jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sergio Pablo‐García | Santiago Morandi | Rodrigo A. Vargas–Hernández | Kjell Jorner | Žarko Ivković | Núria López | Alán Aspuru‐Guzik
Source: Nature Computational Science (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks Modeling in heterogeneous catalysis requires the extensive evaluation of the energy of molecules adsorbed on surfaces. This is done via density functional theory but for large organic molecules it requires enormous computational time, compromising the viability of the approach. Here we present GAME-Net, a graph neural network to quickly evaluate the adsorption energy. GAME-Net is trained on a well-balanced chemically diverse dataset with C<sub>1-4</sub> molecules with functional groups including N, O, S and C<sub>6-10</sub> aromatic rings. The model yields a mean absolute error of 0.18 eV on the test set and is 6 orders of magnitude faster than density functional theory. Applied to biomass and plastics (up to 30 heteroatoms), adsorption energies are predicted with a mean absolute error of 0.016 eV per atom. The framework represents a tool for the fast screening of catalytic materials, particularly for systems that cannot be simulated by traditional methods. Adsorption ; Artificial neural network ; Organic molecules ; Molecule ; Graph ; Computer science ; Materials science ; Chemistry ; Biological system ; Chemical engineering ; Artificial intelligence ; Organic chemistry ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gas Sensing Nanomaterials and Sensors
Source: International Journal of Corrosion and Scale Inhibition (review, 2023-12-24)
Field: Materials Science — Materials Chemistry

Advances in corrosion protection coatings: A comprehensive review Corrosion is a pervasive and costly issue with significant economic and environmental implications.Corrosion protection coatings play a vital role in safeguarding various industries against the detrimental effects of corrosion.This comprehensive review provides an overview of recent advances in corrosion protection coatings, focusing on organic, inorganic, and metallic coatings.The fundamentals of corrosion protection coatings are discussed, including the mechanisms by which these coatings provide protection against corrosion.The review highlights recent advancements in organic coatings, such as the development of novel formulations, self-healing coatings, and the utilization of nanotechnology.Furthermore, the progress in inorganic and ceramic coatings, including surface modification techniques and the integration of organic-inorganic hybrid coatings, is explored.Additionally, the paper presents emerging trends in metallic coatings, covering alloy design, environmentally friendly options, and surface engineering techniques.Evaluation methods for coating performance and testing, including accelerated corrosion testing, are summarized.The review showcases the wideranging applications of corrosion protection coatings in various industries, accompanied by case studies.The challenges and opportunities in emerging sectors, such as renewable energy and aerospace, are also discussed.Lastly, the paper outlines future directions and challenges, emphasizing the importance of ongoing research and the integration of advanced materials for multifunctional corrosion protection.This review paper serves as a valuable resource for researchers, engineers, and practitioners involved in corrosion protection, providing a comprehensive understanding of recent advances and guiding future research endeavors. Corrosion ; Materials science ; Metallurgy ; Forensic engineering ; Engineering Corrosion Behavior and Inhibition
Authors: Ning Ma | Huaixian Yin | Kai Wang
Source: Energies (article, 2023-07-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory As a novel type of energy storage element, supercapacitors have been extensively used in power systems, transportation and industry due to their high power density, long cycle life and good low-temperature performance. The health status of supercapacitors is of vital importance to the safe operation of the entire energy storage system. In order to improve the prediction accuracy of the remaining useful life (RUL) of supercapacitors, this paper proposes a method based on the Harris hawks optimization (HHO) algorithm and long short-term memory (LSTM) recurrent neural networks (RNNs). The HHO algorithm has the advantages of a wide global search range and a high convergence speed. Therefore, the HHO algorithm is used to optimize the initial learning rate of LSTM RNNs and the number of hidden-layer units, so as to improve the stability and reliability of the system. The root mean square error (RMSE) between the predicted result and the observed result reduced to 0.0207, 0.026 and 0.0341. The prediction results show that the HHO-LSTM has higher accuracy and robustness than traditional LSTM and GRU (gate recurrent unit) models. Robustness (evolution) ; Supercapacitor ; Long short term memory ; Mean squared error ; Computer science ; Artificial neural network ; Convergence (economics) ; Recurrent neural network ; Term (time) ; Artificial intelligence ; Algorithm ; Mathematics ; Statistics ; Capacitance Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Advanced Battery Technologies Research
Authors: R. Gray Huffman | Andrew Leduc | Christoph Wichmann | Marco Di Gioia | Francesco Borriello | Harrison Specht | Jason Derks | Saad Khan | Luke Khoury | Edward Emmott | Aleksandra A. Petelski | David H. Perlman | Jürgen Cox | Ivan Zanoni | Nikolai Slavov
Source: Nature Methods (article, 2023-04-03)
Field: Chemistry — Spectroscopy

Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics Major aims of single-cell proteomics include increasing the consistency, sensitivity and depth of protein quantification, especially for proteins and modifications of biological interest. Here, to simultaneously advance all these aims, we developed prioritized Single-Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while maximizing instrument time spent analyzing identifiable peptides, thus increasing proteome depth. These strategies increased the sensitivity, data completeness and proteome coverage over twofold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport, similarly across both treatment conditions. This covariation is coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at http://scp.slavovlab.net/pSCoPE . Proteomics ; Mass spectrometry ; Sensitivity (control systems) ; Computer science ; Computational biology ; Completeness (order theory) ; Chemistry ; Chromatography ; Biology ; Mathematics ; Biochemistry ; Engineering Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Mass Spectrometry Techniques and Applications
Authors: Huanchen Zhai | Henrik R. Larsson | Seunghoon Lee | Zhi‐Hao Cui | Tianyu Zhu | Chong Sun | Linqing Peng | Ruojing Peng | Ke Liao | Johannes Tölle | Junjie Yang | Shuoxue Li | Garnet Kin‐Lic Chan
Source: The Journal of Chemical Physics (article, 2023-12-18)
Field: Chemistry — Spectroscopy

<scp>Block2</scp>: A comprehensive open source framework to develop and apply state-of-the-art DMRG algorithms in electronic structure and beyond block2 is an open source framework to implement and perform density matrix renormalization group and matrix product state algorithms. Out-of-the-box it supports the eigenstate, time-dependent, response, and finite-temperature algorithms. In addition, it carries special optimizations for ab initio electronic structure Hamiltonians and implements many quantum chemistry extensions to the density matrix renormalization group, such as dynamical correlation theories. The code is designed with an emphasis on flexibility, extensibility, and efficiency and to support integration with external numerical packages. Here, we explain the design principles and currently supported features and present numerical examples in a range of applications. Density matrix renormalization group ; Eigenvalues and eigenvectors ; Computer science ; Matrix product state ; Flexibility (engineering) ; Ab initio ; Matrix multiplication ; Matrix (chemical analysis) ; Electronic structure ; Algorithm ; Computational science ; Theoretical computer science ; Statistical physics ; Quantum ; Physics ; Mathematics ; Quantum mechanics ; Materials science Advanced NMR Techniques and Applications ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Lars Grunenberg | Gökçen Savaşçı | Sebastian T. Emmerling | Fabian Heck | Sebastian Bette | Afonso Cima Bergesch | Christian Ochsenfeld | Bettina V. Lotsch
Source: Journal of the American Chemical Society (article, 2023-05-26)
Field: Materials Science — Materials Chemistry

Postsynthetic Transformation of Imine- into Nitrone-Linked Covalent Organic Frameworks for Atmospheric Water Harvesting at Decreased Humidity Herein, we report a facile postsynthetic linkage conversion method giving synthetic access to nitrone-linked covalent organic frameworks (COFs) from imine- and amine-linked COFs. The new two-dimensional (2D) nitrone-linked covalent organic frameworks, NO-PI-3-COF and NO-TTI-COF, are obtained with high crystallinity and large surface areas. Nitrone-modified pore channels induce condensation of water vapor at 20% lower humidity compared to their amine- or imine-linked precursor COFs. Thus, the topochemical transformation to nitrone linkages constitutes an attractive approach to postsynthetically fine-tune water adsorption properties in framework materials. Imine ; Chemistry ; Nitrone ; Covalent bond ; Covalent organic framework ; Crystallinity ; Amine gas treating ; Adsorption ; Methylamine ; Chemical engineering ; Organic chemistry ; Polymer chemistry ; Crystallography ; Catalysis ; Cycloaddition Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Soumy Prateek | Rajnish Garg | Kuldeep K. Saxena | V.K. Srivastav | Hitesh Vasudev | Nikhil Kumar
Source: Advances in Materials and Processing Technologies (article, 2023-01-29)
Field: Materials Science — Materials Chemistry

Data-driven materials science: application of ML for predicting band gap The conventional technique of materials design and discovery relies on experimentation and complex computational methods. The sheer number of materials present makes testing each one for specific properties and application makes the entire process time and resource intensive. This has resulted into Machine Learning (ML) to gain popularity in the domain of materials science to revolutionise and accelerate the entire process. Data-driven approaches such as these can be applied to narrow down the number of materials on which we apply the tradition methods, thus cutting down both the cost and time for development of new materials. This paper demonstrates how data-driven approach can be incorporated in materials science taking the use of band gap prediction of different materials using different machine ML algorithms. Using Random Forest Regressor, the highest R squared (r2) score of 0.685 and least root mean squared error (RMSE) of 0.87 eV were achieved. Mean squared error ; Process (computing) ; Computer science ; Random forest ; Domain (mathematical analysis) ; Materials science ; Algorithm ; Machine learning ; Statistics ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications ; X-ray Diffraction in Crystallography
Authors: Ghaidaa Raheem Lateef Al‐Awsi | Ameer A. Alameri | Aiman Mohammed Baqir Al‐Dhalimy | Gamal A. Gabr | Ehsan Kianfar
Source: Brazilian Journal of Biology (review, 2023-02-01)
Field: Materials Science — Materials Chemistry

Application of nano-antibiotics in the diagnosis and treatment of infectious diseases Infectious diseases are the leading cause of death worldwide. Thus, nanotechnology provides an excellent opportunity to treat drug-resistant microbial infections. Numerous antibiotics have been used to inhibit the growth and kill of microbes, but the development of resistance and the emergence of side effects have severely limited the use of these agents. Due to the development of the nanotechnology, nanoparticles are widely used as antimicrobials. Silver and chitosan nanoparticles have antifungal, antiviral and antibacterial properties, and many studies confirm the antifungal properties of silver nanoparticles. Nowadays, the use of nanoparticles in the diagnosis and treatment of infectious diseases has developed due to less side effects and also the help of these particles in effective drug delivery to the target tissue. Liposomes are also used as carriers of drug delivery, genes, and modeling of cell membranes in both animals and humans. The ability of these liposomes to encapsulate large amounts of drugs, minimize unwanted side effects, high effectiveness and low toxicity has attracted the interest of researchers. This review article examines recent efforts by researchers to identify and treat infectious diseases using antimicrobial nanoparticles and drug nano-carriers. Antifungal ; Antimicrobial ; Antibiotics ; Drug ; Biology ; Drug delivery ; Liposome ; Nanotechnology ; Drug resistance ; Pharmacology ; Microbiology ; Materials science Nanoparticles: synthesis and applications ; Biosensors and Analytical Detection ; Nanoparticle-Based Drug Delivery
Authors: Zhiqing Zhang | Rui Dong | Guanglin Lan | Tao Yuan | Dongli Tan
Source: Environmental Science and Pollution Research (review, 2023-02-08)
Field: Materials Science — Materials Chemistry

Diesel particulate filter regeneration mechanism of modern automobile engines and methods of reducing PM emissions: a review Diesel particulate filter ; Particulates ; Soot ; Diesel fuel ; Regeneration (biology) ; Diesel exhaust ; Diesel engine ; Exhaust gas recirculation ; Environmental science ; Selective catalytic reduction ; Waste management ; Automotive engineering ; Environmental engineering ; Exhaust gas ; Engineering ; Chemistry ; Catalysis ; Combustion Catalytic Processes in Materials Science ; Vehicle emissions and performance ; Air Quality and Health Impacts
Authors: Shan Zhang | Chao Wang
Source: Methods and Protocols (article, 2023-07-03)
Field: Materials Science — Biomaterials

Precise Analysis of Nanoparticle Size Distribution in TEM Image As an essential characterization, size distribution is an important indicator for the synthesis, optimization, and application of nanoparticles. Electron microscopes such as transmission electron microscopes (TEMs) are commonly utilized to collect size information on nanoparticles. However, the current popular statistical method of manually measuring large particles one by one, using a ruler tool in the corresponding image analysis software is time-consuming and can introduce manual errors. Moreover, it is difficult to determine the measurement interval for irregularly shaped nanoparticles. Therefore, it is necessary to use an efficient and standard method to perform size distribution analysis of nanoparticles. In this work, we use basic <i>ImageJ</i> software (1.53 t) to analyze the size of typical silica nanoparticles in a TEM image and use <i>Origin</i> software to process the data, to obtain its accurate distribution quickly. Using it as a template, we believe that this work can provide a paradigm for the standardized analysis of nanoparticle size. Software ; Computer science ; Nanoparticle ; Characterization (materials science) ; Microscope ; Process (computing) ; Image processing ; Image (mathematics) ; Work (physics) ; Nanotechnology ; Biological system ; Materials science ; Artificial intelligence ; Optics ; Physics ; Engineering ; Mechanical engineering Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticles: synthesis and applications
Authors: Zeshan Ali Sandhu | Muhammad Asam Raza | Nasser S. Awwad | Hala A. Ibrahium | Umme Farwa | Sawera Ashraf | Arooj Dildar | E Santo Fatima | Sufyan Ashraf | Furqan Ali
Source: Materials Advances (review, 2023-11-17)
Field: Chemistry — Inorganic Chemistry

