jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alauddin Ahmed
Source: Physical Review Materials (article, 2024-11-21)
Field: Chemistry — Inorganic Chemistry

Machine-learning-enhanced symbolic regression for methane storage prediction in covalent organic frameworks Covalent organic frameworks (COFs), recognized for their potential in vehicular methane storage, typically require comprehensive and resource-intensive evaluations for capacity determination. Herein, we introduce a novel approach that synergizes symbolic regression with machine learning, augmented by descriptive statistics, systematic feature selection, and high-throughput computational techniques, to develop predictive equations for COF methane storage capacities. By strategically selecting a concise set of three to seven features based on their interpretability and measurability, we have developed a spectrum of symbolic regression-based equations to meet diverse precision requirements in forecasting COF methane capacities. The fidelity of these equations varies: an equation with three variables achieves a root mean square error (RMSE) of $10\phantom{\rule{4pt}{0ex}}\mathrm{c}{\mathrm{m}}^{3}$ (standard temperature and pressure, STP) per $\phantom{\rule{0.16em}{0ex}}\mathrm{c}{\mathrm{m}}^{3}$ and a mean absolute percentage error (MAPE) of 5%, while expanding to seven variables fine-tunes the RMSE to $8.1\phantom{\rule{4pt}{0ex}}\mathrm{c}{\mathrm{m}}^{3}$ (STP) per $\mathrm{c}{\mathrm{m}}^{3}$ and the MAPE to 4.2%. Our structured approach ensures accurate predictions that correspond to the resolution of available data. Upon applying our most precise equation to an extensive dataset of $449\phantom{\rule{0.16em}{0ex}}468$ COFs, we identified numerous COFs with exceptional methane storage capacities. The adaptability of our equations to integrate new data broadens their applicability beyond COFs, making them versatile tools for optimizing methane storage across a range of nanoporous materials. Materials science ; Methane ; Covalent bond ; Machine learning ; Artificial intelligence ; Computer science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Emma Van Puyenbroeck | Niels Wouters | Tom Leblicq | Wouter Saeys
Source: Computers and Electronics in Agriculture (article, 2024-08-20)
Field: Chemistry — Analytical Chemistry

Detection of kernels in maize forage using hyperspectral imaging Hyperspectral imaging ; Forage ; Remote sensing ; Artificial intelligence ; Agricultural engineering ; Environmental science ; Agronomy ; Computer science ; Computer vision ; Engineering ; Geography ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Meat and Animal Product Quality
Authors: Limo Afsar | Hassan Hassanzadazar | Saeedeh Shojaee‐Aliabadi | Majid Aminzare
Source: LWT (article, 2024-12-09)
Field: Materials Science — Biomaterials

Galbanum nanocomposite coating containing Satureja khuzestanica essential oil for prolonging shrimp (Metapenaeus affinis) shelf life at various temperatures The effects of nanocomposite coatings based on galbanum gum (GG) and lignocellulose nanofibers (LCNF) containing Satureja khuzestanica essential oils (SKEO) (1 and 2%) on increasing the shelf life of shrimp and modeling the shelf life of shrimp during 12 days’ storage at different temperatures of 0, 8, and 15°C were investigated. During the storage period, t otal aerobic mesophilic bacteria (TMB), total psychrophilic bacteria (PBC), pseudomonas spp., lactic acid bacteria, H 2 S-producing bacteria, and chemical tests including volatile nitrogen bases (TVB-N) and thiobarbituric acid substances (TBARs) and pH increased in all treatments. The pattern of the parameters’ increase in different groups was as the following: G+LN+SE2 < G+SE2 < G+LN+SE1 < G+SE1 < G+LN < G< C. The values of a* and b* parameters increase less with decreasing temperature during storage time and increasing the concentration of essential oil in the nanocomposites (P<0.05). The sensory attributes of the coated shrimp samples were significantly more acceptable (P<0.05). Integration of GG/LCNF/SKEO coating promisingly can improve the quality of shrimp samples during refrigerated storage. Artificial neural network sensitivity analysis (ANN model) as a computational tool to predict food quality showed that the storage time has the greatest effect on the quality of shrimp samples. • • New edible nanocomposite coating of GG containing S. khuzestanica EO was developed. • • There is no similar study as a survey on the shrimp shelf life at three temperature • • GG /S. khuzestanica nanocomposite showed good antioxidant effects • • The coated shrimp samples had better microbiological and sensorial quality Shelf life ; Shrimp ; Fishery ; Coating ; Nanocomposite ; Biology ; Chemistry ; Food science ; Materials science ; Nanotechnology Nanocomposite Films for Food Packaging ; Biodiesel Production and Applications ; Medicinal Plant Research
Authors: Dongsun Lee | Chaeyoung Lee
Source: Journal of Computational Science (article, 2024-02-17)
Field: Materials Science — Materials Chemistry

Assessment of morphological similarities for the conservative Allen–Cahn and Cahn–Hilliard equations Similarity (geometry) ; Convolutional neural network ; Dissipation ; Computer science ; Field (mathematics) ; Phase (matter) ; Dynamics (music) ; Artificial neural network ; Artificial intelligence ; Mathematics ; Statistical physics ; Algorithm ; Applied mathematics ; Physics ; Image (mathematics) ; Pure mathematics ; Thermodynamics Solidification and crystal growth phenomena ; nanoparticles nucleation surface interactions ; Aluminum Alloy Microstructure Properties
Authors: Farzaneh M. Parizi | Yannick J. M. Aarts | Nils Smit | Dona Roran AR | Daniëlle Diepenbroek | Wieke A. Krösschell | Levin Thijs | Joost Tepperik | Sanna Eerden | Dario F. Marzella | Gayatri Ramakrishnan | Li C. Xue
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-06-01)
Field: Chemistry — Spectroscopy

SwiftTCR: Efficient computational docking protocol of TCRpMHC-I complexes using restricted rotation matrices Abstract The T cell’s ability to discern self and non-self depends on its T cell receptor (TCR), which recognizes peptides presented by MHC molecules. Understanding this TCR-peptide-MHC (TCRpMHC) interaction is important for cancer immunotherapy design, tissue transplantation, pathogen identification, and autoimmune disease treatments. Understanding the intricacies of TCR recognition, encapsulated in TCRpMHC structures, remains challenging due to the immense diversity of TCRs (&gt;10 8 /individual), rendering experimental determination and general-purpose computational docking impractical. Addressing this gap, we have developed a rapid integrative modeling protocol leveraging unique docking patterns in TCRpMHC complexes. Built upon PIPER, our pipeline significantly cuts down FFT rotation sets, exploiting the consistent polarized docking angle of TCRs at pMHC. Additionally, our ultra-fast structure superimposition tool, GradPose, accelerates clustering. It models a case in 3-4 minutes on 12 CPUs, showcasing a speedup of up to 25-40 times compared to the ClusPro webserver. On a benchmark set of 38 TCRpMHC class I (TCRpMHC-I) complexes, our protocol outperforms the state-of-the-art docking tools in model quality. This protocol can potentially provide structural information to TCR repertoires targeting specific peptides. Its computational efficiency can also enrich existing pMHC-specific single-cell sequencing TCR data, facilitating the development of structure-based deep learning (DL) algorithms. These insights are essential for understanding T cell recognition and specificity, advancing the development of therapeutic interventions. Docking (animal) ; Protocol (science) ; Computer science ; Rotation (mathematics) ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Carbon Nanotubes in Composites ; Photoreceptor and optogenetics research
Authors: Fanny Duquet | Matthieu Rivallin | Florence Bosc Rouessac | Raphaël Costes | Jim Cartier | Christophe Charmette | S. Roualdès
Source: International Journal of Hydrogen Energy (article, 2024-09-26)
Field: Materials Science — Materials Chemistry

Photo-electrochemical study of TiO2/Co3O4 thin films in polluted electrolyte: A promising route for coupling hydrogen production with water remediation The use of a photo-electrochemical cell (PEC) to produce hydrogen from wastewater is a promising innovation. In this context, this study investigates the impact of a pollutant on the photo-electrochemical properties of sol-gel synthesized TiO 2 and TiO 2 –Co 3 O 4 thin films for hydrogen production in a polluted electrolyte. Combining the photocatalytic properties of TiO 2 with the electronic properties of Co 3 O 4 offers an effective solution for achieving effective photo-electrochemical properties in polluted environments. Nevertheless, despite the lowest photocatalytic activity, the hybrid thin film TiO 2 –Co 3 O 4 with the highest Ti/Co ratio (1:0.5) shows the most promising performance with simultaneous 11.4 μmol cm −2 h −1 H 2 production and 12% acid orange 7 degradation after 3 h irradiation under xenon light without the use of any sacrificial agent. This indicates that the electronic conductivity provided by the presence of Co 3 O 4 is a critical property for achieving optimal performance in PEC coupling for hydrogen production and wastewater treatment. • Sol-gel TiO 2 –Co 3 O 4 films enabled coupling of water splitting and remediation. • 11 μmol cm −2 h −1 H 2 was produced after 3h Xe irradiation without sacrificial agent. • Simultaneous 12% acid orange 7 degradation was measured. Electrochemistry ; Electrolyte ; Hydrogen production ; Environmental remediation ; Materials science ; Chemical engineering ; Coupling (piping) ; Hydrogen ; Thin film ; Inorganic chemistry ; Electrode ; Chemistry ; Nanotechnology ; Contamination ; Organic chemistry ; Metallurgy ; Physical chemistry Copper-based nanomaterials and applications ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Niharbala Devi | Rekha Pachaiappan | Claudia Lorena | Verónica Patricia | Alessandroni Maria | R. Uma Devi | S. Thanikaikarasan
Source: International Journal of Advanced Science and Engineering (article, 2024-12-14)
Field: Materials Science — Materials Chemistry

Silver Doped Manganese Dioxide as an Efficient Photocatalyst for Solar Light Induced Degradation of Malachite Green Dye and Antibacterial Activity Every day, the issue of water scarcity worsens as a result of climate change and inadequate management of the water resources at hand.An alternative approach that is continuously investigated is the remediation of wastewater, which contains a significant quantity of organic and inorganic pollutants, via the utilization of nanomaterials possessing photocatalytic and antibacterial properties.Previous reports show that the manganese dioxide nanoparticles are an inorganic substance that exhibits antimicrobial activity and dye degradation acting as an efficient photocatalyst.Silver modification has the potential to enhance the antimicrobial activity of this oxide.In pursuit of this objective, silver doped manganese dioxide was synthesized and prepared as a nanoparticle that functions as a solar photocatalyst in degradation of industrial dyes and killing of bacteria.Prepared photocatalyst was characterized with X-ray diffraction, UV-visible spectroscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, TEM and Scanning Electron Microscopy to understand its physico-chemical characterizes and evaluated for its photocatalytic behavior.Furthermore, the prepared nano-photocatalyst revealed that it is impurity-free and possesses a particle size of nanometer range.It exhibits enhanced photocatalytic activity in the degradation of malachite green dye and killing of gram negative bacteria -Escherichia coli, when exposed to ambient sunlight.Inhibition of the growth of microbe by silver doped manganese dioxide was due to the reactive oxygen species by the metal ions. Malachite green ; Degradation (telecommunications) ; Photocatalysis ; Manganese ; Doping ; Titanium dioxide ; Malachite ; Chemistry ; Visible spectrum ; Materials science ; Nuclear chemistry ; Photochemistry ; Optoelectronics ; Copper ; Metallurgy ; Organic chemistry ; Catalysis Advanced Nanomaterials in Catalysis ; TiO2 Photocatalysis and Solar Cells ; Pigment Synthesis and Properties
Authors: Vaidish Sumaria | Takat B. Rawal | Young Feng Li | David E. Sommer | Jake Vikoren | Robert J. Bondi | Matthias Rupp | Amrit Prasad | Deeptanshu Prasad
Source: arXiv (Cornell University) (preprint, 2024-02-14)
Field: Materials Science — Materials Chemistry

Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO$_2$ by CuPt/TiO$_2$ The photoconversion of CO$_2$ to hydrocarbons is a sustainable route to its transformation into value-added compounds and, thereby, crucial to mitigating the energy and climate crises. CuPt nanoparticles on TiO$_2$ surfaces have been reported to show promising photoconversion efficiency. For further progress, a mechanistic understanding of the catalytic properties of these CuPt/TiO$_2$ systems is vital. Here, we employ $\textit{ab-initio}$ calculations, machine learning, and photocatalysis experiments to explore their configurational space and examine their reactivity and find that the interface plays a key role in stabilizing *CO$_2$, *CO, and other CH-containing intermediates, facilitating higher activity and selectivity for methane. A bias-corrected machine-learning interatomic potential trained on density functional theory data enables efficient exploration of the potential energy surfaces of numerous CO$_2$@CuPt/TiO$_2$ configurations via basin-hopping Monte Carlo simulations, greatly accelerating the study of these photocatalyst systems. Our simulations show that CO$_2$ preferentially adsorbs at the interface, with C atom bonded to a Pt site and one O atom occupying an O-vacancy site. The interface also promotes the formation of *CH and *CH$_2$ intermediates. For confirmation, we synthesize CuPt/TiO$_2$ samples with a variety of compositions and analyze their morphologies and compositions using scanning electron microscopy and energy-dispersive X-ray spectroscopy, and measure their photocatalytic activity. Our computational and experimental findings qualitatively agree and highlight the importance of interface design for selective conversion of CO$_2$ to hydrocarbons. Density functional theory ; Photocatalysis ; Reduction (mathematics) ; Materials science ; Nanotechnology ; Computer science ; Chemical engineering ; Chemistry ; Engineering ; Mathematics ; Computational chemistry ; Catalysis Machine Learning in Materials Science
Authors: Fernando Lomello | Lucie Bard | Mario Maglione | Frédéric Schuster
Source: Computational and Structural Biotechnology Journal (article, 2024-09-26)
Field: Materials Science — Materials Chemistry

PEPR DIADEM: Priority equipment and research program on the development of innovative materials using artificial intelligence The quest to develop efficient, sustainable materials from non-critical, non-toxic resources is one of today's most formidable challenges in the current context of energy, transport, digital or healthcare transitions. In response, France launched the pioneering Priority Equipment and Research Program (PEPR) DIADEM in 2022. This innovative initiative, focused on DIscovery Acceleration for the Deployment of Emerging Materials (DIADEM), leverages Artificial Intelligence (AI) to accelerate the innovation chain from conception to realization, revolutionizing Materials Science sustainably. With a strategic emphasis on scientific synergy, PEPR DIADEM aims to expedite the discovery and development of novel materials essential for contemporary and future societal challenges. To achieve this, the program seeks to catalyze breakthroughs in areas ranging from energy efficiency to transportation, digitalization, and healthcare, covering a broad spectrum of materials from metallic alloys to functional nanostructures. Aligned with the Green Deal framework's ambitious targets, PEPR DIADEM addresses the urgent need for accelerated sustainable materials research. By utilizing cutting-edge technologies like rapid synthesis and characterization tools, automation, digital simulations, data management, AI, additive manufacturing, and thin film engineering, the program is set to significantly reshape the materials science landscape. As PEPR DIADEM embarks on its journey of innovation, it not only advances scientific knowledge but also holds the promise of addressing current global challenges and paving the way for a more sustainable and prosperous future. Context (archaeology) ; Software deployment ; Sustainability ; Engineering ; Engineering management ; Business Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Odette Agnes Regina
Source: Idosr Journal of Computer and Applied Sciences. (article, 2024-06-30)
Field: Chemistry — Physical and Theoretical Chemistry

