jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jong Hak Lee | N. Duane Loh | Zhen Yuan Yeo | Yong Kang Ong | Deepan Balakrishnan | Carlos Limpo | Abhik Datta | Cagdas Cetin | Shoucong Ning | Clarissa Wong | Jian Shi | Fuchen Hou | Junhao Lin | Tadahiro Minamikawa | Tomonori Ito | Hiroyuki Kamisuki | Stephen J. Pennycook | Paul Matsudaira | Barbaros Özyilmaz
Source: Advanced Materials (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Engineering a Hierarchy of Disorder: A New Route to Synthesize High‐Performance 3D Nanoporous All‐Carbon Materials** A new nanoporous amorphous carbon (NAC) structure that achieves both ultrahigh strength and high electrical conductivity, which are usually incompatible in porous materials is reported. By using modified spark plasma sintering, three amorphous carbon phases with different atomic bonding configurations are created. The composite consisted of an amorphous sp<sup>2</sup>-carbon matrix mixed with amorphous sp<sup>3</sup>-carbon and amorphous graphitic motif. NAC structure has an isotropic electrical conductivity of up to 12 000 S m<sup>-1</sup>, Young's modulus of up to ≈5 GPa, and Vickers hardness of over 900 MPa. These properties are superior to those of existing conductive nanoporous materials. Direct investigation of the multiscale structure of this material through transmission electron microscopy, electron energy loss spectroscopy, and machine learning-based electron tomography revealed that the origin of the remarkable material properties is the well-organized sp<sup>2</sup>/sp<sup>3</sup> amorphous carbon phases with a core-shell-like architecture, where the sp<sup>3</sup>-rich carbon forms a resilient core surrounded by a conductive sp<sup>2</sup>-rich layer. This research not only introduces novel materials with exceptional properties but also opens new opportunities for exploring amorphous structures and designing high-performance materials. Materials science ; Nanoporous ; Nanotechnology ; Hierarchy ; Carbon fibers ; Composite material Nanoporous metals and alloys ; Electrocatalysts for Energy Conversion ; Nanomaterials for catalytic reactions
Authors: Yue Wu | Tianhao Su | Boxue Du | Shunbo Hu | Jie Xiong | Deng Pan
Source: The Journal of Physical Chemistry Letters (article, 2024-12-10)
Field: Materials Science — Materials Chemistry

