jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiying Yu | Yudi Zhang | Yuyan Ni | Shikun Feng | Yanyan Lan | Hao Zhou | Jingjing Liu
Source: arXiv (Cornell University) (preprint, 2023-07-12)
Field: Materials Science — Materials Chemistry

Multimodal Molecular Pretraining via Modality Blending Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery. Current studies consider leveraging both 2D and 3D molecular structures for representation learning. However, relying on straightforward alignment strategies that treat each modality separately, these methods fail to exploit the intrinsic correlation between 2D and 3D representations that reflect the underlying structural characteristics of molecules, and only perform coarse-grained molecule-level alignment. To derive fine-grained alignment and promote structural molecule understanding, we introduce an atomic-relation level "blend-then-predict" self-supervised learning approach, MoleBLEND, which first blends atom relations represented by different modalities into one unified relation matrix for joint encoding, then recovers modality-specific information for 2D and 3D structures individually. By treating atom relationships as anchors, MoleBLEND organically aligns and integrates visually dissimilar 2D and 3D modalities of the same molecule at fine-grained atomic level, painting a more comprehensive depiction of each molecule. Extensive experiments show that MoleBLEND achieves state-of-the-art performance across major 2D/3D molecular benchmarks. We further provide theoretical insights from the perspective of mutual-information maximization, demonstrating that our method unifies contrastive, generative (cross-modality prediction) and mask-then-predict (single-modality prediction) objectives into one single cohesive framework. Modality (human–computer interaction) ; Computer science ; Artificial intelligence ; Representation (politics) ; Relation (database) ; Modalities ; Perspective (graphical) ; Encoding (memory) ; Machine learning ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Web Data Mining and Analysis
Authors: Çiğdem Akduman | Nida Oğlakçıoğlu
Source: Sakarya University Journal of Science (article, 2023-05-25)
Field: Materials Science — Biomaterials

Tailoring the Porosity and Breathability of Nanofiber Webs with Mesh Size of the Deposition Material Nano and micro-pores of the electrospun webs present good moisture vapor transmission rate, while it maintains resistance to pressured air and resistance to liquid for some type of clothing. Laminating a nanofiber web to any textile structure could improve the desired resistance to air permeability with providing excellent breathability. In the present study, hydrophobic thermoplastic polyurethane (TPU) and hydrophilic poly (vinyl alcohol) (PVA) nanofiber webs were produced onto three different chromium sieve wires and then laminated to an interlining fabric and compared in means of pore size, breathability, and air permeability. Mesh count of the wires affected the pore size and smallest pore size are belong to 90 mesh wire. The water vapor permeability of the samples varied between 80% and 90% as well as providing relatively low air permeability values. With increasing nanofiber amount, air permeability decreased dramatically. Materials science ; Composite material ; Air permeability specific surface ; Porosity ; Nanofiber ; Thermoplastic polyurethane ; Permeability (electromagnetism) ; Elastomer ; Membrane Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Surface Modification and Superhydrophobicity
Authors: Alejandro Gómez‐García | Daniel A. Acuña Jiménez | William J. Zamora | Haruna Luz Barazorda-Ccahuana | Miguel Á. Chávez‐Fumagalli | Marília Valli | Adriano D. Andricopulo | Vanderlan da Silva Bolzani | Dionisio A. Olmedo | Pablo N. Solís | Marvin J. Núñez | Johny R. Rodríguez-Pérez | Hoover Albeiro Valencia Sánchez | Héctor F. Cortés Hernández | José L. Medina‐Franco
Source: ChemRxiv (preprint, 2023-08-24)
Field: Chemistry — Spectroscopy

