathematics ; Geology ; Machine learning ; Metallurgy Natural Fiber Reinforced Composites ; Advanced machining processes and optimization ; Advanced Machining and Optimization Techniques
Authors: Hamed Hosseinian | Martín Francisco Jiménez | Mazhar Sher | Aida Rodríguez‐García | Sergio O. Martínez‐Chapa | Samira Hosseini
Source: Scientific Reports (article, 2023-05-01)
Field: Materials Science — Biomaterials

An origami-based technique for simple, effective and inexpensive fabrication of highly aligned far-field electrospun fibers Fabrication of highly aligned fibers by far-field electrospinning is a challenging task to accomplish. Multiple studies present advances in the alignment of electrospun fibers which involve modification of the conventional electrospinning setup with complex additions, multi-phased fabrication, and expensive components. This study presents a new collector design with an origami structure to produce highly-aligned far-field electrospun fibers. The origami collector mounts on the rotating drum and can be easily attached and removed for each round of fiber fabrication. This simple, effective, and inexpensive technique yields high-quality ultra-aligned fibers while the setup remains intact for other fabrication types. The electrospun poly(ɛ-caprolactone) (PCL) fibers were assessed by scanning electron microscope (SEM), fiber diameter distribution, water contact angle (WCA), Fast Fourier Transform analysis (FFT), surface plot profile, and pixel intensity plots. We thoroughly explored the impact of influential parameters, including polymer concentration, injection rate, collector rotation speed, distance from the collector to the tip, and needle gauge number on fibers' quality and alignment. Moreover, we employed machine learning algorithms to predict the outcomes and classify the high-quality fibers instead of low-quality productions. Fabrication ; Simple (philosophy) ; Nanotechnology ; Electrospinning ; Computer science ; Materials science ; Near and far field ; Field (mathematics) ; Composite material ; Physics ; Polymer ; Medicine ; Optics ; Mathematics Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Silk-based biomaterials and applications
Authors: Rajib Nandee | Mohammad Asaduzzaman Chowdhury | Md. Arefin Kowser | Sagar Kumer Nondy | Nayem Hossain | Md Rasadujjaman | Assadullah Al Mostazi | Md. Baizid Molla | Sosmoy Barua | Md Masud Rana | Md. Sherajul Islam
Source: Results in Chemistry (article, 2023-11-24)
Field: Materials Science — Materials Chemistry

Bandgap formation in graphene doped with BN, TiO2, Al2O3 and ZnO by sintering process Nanoelectronics emphasises graphene and bandgap opening, where a larger band gap is helpful for different electronic applications. Here, we report on the larger bandgap opening and tuneable bandgap in graphene doped with BN, TiO2, Al2O3 and ZnO by the novel sintering process. In this study, the electronic band structure of graphene is studied systematically in the presence of doping by UV-vis spectroscopy; studies revealed a bandgap formation in graphene. For the first time, a significant and larger bandgap as high as 2.65 eV is observed for graphene-95% TiO2-3% BN-2% combination, and the TiO2 doping and isoelectronic BN opened the π π* band gap of graphene. The quantum confinement effect confined the holes and electrons in the nanoscale semiconductors. As a result, the semiconductor’s band gap increases the energy difference between the empty and filled states. The experimental results are varified by a theoretical approach called machine learning. The current findings open the door to creating graphene-based devices doped with BN, TiO2, Al2O3, and ZnO tailored for band gaps. Graphene ; Band gap ; Materials science ; Doping ; Semiconductor ; Optoelectronics ; Nanoelectronics ; Nanotechnology ; Graphene nanoribbons ; Wide-bandgap semiconductor Graphene research and applications ; Semiconductor materials and devices ; 2D Materials and Applications