Quantum mechanical methods in computational chemistry Quantum mechanical methods constitute the cornerstone of computational chemistry, providing unprecedented insights into molecular behaviour and properties at the atomic scale. These methods elucidate fundamental electronic structures, energies, and properties that are critical for understanding diverse chemical systems by solving the Schrödinger equation. Among these methods, Density Functional Theory (DFT) stands out for its versatility in investigating the electronic properties of atoms, molecules, and solids, rooted in the seminal Hohenberg-Kohn theorems and Kohn-Sham equations. This review explores the expansive landscape of quantum mechanical methods in computational chemistry, highlighting their pivotal role in advancing scientific understanding and technological innovation. Many areas, including material science, catalysis, and drug development, utilise these techniques to enhance molecular structures, forecast reactions, simulate spectroscopic properties, and clarify solvation effects. Quantum chemistry can now predict even more with the help of advanced techniques like post-Hartree-Fock methods and time-dependent DFT. These techniques give us more information about how molecules move and how electrons are excited. Molecular dynamics (MD) simulations add to quantum mechanical approaches by showing how molecules move and interact with each other over time. They achieve this by linking what scientists believe should occur theoretically with actual occurrences. Adding computer programs such as Structure-Based Drug Design (SBDD) and materials modelling shows how quantum chemistry can change things, speeding up the discovery process and improving the accuracy of molecular behaviour. Spectroscopy simulations and solvation studies help us predict how to interpret experimental data and figure out how the environment affects molecules' behaviour, and its applications thus making computational chemistry even more useful. The continual evolution of quantum chemistry software and high-performance computing frameworks democratises access to advanced computational tools, fostering collaboration and innovation in tackling complex scientific challenges. As quantum capabilities advance, the future promises even greater applications across chemistry and interdisciplinary fields, driving continual progress in materials design, drug development, and environmental sustainability. Keywords: quantum, mechanical methods, computational chemistry Quantum chemistry ; Chemistry ; Quantum ; Computational chemistry ; Physics ; Quantum mechanics ; Molecule ; Organic chemistry Various Chemistry Research Topics ; Molecular spectroscopy and chirality
Authors: Phuti Ngoepe | A. V. Chadwick | Happy Sithole | Khotso Mokhele | C. Richard A. Catlow
Source: Interface Focus (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

Materials modelling in the University of Limpopo This article provides insights into building research capacity in computational modelling of materials at the University of Limpopo (UL), formerly University of the North, in South Africa, through a collaboration with a consortium of universities in the United Kingdom (UK) through the support of the National Research Foundation (NRF), formerly the Foundation for Research and Development, and the Royal Society (RS). A background that led to the choice of building research capacity at historically disadvantaged universities in South Africa, including the UL, is given. The modus operandi of the collaboration between the UL and several UK universities on computational modelling of materials is outlined, together with the scientific highlights that were achieved in themes of minerals, energy storage and alloy development. The capacity built in terms of human capital and institutions set up is shared, which is followed by a discussion of the continuing research activities after the formal NRF–RS collaboration ceased with more alignment to industrial applications with national and international support. We conclude by highlighting the success of the project in capacity-building and consolidating the Materials Modelling Centre with developments of high-performance computing in South Africa and the African continent. We comment on the lessons learned regarding successful capacity-building programmes. Computer science ; Data science Material Selection and Properties ; Innovations in Concrete and Construction Materials
Authors: Ali Saeed | Muhammad Akhyar Farrukh | Hafiz Mahfooz Ul Haque | Duaa Javaid
Source: ES Energy & Environments (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Advanced Machine Learning Algorithms for Accurate Prediction of Band Gaps in Rare Earth Metal Oxides Nanoparticles This study pioneers the use of seven machine learning models to precisely predict band gaps in cerium dioxide and neodymium oxide nanoparticles. Achieving correlation coefficient of 1.00, a mean absolute error (MAE) below 0.01 eV., the research enhances understanding and optimization of these nanomaterials' electronic properties for diverse technological applications. Rare earth ; Nanoparticle ; Machine learning ; Computer science ; Algorithm ; Artificial intelligence ; Materials science ; Nanotechnology ; Metallurgy Machine Learning in Materials Science