Navigating the Chemical Space and Chemical Multiverse of a Unified Latin American Natural Product Database: LANaPDB The number of databases of natural products (NPs) have increased substantially. Latin America is extraordinarily rich in biodiversity enabling the identification of novel NPs, which has encouraged both the development of databases and the implementation of those that are being created or are under development. In a collective effort from several Latin American countries, herein we introduce the first version of Latin American Natural Products Database (LANaPDB), a public compound collection that gathers the chemical information of NPs contained in diverse databases from this geographical region. The current version of LANaPD unifies the information from six countries and contains 12,959 chemical structures. The structural classification showed that the most abundant compounds are the terpenoids 63.2%, phenylpropanoids 18% and the alkaloids 11.8%. From the analysis of the distribution of properties of pharmaceutical interest, it was observed that many LaNaPDB compounds satisfy some drug-like rules of thumb for physicochemical properties. The concept of the chemical multiverse was employed to generate multiple chemical spaces from two different fingerprints and two dimensionality reduction techniques. Comparing LaNaPDB with FDA-approved drugs and the major open-access repository of NPs, COCONUT it was concluded that the chemical space covered by LaNaPDB completely overlaps with COCONUT and in some regions with FDA-approved drugs. LANaPD will be updated adding more compounds from each database plus the addition of databases from other Latin American countries. The database is freely available at https://github.com/alexgoga21/LaNaPDB. Chemical space ; Database ; Latin Americans ; Chemical database ; Computer science ; Identification (biology) ; Drug discovery ; Information retrieval ; Data science ; Chemistry ; Bioinformatics ; Political science ; Biology ; Organic chemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Chemical synthesis and alkaloids
Authors: Κωνσταντίνος Κώστας | Constantinos Valagiannopoulos
Source: Research Square (preprint, 2023-10-31)
Field: Materials Science — Materials Chemistry

Optimally Shaped Nanotubes for Field Concentration Field (mathematics) ; Materials science ; Nanotechnology ; Environmental science ; Mathematics ; Pure mathematics Carbon Nanotubes in Composites ; Graphene research and applications ; Nanopore and Nanochannel Transport Studies
Authors: Yu‐Chen Chen
Source: CPSS Transactions on Power Electronics and Applications (article, 2023-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Parameter Shift Prediction of Planar Transformer Based on Bi-LSTM Algorithm Algorithm ; Transformer ; Computer science ; Planar ; Artificial intelligence ; Electrical engineering ; Engineering ; Voltage ; Computer graphics (images) Magnetic Properties and Applications ; Power Transformer Diagnostics and Insulation ; Non-Destructive Testing Techniques
Authors: Chao Ma | Zihao Wu | Shuai Xie | Xin Yang | Tiantian Si | Junyu Wu | Zhijiang Ji | Jing Wang | Chunhong Guo
Source: Journal of Material Science and Technology Research (article, 2023-05-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization with a Genetic Algorithm for Multilayer Electromagnetic Wave Absorption Cement Mortar Filled with Expended Perlite Abstract: Due to the complexity of the design of multilayer electromagnetic (EM) wave absorbing materials, it is difficult to establish the relationship between material parameters (type and filling ratios) and EM properties using traditional trial and error methods. Based on the measured EM parameters within a few materials and Boltzmann mixing theory, a database of EM parameters was thereafter built up. In this study, the genetic algorithm (GA) was used to design the multilayer wave-absorbing cement mortar. In order to verify this method, a multilayer mortar was fabricated and measured. The simulated and measured results are well consistent, which convincingly verifies computer-aided design. In addition, the optimized result expresses that the first layer as a matching layer guides EM waves into the interior of the material, while the other layers as absorption layers attenuate EM waves. The multilayer material may not meet the impedance gradient principle but still exhibits better EM wave absorption performance. The reflection loss (RL) of all optimized three layer sample is below –6.89 dB in the full frequency band and the minimum RL is –26.21 dB. This composite absorbing material and the GA method provide more design ideas for the design of future cement-based wave-absorbing materials and save a lot of time and material cost. Materials science ; Mortar ; Perlite ; Cement ; Absorption (acoustics) ; Reflection (computer programming) ; Genetic algorithm ; Composite material ; Reflection loss ; Material Design ; Reflection coefficient ; Electromagnetic radiation ; Acoustics ; Impedance matching ; Layer (electronics) ; Optics ; Composite number ; Computer science ; Electrical impedance ; Engineering ; Physics Electromagnetic wave absorption materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Structural Behavior of Reinforced Concrete
Authors: August George | Daniel M. Zuckerman
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-11-02)
Field: Chemistry — Electrochemistry