Kolmogorov–Arnold Network Made Learning Physics Laws Simple In recent years, contrastive learning has gained widespread adoption in machine learning applications to physical systems primarily due to its distinctive cross-modal capabilities and scalability. Building on the foundation of Kolmogorov-Arnold Networks (KANs) [Liu, Z. et al. Kan: Kolmogorov-arnold networks. <i>arXiv</i> 2024, 2404.19756], we introduce a novel contrastive learning framework, Kolmogorov-Arnold Contrastive Crystal Property Pretraining (KCCP), which integrates the principles of CLIP and KAN to establish robust correlations between crystal structures and their physical properties. During the training process, we conducted a comparative analysis between Multilayer Perceptron (MLP) and KAN, revealing that KAN significantly outperforms MLP in both accuracy and convergence speed for this task. By extending the capabilities of contrastive learning to the realm of physical systems, KCCP offers a promising approach for constructing cross-data structural and cross-modal physical models, representing an area of considerable potential. Simple (philosophy) ; Statistical physics ; Theoretical physics ; Physics ; Mathematics education ; Psychology ; Epistemology ; Philosophy Machine Learning in Materials Science ; Neural Networks and Applications ; Statistical Mechanics and Entropy
Authors: Monday Omotioma | O.D. Onukwuli | Ikechukwu A. Nnanwube
Source: Heliyon (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Testing the inhibition efficiency of the castor oil leaf as corrosion inhibitor of mild steel in H2SO4 This study focused on the testing of castor oil leaf as corrosion inhibitor of mild steel in H<sub>2</sub>SO<sub>4</sub>. It involved the examination of the dynamics of molecular forces at the inhibitor and mild steel interface. Findings of the study will promote industrial diversification of castor oil leaf. On the experiment, bio-molecular compounds of the leaf were identified by gas chromatography mass spectrophotometer. Then, gravimetric and potentiodynamic polarization techniques were employed, where the efficacy and type of the inhibitor were determined. Considering the gravimetric process, factors of inhibitor concentration (0.2 g/L - 1.0 g/L), temperature (303 K-333 K) and time (8 h-24 h) were considered, with the inhibitor efficiency as the response. The efficiency was determined and optimized using response surface methodology (RSM) and artificial neural network (ANN). On the other hand, potentiodynamic polarization process was used to identify the type of inhibitor. Adsorption quantities were ascertained through the subjection of the experimental raw data to various adsorption isotherms. The analyses showed that castor oil leaf contains methyl nicotinate and pyridine-4-carbohydrazide. The bio-molecular compounds possess heteroatoms <b>(</b>N, S and O<b>)</b>, signifying strong corrosion inhibitive tendencies<b>.</b> High efficiency (96.25 %) was obtained, with ANN having better prediction capability than the RSM. Potentiodynamic polarization curves revealed castor oil leaf as mixed-type inhibitor<b>.</b> On the adsorption phenomenon, the process of the inhibition was best fitted by Frumkin isotherm, revealing physical adsorption mechanism of Gibb's free energy less than -40 kJ/mol. As a mixed-type inhibitor, castor oil leaf should be applied for mitigation of anodic and cathodic corrosion. Castor oil ; Corrosion inhibitor ; Corrosion ; Materials science ; Metallurgy ; Chemistry ; Organic chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Amirhossein D. Naghdi | Franco Pellegrini | Emine Küçükbenli | Dario Massa | F. J. Domínguez-Gutiérrez | Efthimios Kaxiras | Stefanos Papanikolaou
Source: Acta Materialia (article, 2024-07-24)
Field: Materials Science — Materials Chemistry

Neural network interatomic potentials for open surface nano-mechanics applications Material characterization in nano-mechanical tests may provide information on the potential heterogeneity of mechanical properties. Here, we develop a robust neural-network interatomic potential (NNIP), and we provide a test for the example of molecular dynamics (MD) nanoindentation, and the case of body-centered cubic crystalline molybdenum (Mo). We employ a similarity measurement protocol, using standard local environment descriptors, to select ab initio configurations for the training dataset that capture the behavior of the indented sample. We find that it is critical to include generalized stacking fault (GSF) configurations, featuring a dumbbell self-interstitial on the surface, to capture dislocation cores, and also high-temperature configurations with frozen atom layers for the indenter tip contact. We develop a NNIP with distinct dislocation nucleation mechanisms, realistic generalized stacking fault energy (GSFE) curves, and an informative energy landscape for the atoms on the sample surface during nanoindentation. We compare our NNIP results with nanoindentation simulations, performed with three existing potentials – an embedded atom method (EAM) potential, a gaussian approximation potential (GAP), and a tabulated GAP (tabGAP) potential – that predict different dislocation nucleation mechanisms, and display the absence of essential information on the shear stress at the sample surface in the elastic region. Finally, we compared our NNIP nanoindentation results with experiments, showing reliable predictions for reduced Young’s modulus and observable slip traces. Materials science ; Nano- ; Interatomic potential ; Surface (topology) ; Artificial neural network ; Nanotechnology ; Statistical physics ; Molecular dynamics ; Composite material ; Quantum mechanics ; Computer science ; Physics ; Artificial intelligence ; Geometry ; Mathematics Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Electronic and Structural Properties of Oxides
Authors: Hamada A. A. Noreldeen | Shao‐Bin He | Gang-Wei Wu | Hua‐Ping Peng | Hao‐Hua Deng | Wei Chen
Source: Talanta (article, 2024-08-06)
Field: Materials Science — Materials Chemistry