Metal–organic frameworks for next-generation energy storage devices; a systematic review The future of renewable energy and sustainable transportation depends on advanced energy storage technologies. Energy storage ; Metal-organic framework ; Materials science ; Nanotechnology ; Process engineering ; Computer science ; Environmental science ; Engineering ; Chemistry ; Physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Daniel Nframah Ampong | Emmanuel Agyekum | Frank Ofori Agyemang | Kwadwo Mensah‐Darkwa | Anthony Andrews | Anuj Kumar | Ram K. Gupta
Source: Discover Nano (review, 2023-02-03)
Field: Materials Science — Materials Chemistry

MXene: fundamentals to applications in electrochemical energy storage A new, sizable family of 2D transition metal carbonitrides, carbides, and nitrides known as MXenes has attracted a lot of attention in recent years. This is because MXenes exhibit a variety of intriguing physical, chemical, mechanical, and electrochemical characteristics that are closely linked to the wide variety of their surface terminations and elemental compositions. Particularly, MXenes are readily converted into composites with materials including oxides, polymers, and CNTs, which makes it possible to modify their characteristics for a variety of uses. MXenes and MXene-based composites have demonstrated tremendous promise in environmental applications due to their excellent reducibility, conductivity, and biocompatibility, in addition to their well-known rise to prominence as electrode materials in the energy storage sector. The remarkable characteristics of 2D MXene, including high conductivity, high specific surface area, and enhanced hydrophilicity, account for the increasing prominence of its use in storage devices. In this review, we highlight the most recent developments in the use of MXenes and MXene-based composites for electrochemical energy storage while summarizing their synthesis and characteristics. Key attention is paid to applications in supercapacitors, batteries, and their flexible components. Future research challenges and perspectives are also described. MXenes ; Supercapacitor ; Nanotechnology ; Materials science ; Energy storage ; Carbide ; Nitride ; Electrochemical energy storage ; Electrochemistry ; Electrode ; Layer (electronics) ; Composite material ; Chemistry MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advanced Memory and Neural Computing
Authors: Jon G. C. Kragskow | Andrea Mattioni | Jakob K. Staab | Daniel Reta | Jonathan M. Skelton | Nicholas F. Chilton
Source: Chemical Society Reviews (review, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spin–phonon coupling and magnetic relaxation in single-molecule magnets Electron-phonon coupling is important in many physical phenomena, <i>e.g.</i> photosynthesis, catalysis and quantum information processing, but its impacts are difficult to grasp on the microscopic level. One area attracting wide interest is that of single-molecule magnets, which is motivated by searching for the ultimate limit in the miniaturisation of binary data storage media. The utility of a molecule to store magnetic information is quantified by the timescale of its magnetic reversal processes, also known as magnetic relaxation, which is limited by spin-phonon coupling. Several recent accomplishments of synthetic organometallic chemistry have led to the observation of molecular magnetic memory effects at temperatures above that of liquid nitrogen. These discoveries have highlighted how far chemical design strategies for maximising magnetic anisotropy have come, but have also highlighted the need to characterise the complex interplay between phonons and molecular spin states. The crucial step is to make a link between magnetic relaxation and chemical motifs, and so be able to produce design criteria to extend molecular magnetic memory. The basic physics associated with spin-phonon coupling and magnetic relaxation was outlined in the early 20th century using perturbation theory, and has more recently been recast in the form of a general open quantum systems formalism and tackled with different levels of approximations. It is the purpose of this Tutorial Review to introduce the topics of phonons, molecular spin-phonon coupling, and magnetic relaxation, and to outline the relevant theories in connection with both the traditional perturbative texts and the more modern open quantum systems methods. Molecular magnets ; Magnet ; Condensed matter physics ; Coupling (piping) ; Relaxation (psychology) ; Spin (aerodynamics) ; Phonon ; Magnetic relaxation ; Molecule ; Inductive coupling ; Materials science ; Physics ; Magnetic field ; Magnetization ; Quantum mechanics ; Thermodynamics Magnetism in coordination complexes ; Advanced NMR Techniques and Applications ; Quantum and electron transport phenomena