From average transient transporter currents to microscopic mechanism – A Bayesian analysis Abstract Electrophysiology studies of secondary active transporters have revealed quantitative, mechanistic insights over many decades of research. However, the emergence of new experimental and analysis approaches calls for investigation of the capabilities and limitations of the newer methods. We examine the ability of solid-supported membrane electrophysiology (SSME) to characterize discrete-state kinetic models with &gt; 10 rate constants. We use a Bayesian framework applied to synthetic data for three tasks: to quantify and check (i) the precision of parameter estimates under different assumptions, (ii) the ability of computation to guide selection of experimental conditions, and (iii) the ability of SSME data to distinguish among mechanisms. When the general mechanism – event order – is known in advance, we show that a subset of kinetic parameters can be “practically identified” within ∼ 1 order of magnitude, based on SSME current traces that visually appear to exhibit simple exponential behavior. This remains true even when accounting for systematic measurement bias and realistic uncertainties in experimental inputs (concentrations) are incorporated into the analysis. When experimental conditions are optimized or different experiments are combined, the number of practically identifiable parameters can be increased substantially. Some parameters remain intrinsically difficult to estimate through SSME data alone, suggesting additional experiments are required to fully characterize parameters. We additionally demonstrate the ability to perform model selection and determine the order of events when that is not known in advance, comparing Bayesian and maximum-likelihood approaches. Finally, our studies elucidate good practices for the increasingly popular, but subtly challenging, Bayesian calculations for structural and systems biology. Bayesian probability ; Approximate Bayesian computation ; Experimental data ; Computer science ; Selection (genetic algorithm) ; Model selection ; Computation ; Mechanism (biology) ; Transient (computer programming) ; Biological system ; Algorithm ; Artificial intelligence ; Statistics ; Mathematics ; Biology ; Physics Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Fuel Cells and Related Materials
Authors: Nur Adlina Johari | Nur Fatin Aqilah Mahathir | Nor’aishah Abu Shah | Nur Nadia Dzulkifli | Is Fatimah | Nurain Adam | Vadym Кovalenko | Sheikh Ahmad Izaddin Sheikh Mohd Ghazali
Source: Trends in Sciences (article, 2023-09-03)
Field: Materials Science — Materials Chemistry

Zinc Layered Hydroxide 2-methyl-4-chlorophenoxyacetate: Synthesis via ZnO, Characterization and Effect on Seed Germination 2-methyl-4-chlorophenoxyacetic acid (MCPA) was successfully intercalated into zinc layered hydroxide (ZLH) by anion exchange technique followed by hydrothermal treatment, resulting in the formation of nanoherbicide, zinc-layered hydroxide-2-methyl-4-chlorophenoxyacetic acid (ZMCPA). Fourier-transform infrared spectroscopy (FTIR) and Powder X-ray diffraction (PXRD) was used to confirm the intercalation of herbicide into ZLH, while Ultraviolet-visible spectroscopy (UV-vis) was employed to assess the plant’s protein content and amino acid composition before and after 1 h treatment with ZnO, MCPA and ZMCPA via the foliar technique and direct soil application. The basal spacing of ZMCPA in this study is 21.6 Å at 4.0815°, revealing the triumphant incorporation of MCPA into the ZLH host. The zinc oxide (ZnO) fingerprint from 30° to 60° that certainly vanished in the ZMCPA diffractogram indicates that all ZnO has successfully converted to ZLH matrices. The FTIR spectrum of ZMCPA shows the similarity of most MCPA spectra, but with the COO– peaks at 1610 and 1700 cm–1 replacing the intense C=O band at 1742.21 cm–1, implying that the herbicide has assimilated into the host. The protein level of the plant treated with ZMCPA was discovered to be more significant, which signifies that ZLH has effectively acted as a herbicide carrier. This study has depicted the potential of ZMCPA as a more unassailable agrochemical agent in increasing crop productivity while minimizing environmental consequences. HIGHLIGHTS Successful Intercalation of MCPA into Zinc Layered Hydroxide (ZLH) Enhanced Plant Protein Levels with nanocomposite, ZMCPA Treatment ZMCPA as a potentially more effective and environmentally friendly agrochemical agent compared to the traditional agrochemical GRAPHICAL ABSTRACT Zinc hydroxide ; Zinc ; Fourier transform infrared spectroscopy ; Hydroxide ; Chemistry ; Nuclear chemistry ; Intercalation (chemistry) ; MCPA ; Materials science ; Inorganic chemistry ; Chemical engineering ; Organic chemistry ; Agronomy Layered Double Hydroxides Synthesis and Applications
Authors: Yafei Wang | Aeli P. Olson | Cody Falconer | Brian Kelleher | Ivan Mitchell | Hongliang Zhang | Kumar Sridha | Jonathan W. Engle | Adrien Couet
Source: Research Square (preprint, 2023-10-09)
Field: Materials Science — Materials Chemistry