Deep convolutional neural network-based 3D fluorescence sensor array for sugar identification in serum based on the oxidase-mimicking property of CuO nanoparticles Chemistry ; Sugar ; Fluorescence ; Biological system ; Glucose oxidase ; Nanoparticle ; Convolutional neural network ; Deep learning ; Identification (biology) ; Nanotechnology ; Biosensor ; Artificial intelligence ; Biochemistry ; Computer science ; Botany Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Yong Lu | Fawei Zheng
Source: Physical review. B./Physical review. B (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Lattice thermal conductivity of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>ZrSe</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> based on the anharmonic phonon approach and on-the-fly machine learning force fields The lattice thermal conductivity (LTC) of ${\mathrm{ZrSe}}_{2}$, a typical layered transition metal disulfide, has been calculated using a hybrid approach that combines force field molecular dynamics (MD) simulation and Boltzmann transport equation (BTE). In this approach, the phonon quasiparticle picture of each normal mode can be obtained directly by the velocity autocorrelation function and its power spectrum projected in $q$ space. By employing the retarded one phonon Green's function method, the phonon quasiparticle frequency and lifetime of each independent normal mode are effectively determined. On-the-fly machine learning force fields combine the precision of quantum mechanics and the scale of classical MD to analyze sizable supercells with long-wavelength phonons. This yields accurate LTC by using sufficient $q$ samples in the Brillouin zone, which cannot be achieved at the ab initio molecular dynamics scale. The convergent LTC tensor of bulk and monolayer ${\mathrm{ZrSe}}_{2}$ decays faster with increasing temperature than the well-known $\frac{1}{T}$ scale, which is typically observed when considering only three phonon scattering. The phonon lifetime and mean free path exhibit significant dependence on temperature. MD simulations encompass all orders of anharmonic effects, thereby enabling an accurate description of anharmonic interactions between phonons at finite temperatures. Moreover, this approach respects the contribution of each normal mode to the LTC based on the BTE, which facilitates the quantitative analysis of phonon anharmonic properties and the role of specific normal modes. Phonon ; Anharmonicity ; Quasiparticle ; Brillouin zone ; Condensed matter physics ; Physics ; Thermal conductivity ; Materials science ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Michela Albano‐Gaglio | Puneet Mishra | Sara W. Erasmus | Juan Florencio Tejeda | Albert Brun | Begonya Marcos | Cristina Zomeño | Maria Font‐i‐Furnols
Source: Meat Science (article, 2024-09-06)
Field: Chemistry — Analytical Chemistry

Visible and near-infrared spectral imaging combined with robust regression for predicting firmness, fatness, and compositional properties of fresh pork bellies VNIR ; Partial least squares regression ; Imaging spectroscopy ; Mathematics ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Hyperspectral imaging ; Statistics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Animal Nutrition and Physiology
Authors: Junhong Qiu | Yuduan Lin | Jiaqing Wu | Yuhui Xiao | Honghao Cai | Hui Ni
Source: International Journal of Food Science & Technology (article, 2024-01-15)
Field: Chemistry — Analytical Chemistry

Rapid beef quality detection using spectra pre‐processing methods in electrical impedance spectroscopy and machine learning Summary The fraudulent practice of beef adulteration is a growing concern, as it violates consumer rights. Electrical impedance spectroscopy (EIS) combined with machine learning has emerged as a widely used approach to identify low‐quality meat. Unlike traditional biochemical methods that require expensive instruments, complex sample preparation, and chemical reagents, EIS is a cost‐effective alternative. However, EIS data are susceptible to temperature fluctuations, requiring a waiting period under consistent temperature conditions for data stabilisation before measurements. This process becomes impractical when dealing with a large number of samples. To overcome this limitation, standardisation, normalisation, and smoothing methods were introduced in the meat quality detection based on EIS data. A recognition model for detecting carrageenan adulteration in beef was established. Under an inconsistent temperature condition, by applying the spectra pre‐processing methods to the prediction dataset, the model accuracy reached 84%, whereas the accuracy of the unprocessed prediction dataset dropped to 54%. This study demonstrates that acquiring EIS data under consistent temperature conditions is unnecessary if proper spectra pre‐processing methods are applied. By eliminating the waiting time for data stabilisation, this practical approach enhances the efficiency and accuracy of meat quality detection. Quality (philosophy) ; Smoothing ; Computer science ; Dielectric spectroscopy ; Artificial intelligence ; Machine learning ; Sample (material) ; Data processing ; Data mining ; Chemistry ; Database ; Chromatography ; Computer vision Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality
Authors: Payam Kelich | Jaquesta Adams | Sanghwa Jeong | Nicole Navarro | Markita P. Landry | Lela Vuković
Source: Journal of Chemical Information and Modeling (article, 2024-05-13)
Field: Materials Science — Materials Chemistry

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors across Multiple Spectral Wavelengths Owing to the value of DNA-wrapped single-walled carbon nanotube (SWNT)-based sensors for chemically specific imaging in biology, we explore machine learning (ML) predictions DNA-SWNT serotonin sensor responsivity as a function of DNA sequence based on the whole SWNT fluorescence spectra. Our analysis reveals the crucial role of DNA sequence in the binding modes of DNA-SWNTs to serotonin, with a smaller influence of SWNT chirality. Regression ML models trained on existing data sets predict the change in the fluorescence emission in response to serotonin, Δ<i>F</i>/<i>F</i>, at over a hundred wavelengths for new DNA-SWNT conjugates, successfully identifying some high- and low-response DNA sequences. Despite successful predictions, we also show that the finite size of the training data set leads to limitations on prediction accuracy. Nevertheless, incorporating entire spectra into ML models enhances prediction robustness and facilitates the discovery of novel DNA-SWNT sensors. Our approaches show promise for identifying new chemical systems with specific sensing response characteristics, marking a valuable advancement in DNA-based system discovery. Carbon nanotube ; Wavelength ; DNA ; Serotonin ; Materials science ; Nanotechnology ; Optoelectronics ; Chemistry Carbon Nanotubes in Composites ; Advanced biosensing and bioanalysis techniques ; Electrochemical Analysis and Applications
Authors: Xu Zhang | Bei Li | Ji Jun Zou | Hanxing Liu | Ben Xu | Kai Liu
Source: Science China Materials (article, 2024-07-05)
Field: Materials Science — Materials Chemistry

Deep-learning enabled atomic insights into the phase transitions and nanodomain topology of lead-free (K,Na)NbO3 ferroelectrics Materials science ; Lead (geology) ; Phase (matter) ; Phase transition ; Topology (electrical circuits) ; Nanotechnology ; Condensed matter physics ; Chemical physics ; Quantum mechanics ; Physics ; Electrical engineering Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Electronic and Structural Properties of Oxides
Authors: Mengfan Tang | Yue Ding | Tanwei Hu | Xiaolong Zhu | Guang Zheng | Yu Tian
Source: ChemPhysChem (article, 2024-07-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning‐Based Prediction of Supercapacitor Capacitance for MgCo<sub>2</sub>O<sub>4</sub> Electrodes Electrode materials are essential in the electrochemical process of storing charge in supercapacitors and have a significant impact on the cost and capacitive performance of the final product. Hence, it is imperative to make precise predictions regarding the capacitance of electrode materials in order to further the development of supercapacitors. MgCo<sub>2</sub>O<sub>4</sub>, with a theoretical capacitance of up to 3122 F g<sup>-1</sup>, holds immense research value as an electrode material. The objective of this study is to predict the capacitance of MgCo<sub>2</sub>O<sub>4</sub> with high accuracy. This will be achieved by extracting numerous data from published papers and using some parameters as input features. The Recursive Feature Elimination (RFE) method was employed, using Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Regression Tree (RT) as selectors to identify the optimal feature subset. Then, combining them with these three regression models to construct nine machine learning (ML) models. After performance evaluation and outlier analysis, the XGB-RFE-XGB model achieved R-squared (R<sup>2</sup>), root mean squared error (RMSE), and mean absolute error (MAE) of 0.95, 111.83 F g<sup>-1</sup> and 68.25 F g<sup>-1</sup>, respectively, demonstrating its stability and reliability. Therefore, the XGB-RFE-XGB model can be used as a reliable predictive tool in subsequent experimental designs. Supercapacitor ; Capacitance ; Electrode ; Materials science ; Nanotechnology ; Electrode array ; Computer science ; Chemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Conducting polymers and applications
Authors: Xiaorui Liu | Haiping Yang | Peixuan Xue | Yuanjun Tang | Chao Ye | Wenwen Guo
Source: Renewable Energy (article, 2024-07-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning modeling of the capacitive performance of N-doped porous biochar electrodes with experimental verification Biochar ; Capacitive sensing ; Porosity ; Electrode ; Doping ; Materials science ; Environmental science ; Computer science ; Optoelectronics ; Composite material ; Electrical engineering ; Waste management ; Engineering ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Yonglong Du | Daxu Zhang | Yi Zhang | Weiyu Guo | Qi Zhao
Source: Ceramics International (article, 2024-02-07)
Field: Materials Science — Ceramics and Composites

Predicting tensile behaviour of plain weave CMCs using a nonlinear data-driven constitutive model for fibre tow composites Materials science ; Composite material ; Ultimate tensile strength ; Mesoscopic physics ; Constitutive equation ; Ceramic matrix composite ; Plain weave ; Nonlinear system ; Tensile testing ; Tension (geology) ; Ceramic ; Finite element method ; Structural engineering Advanced ceramic materials synthesis ; Mechanical Behavior of Composites ; Innovative concrete reinforcement materials
Authors: Matthew Cho | Zahra Mahmoodi | Prasad Shetty | Lauren R. Harrison | Maru Arias Montecillo | Ayyappasamy Sudalaiyadum Perumal | Gerardin Solana | Dan V. Nicolau
Source: ACS Applied Materials & Interfaces (article, 2024-05-24)
Field: Materials Science — Surfaces, Coatings and Films

Protein Adsorption on Solid Surfaces: Data Mining, Database, Molecular Surface-Derived Properties, and Semiempirical Relationships Protein adsorption on solid surfaces is a process relevant to biological, medical, industrial, and environmental applications. Despite this wide interest and advancement in measurement techniques, the complexity of protein adsorption has frustrated its accurate prediction. To address this challenge, here, data regarding protein adsorption reported in the last four decades was collected, checked for completeness and correctness, organized, and archived in an upgraded, freely accessible Biomolecular Adsorption Database, which is equivalent to a large-scale, ad hoc, crowd-sourced multifactorial experiment. The shape and physicochemical properties of the proteins present in the database were quantified on their molecular surfaces using an in-house program (ProMS) operating as an add-on to the PyMol software. Machine learning-based analysis indicated that protein adsorption on hydrophobic and hydrophilic surfaces is modulated by different sets of operational, structural, and molecular surface-based physicochemical parameters. Separately, the adsorption data regarding four "benchmark" proteins, i.e., lysozyme, albumin, IgG, and fibrinogen, was processed by piecewise linear regression with the protein monolayer acting as breakpoint, using the linearization of the Langmuir isotherm formalism, resulting in semiempirical relationships predicting protein adsorption. These relationships, derived separately for hydrophilic and hydrophobic surfaces, described well the protein concentration on the surface as a function of the protein concentration in solution, adsorbing surface contact angle, ionic strength, pH, and temperature of the carrying fluid, and the difference between pH and the isoelectric point of the protein. When applying the semiempirical relationships derived for benchmark proteins to two other "test" proteins with known PDB structure, i.e., β-lactoglobulin and α-lactalbumin, the errors of this extrapolation were found to be in a linear relationship with the dissimilarity between the benchmark and the test proteins. The work presented here can be used for the estimation of operational parameters modulating protein adsorption for various applications such as diagnostic devices, pharmaceuticals, biomaterials, or the food industry. Adsorption ; Protein adsorption ; Isoelectric point ; Materials science ; Protein Data Bank (RCSB PDB) ; Database ; Computer science ; Chemistry ; Physical chemistry ; Organic chemistry ; Biochemistry Polymer Surface Interaction Studies ; Protein purification and stability ; Microfluidic and Bio-sensing Technologies