Radionuclide Tracing Based in situ Corrosion and Mass Transport Monitoring of 316L Stainless Steel in a Molten Salt Closed Loop Radionuclide ; Tracing ; In situ ; Corrosion ; Mass transport ; Metallurgy ; Loop (graph theory) ; Molten salt ; Salt (chemistry) ; Materials science ; Closed loop ; Nuclear engineering ; Environmental science ; Chemistry ; Engineering ; Nuclear physics ; Computer science ; Physics ; Control engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: Koen Veys | Manon H. E. Bousquet | Denis Jacquemin | Daniel Escudero
Source: ChemRxiv (preprint, 2023-06-28)
Field: Chemistry — Physical and Theoretical Chemistry

Modelling the Fluorescence Quantum Yields of Aromatic Compounds: Benchmarking the Machinery to Compute Intersystem Crossing Rates The from-first-principles calculation of fluorescence quantum yields (FQYs) and lifetimes of organic dyes remains very challenging. In this manuscript we extensively test the static machinery to calculate FQYs. Specifically, we perform an extensive analysis on the parameters influencing the intersystem crossing (ISC), internal conversion (IC), and fluorescence rates calculations. The impact of i) the electronic structure (chosen exchange-correlation functional and spin-orbit Hamiltonian), ii) the vibronic parameters (coordinate system, broadening function, and dipole expansion), and iii) the excited-state kinetic models, are systematically assessed for a series of seven rigid aromatic molecules. Our studies provide more insights into the choice of parameters and the expected accuracy for the computational protocols aiming to deliver FQYs values. Some challenges are highlighted, such as, on the one hand, the difficulty to benchmark against the experimental non-radiative rates, for which the separation between the IC and ISC contributions is often not provided in the literature and, on the other hand, the need to go beyond the harmonic approximation for the calculation of the IC rates. Intersystem crossing ; Hamiltonian (control theory) ; Excited state ; Internal conversion ; Radiative transfer ; Quantum ; Harmonic oscillator ; Dipole ; Chemistry ; Statistical physics ; Physics ; Computational chemistry ; Quantum mechanics ; Atomic physics ; Mathematics ; Mathematical optimization Photochemistry and Electron Transfer Studies ; Free Radicals and Antioxidants ; Electrochemical Analysis and Applications
Authors: Saurabhsingh Rajput | Tim Widmayer | Ziyuan Shang | Maria Kechagia | Federica Sarro | Tushar Sharma
Source: arXiv (Cornell University) (preprint, 2023-08-23)
Field: Materials Science — Surfaces, Coatings and Films

Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement With the increasing usage, scale, and complexity of Deep Learning (DL) models, their rapidly growing energy consumption has become a critical concern. Promoting green development and energy awareness at different granularities is the need of the hour to limit carbon emissions of DL systems. However, the lack of standard and repeatable tools to accurately measure and optimize energy consumption at a fine granularity (e.g., at method level) hinders progress in this area. This paper introduces FECoM (Fine-grained Energy Consumption Meter), a framework for fine-grained DL energy consumption measurement. FECoM enables researchers and developers to profile DL APIs from energy perspective. FECoM addresses the challenges of measuring energy consumption at fine-grained level by using static instrumentation and considering various factors, including computational load and temperature stability. We assess FECoM's capability to measure fine-grained energy consumption for one of the most popular open-source DL frameworks, namely TensorFlow. Using FECoM, we also investigate the impact of parameter size and execution time on energy consumption, enriching our understanding of TensorFlow APIs' energy profiles. Furthermore, we elaborate on the considerations, issues, and challenges that one needs to consider while designing and implementing a fine-grained energy consumption measurement tool. This work will facilitate further advances in DL energy measurement and the development of energy-aware practices for DL systems. Computer science ; Deep learning ; Artificial intelligence ; Energy (signal processing) ; Statistics ; Mathematics Electron and X-Ray Spectroscopy Techniques
Authors: Ján Titiš | Cyril Rajnák | Roman Boča
Source: Preprints.org (preprint, 2023-09-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetostructural D‐Correlations and Their Impact to Single‐Molecule Magnetism Functional dependence of the axial zero-field splitting parameter D with respect to a properly chosen geometrical parameter (Dstr) in metal complexes is termed the Magnetostructural D-Correlation. In mononuclear hexacoordinate Ni(II) complexes with the ground electronic term 3B1g (3A2g in the regular octahedron) it proceeds along two intercepting straight lines allowing to predict the sign and magnitude of the D-parameter by knowing the X-ray structure alone; Dstr is constructed from the metal-ligand bond lengths. In hexacoordinate Co(II) complexes it is applicable only in the segment of the compressed bipyramid where the ground electronic term 4B1g is orbitally non-degenerate so that the spin Hamiltonian formalism holds true. The D vs Dstr correlation is strongly non-linear and it is represented by a set of decreasing exponentials. In tetracoordinate Co(II) complexes, on the contrary, the angular distortion from the regular tetrahedron is crucial so that the appropriate structural parameter Dstr is constructed of bond angles. All of these empirical correlations originate in the electronic structure of metal complexes that can be modelled by the generalized crystal-field theory. As the barrier to spin reversal in the single-molecule magnets is proportional to the D-value, for a rational tuning and/or prediction of the single-molecule magnetic behavior knowledge/prediction of the D-parameter is beneficial. In this review, we present the statistical processing of an extensive set of structural and magnetic data of Co(II) and Ni(II) complexes, which were published over the past 15 years. Magnetostructural D-correlations defined for this data set are reviewed in detail. Hexacoordinate ; Degenerate energy levels ; Tetrahedron ; Magnetism ; Physics ; Chemistry ; Crystallography ; Condensed matter physics ; Quantum mechanics Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Metal complexes synthesis and properties
Authors: M. HAJERA | M SANTHI | B. Divya
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2023-10-01)
Field: Chemistry — Analytical Chemistry

DETECTION OF CUMIN SEEDS ADULTERATION BASED ON THERMAL IMAGING USING DEEP LEARNING TECHNIQUE Food adulteration refers to the addition of artificial or poor-quality substances to a food product. Since the start of mankind, food adulteration has been a problem that artificial intelligence has successfully detected. In addition to lowering food quality, it also has several adverse health consequences. Two spices that are often used in the food industry are cumin and fennel, although they can be adulterated. Using deep learning, this research established a convolutional neural network (CNN) architecture that can identify between a food product and additional adulterants. This technique accurately detects 95.5% adulteration in cumin and fennel seeds. Key Words: Food adulteration, thermal imaging, spices, image classification, CNN, inception V3 algorithm. Food products ; Convolutional neural network ; Artificial intelligence ; Product (mathematics) ; Food quality ; Deep learning ; Quality (philosophy) ; Food science ; Computer science ; Mathematics ; Pattern recognition (psychology) ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies