jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pablo del Mazo‐Sevillano | Jan Hermann
Source: PubMed (preprint, 2023-06-30)
Field: Materials Science — Materials Chemistry

Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional Practical density functional theory (DFT) owes its success to the groundbreaking work of Kohn and Sham that introduced the exact calculation of the non-interacting kinetic energy of the electrons using an auxiliary mean-field system. However, the full power of DFT will not be unleashed until the exact relationship between the electron density and the non-interacting kinetic energy is found. Various attempts have been made to approximate this functional, similar to the exchange-correlation functional, with much less success due to the larger contribution of kinetic energy and its more non-local nature. In this work, we propose a new and efficient regularization method to train density functionals based on deep neural networks, with particular interest in the kinetic-energy functional. The method is tested on (effectively) one-dimensional systems, including the hydrogen chain, non-interacting electrons, and atoms of the first two periods, with excellent results. For atomic systems, the generalizability of the regularization method is demonstrated by training also an exchange-correlation functional, and the contrasting nature of the two functionals is discussed from a machine-learning perspective. Kinetic energy ; Density functional theory ; Regularization (linguistics) ; Generalizability theory ; Orbital-free density functional theory ; Hybrid functional ; Electron ; Statistical physics ; Physics ; Time-dependent density functional theory ; Energy functional ; Work (physics) ; Computer science ; Quantum mechanics ; Mathematics ; Artificial intelligence Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Mahdiyeh Ghaffari | Mickey C. J. Lukkien | Nematollah Omidikia | Gerjen H. Tinnevelt | Marcel C. P. van Eijk | Jeroen J. Jansen
Source: arXiv (Cornell University) (preprint, 2023-08-28)
Field: Chemistry — Analytical Chemistry

Systematic reduction of Hyperspectral Images for high-throughput Plastic Characterization Hyperspectral Imaging (HSI) combines microscopy and spectroscopy to assess the spatial distribution of spectroscopically active compounds in objects, and has diverse applications in food quality control, pharmaceutical processes, and waste sorting. However, due to the large size of HSI datasets, it can be challenging to analyze and store them within a reasonable digital infrastructure, especially in waste sorting where speed and data storage resources are limited. Additionally, as with most spectroscopic data, there is significant redundancy, making pixel and variable selection crucial for retaining chemical information. Recent high-tech developments in chemometrics enable automated and evidence-based data reduction, which can substantially enhance the speed and performance of Non-Negative Matrix Factorization (NMF), a widely used algorithm for chemical resolution of HSI data. By recovering the pure contribution maps and spectral profiles of distributed compounds, NMF can provide evidence-based sorting decisions for efficient waste management. To improve the quality and efficiency of data analysis on hyperspectral imaging (HSI) data, we apply a convex-hull method to select essential pixels and wavelengths and remove uninformative and redundant information. This process minimizes computational strain and effectively eliminates highly mixed pixels. By reducing data redundancy, data investigation and analysis become more straightforward, as demonstrated in both simulated and real HSI data for plastic sorting. Hyperspectral imaging ; Redundancy (engineering) ; Computer science ; Pixel ; Sorting ; Data redundancy ; Data quality ; Chemometrics ; Reduction (mathematics) ; Data mining ; Non-negative matrix factorization ; Artificial intelligence ; Data reduction ; Dimensionality reduction ; Pattern recognition (psychology) ; Matrix decomposition ; Machine learning ; Algorithm ; Mathematics ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Aasia Ahmad | Papia Ganguly
Source: International Journal of Chemical and Environmental Sciences (article, 2023-07-03)
Field: Materials Science — Materials Chemistry

Quantum Dots in Drug Delivery: The Artificial Atom Drug delivery is the process of administering a pharmaceutical compound to achieve a therapeutic effect in humans or animals. Quantum dots (QDs), nano-sized semiconductors, are one of the most important materials that create a bridge between nanotechnology, and drug assay. Large-scale particles in drug delivery suffer from some major drawbacks. Thus arises the need for quantum dots. The following properties of quantum dots (especially carbon-based quantum dots) makes them promising candidates for drug delivery; their great aqueous stability, ability to cross cell membranes easily, high specific surface area which provides multiple attachment sites for drug targeting. Graphene quantum dots can load drugs through hydrophobic or noncovalent pi-pi stacking interactions or hydrogen bonding. Quantum dot ; Nanotechnology ; Drug delivery ; Stacking ; Drug ; Materials science ; Graphene ; Chemistry ; Organic chemistry ; Pharmacology ; Medicine Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanocluster Synthesis and Applications
Authors: Marcel Rodekamp | Evan Berkowitz | Maria Dincă | Christoph Gäntgen | Stefan Krieg | Thomas Luu
Source: arXiv (Cornell University) (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

From Theory to Practice: Applying Neural Networks to Simulate Real Systems with Sign Problems The numerical sign problem poses a seemingly insurmountable barrier to the simulation of many fascinating systems. We apply neural networks to deform the region of integration, mitigating the sign problem of systems with strongly correlated electrons. In this talk we present our latest architectural developments as applied to contour deformation. We also demonstrate its applicability to real systems, namely perylene. Sign (mathematics) ; Artificial neural network ; Computer science ; Artificial intelligence ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Quantum many-body systems
Authors: Sergey V. Baryshev | Matthias Muehle
Source: arXiv (Cornell University) (preprint, 2023-08-01)
Field: Materials Science — Materials Chemistry

Scalable Production and Supply Chain of Diamond using Microwave Plasma: a Mini-review Discovered and reported exactly 40 years ago, microwave plasma assisted chemical vapor deposition (MPACVD) pointed out an economic technology that could potentially produce lab-grown diamond stones at scale. After this breakthrough discovery, demonstrating that diamond can be growth at low pressure and temperature, the progress quickly curbed and synthetic single crystal diamond (SCD) size and quality could not be improved toward attaining requirements critical in solid-state electronics. This led to the early promise of MPACVD to not come true and slowed the level of investments, thereby further stalling the progress in diamond syntheses. With the invention of a few novel homo- and hetero-epitaxy growth techniques, the diamond research and technology has recently reinvigorated. This mini review attempts to capture the momentum of recent progress in diamond MPACVD that could finally bring scalable manufacturing of high quality large size wafers for future electronics and optics. Diamond ; Chemical vapor deposition ; Electronics ; Wafer ; Nanotechnology ; Scalability ; Epitaxy ; Materials science ; Microwave ; Engineering physics ; Computer science ; Engineering ; Electrical engineering ; Telecommunications ; Metallurgy Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Electronic and Structural Properties of Oxides
Authors: M. Khodaee | Nima Dalir | F. Feghhi | Narges Ansari | Mohammad Mohammadimasoudi | Alireza Goudarzi | Abdollah Nasiri | Mohammadreza Kolahdouz | Seyed Majid Mohseni
Source: Research Square (preprint, 2023-03-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancement in ionic conductivity of liquid crystals by graphene/metal-oxide-nanocomposite Abstract Enhancing the ionic conductivity of liquid crystal (LC) circumvents challenges for application in advanced electronic components. Toward this, using additives made of different nanostructures that could result in functional LCs is suggested. In this paper, various concentrations of graphene (Gr)/metal-oxide (Fe 3 O 4 ) nanocomposite (GMN) (0.0001-1 w%) were added to E7 nematic LC. We found that the role of anisotropic Gr flakes, their edges as well as surface-decorated-metal-oxide-additives have significant impact on electrical properties of E7. A range of appropriate additives of such a nanocomposite enhances the electrical conductivity of LCs. This effect can be traced through the decrease in the formation of GMN aggregates in the E7 and increase in the electrostatic field at the edges of the Gr sheets. Moreover, the presence of metal-oxide nanoclusters due to the presence of oxygen vacancies and defects facilitates the construction of conductive network for improving the charge transfer pathways and contributes to a stronger interaction of the Gr surface with charged species. These factors can provide Gr layers as dipole moments and lead to signal propagation in the dielectric medium. Our finding conveys a pathway toward significant enhancement of ionic conductivity in the LC family which can be useful for functional applications. Materials science ; Graphene ; Nanocomposite ; Nanoclusters ; Oxide ; Conductivity ; Dielectric ; Metal ; Ionic bonding ; Liquid crystal ; Dipole ; Chemical engineering ; Nanotechnology ; Ionic conductivity ; Electrode ; Ion ; Chemistry ; Optoelectronics ; Organic chemistry ; Physical chemistry Liquid Crystal Research Advancements ; Advanced Sensor and Energy Harvesting Materials ; Advanced Materials and Mechanics
Authors: Fabio Santanni | Laura Chelazzi | Lorenzo Sorace | Grigore A. Timco | Roberta Sessoli
Source: Crystals (article, 2023-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structural and Magnetic Properties of the {Cr(pybd)3[Cu(cyclen)]2}(BF4)4 Heteronuclear Complex Heterotopic ligands containing chemically different binding centers are appealing candidates for obtaining heteronuclear metal complexes. By exploiting this strategy, it is possible to introduce different paramagnetic centers characterized by specific anisotropic magnetic properties that make them distinguishable when weakly magnetically coupled. This molecular approach has great potential to yield multi-spin adducts capable of mimicking logical architectures necessary for quantum information processing (QIP), i.e., quantum logic gates. A possible route for including a single-ion magnetic center within a finite-sized heterometallic compound uses the asymmetric (1-pyridyl)-butane-1,3-dione (pybd) ligand reported in the literature for obtaining Cr3+−Cu2+ metallo-cages. To avoid the formation of cages, we adopted the cyclen (1,4,7,10-tetraazacyclododecane) ligand as a “capping” agent for the Cu2+ ions. We report here the structural and magnetic characterization of the unprecedented adduct {Cr(pybd)3[Cu(cyclen)]2}(BF4)4, whose structure is characterized by a central Cr3+ ion in a distorted octahedral coordination environment and two peripheral Cu2+ ions with square-pyramidal coordination geometries. As highlighted by Continuous Wave Electron Paramagnetic Resonance (EPR) spectroscopy and Direct Current (DC) magnetometry measurements, this adduct shows negligible intramolecular magnetic couplings, and it maintains the characteristic EPR signals of Cr3+ and Cu2+ moieties when diluted in frozen solutions. Heteronuclear molecule ; Cyclen ; Electron paramagnetic resonance ; Chemistry ; Crystallography ; Adduct ; Ligand (biochemistry) ; Paramagnetism ; Intramolecular force ; Ion ; Nuclear magnetic resonance spectroscopy ; Octahedron ; Nuclear magnetic resonance ; Stereochemistry ; Condensed matter physics ; Crystal structure ; Physics Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Electron Spin Resonance Studies
Source: New Journal of Chemistry (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Contents list Permissions Request permissions Contents list New J. Chem., 2023, 47, 18883 DOI: 10.1039/D3NJ90159A This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Chemistry ; Acknowledgement ; Chromatin structure remodeling (RSC) complex ; Attribution ; Library science ; Computer security ; Computer science ; Psychology Radioactive element chemistry and processing
Authors: Alexandre Moreno | Jesús Urbano | Marta Pérez | Rosalı́a Rodrı́guez | Salvador Aznar | Francisco Perdigones | J.M. Quero | Carlos Domı́nguez | Cecilia Jiménez‐Jorquera | Xavier Muñoz
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Biomaterials

Silk Fibroin Functionalised Isfets with Enhanced Stability for Long-Term Ph Monitoring of Cell Cultures Fibroin ; SILK ; Materials science ; ISFET ; Chemical engineering ; Silicon nitride ; Biofouling ; Biocompatibility ; Nanotechnology ; Chemistry ; Composite material ; Membrane ; Field-effect transistor ; Transistor ; Electrical engineering ; Layer (electronics) Silk-based biomaterials and applications ; Surface Modification and Superhydrophobicity ; Polymer Surface Interaction Studies
Authors: Nicolas Castel | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2023-02-22)
Field: Materials Science — Materials Chemistry

Computation of Finite Temperature Mechanical Properties of ZIF Glasses by Molecular Dynamics Mechanical properties of amorphous phases of metal-organic frameworks, such as MOF glasses, are difficult to determine experimentally. Moreover, computational characterization is limited by the level of theory chosen for the description of interatomic interactions and is often computationally expensive. In this work, we have extensively investigated the computation of finite temperature mechanical properties of ZIF-4 in the crystal and glass phases. We critically assessed computational methodologies including ab initio molecular dynamics, reactive force fields, and classical force fields, based on a variety of glass models. We find that ZIF-4 glasses have a larger bulk modulus than the crystal and confirm previous studies that the density is larger for the glass phases. Moreover, we confirm in the case of ZIF glasses the relationship between density and bulk modulus, showing that obtaining models of correct density is key to the prediction of physical properties for these systems. Molecular dynamics ; Amorphous solid ; Materials science ; Computation ; Crystal (programming language) ; Amorphous metal ; Work (physics) ; Modulus ; Characterization (materials science) ; Elastic modulus ; Statistical physics ; Bulk modulus ; Thermodynamics ; Nanotechnology ; Physics ; Computational chemistry ; Computer science ; Composite material ; Chemistry ; Crystallography ; Algorithm Material Dynamics and Properties ; Solid-state spectroscopy and crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Wendong Wang | Nick Clark | Matthew J. Hamer | Amy Carl | Endre Tóvári | Sam Sullivan Allsop | Evan Tillotson | Yunze Gao | Hugo de Latour | Francisco Selles | James Howarth | Eli G. Castanon | Mingwei Zhou | Haoyu Bai | Xiao Li | Astrid Weston | Kenji Watanabe | Takashi Taniguchi | Cecilia Mattevi | Thomas H. Bointon | Paul V. Wiper | Andrew J. Strudwick | Л. А. Пономаренко | Andrey V. Kretinin | Sarah J. Haigh | Alex Summerfield | Roman Gorbachev
Source: arXiv (Cornell University) (preprint, 2023-08-25)
Field: Materials Science — Materials Chemistry

Ultra-clean assembly of van der Waals heterostructures Layer-by-layer assembly of van der Waals (vdW) heterostructures underpins new discoveries in solid state physics, material science and chemistry. Despite the successes, all current 2D material (2DM) transfer techniques rely on the use of polymers which limit the cleanliness, ultimate electronic performance, and potential for optoelectronic applications of the heterostructures. In this article, we present a novel polymer-free platform for rapid and facile heterostructure assembly which utilises re-usable flexible silicon nitride membranes. We demonstrate that this allows fast and reproducible production of 2D heterostructures using both exfoliated and CVD-grown materials with perfect interfaces free from interlayer contamination and correspondingly excellent electronic behaviour, limited only by the size and intrinsic quality of the crystals used. Furthermore, removing the need for polymeric carriers allows new possibilities for vdW heterostructure fabrication: assembly at high temperatures up to 600°C, and in different environments including ultra-high vacuum (UHV) and when the materials are fully submerged in liquids. We demonstrate UHV heterostructure assembly for the first time, and show the reliable creation of graphene moiré superlattices with more than an order of magnitude improvement in their structural homogeneity. We believe that broad adaptation of our novel inorganic 2D materials assembly strategy will allow realisation of the full potential of vdW heterostructures as a platform for new physics and advanced optoelectronic technologies. Heterojunction ; Nanotechnology ; Materials science ; Graphene ; van der Waals force ; Fabrication ; Superlattice ; Optoelectronics ; Chemistry Graphene research and applications ; 2D Materials and Applications ; Energy Harvesting in Wireless Networks
Authors: Qurtul en | Anees Ahmad
Source: Research Square (preprint, 2023-08-31)
Field: Materials Science — Materials Chemistry

Green Tea Waste-Derived Carbon Quantum Dots: Efficient Degradation of RhB Dye and Selective Sensing of Cu2+ Degradation (telecommunications) ; Carbon quantum dots ; Quantum dot ; Green tea ; Carbon fibers ; Chemistry ; Environmental science ; Materials science ; Waste management ; Nanotechnology ; Computer science ; Telecommunications ; Food science ; Composite material ; Composite number ; Engineering Carbon and Quantum Dots Applications ; Mercury impact and mitigation studies ; Graphene and Nanomaterials Applications
Authors: Jiewei Cheng | Tingwei Li | Yongyi Wang | Ahmed H. Ati | Qiang Sun
Source: arXiv (Cornell University) (preprint, 2023-07-08)
Field: Materials Science — Materials Chemistry

The relationship between activated H2 bond length and adsorption distance on MXenes identified with graph neural network and resonating valence bond theory Motivated by the recent experimental study on hydrogen storage in MXene multilayers [Nature Nanotechnol. 2021, 16, 331], for the first time we propose a workflow to computationally screen 23,857 compounds of MXene to explore the general relation between the activated H2 bond length and adsorption distance. By using density functional theory (DFT), we generate a dataset to investigate the adsorption geometries of hydrogen on MXenes, based on which we train physics-informed atomistic line graph neural networks (ALIGNNs) to predict adsorption parameters. To fit the results, we further derived a formula that quantitatively reproduces the dependence of H2 bond length on the adsorption distance from MXenes within the framework of Pauling's resonating valence bond (RVB) theory, revealing the impact of transition metal's ligancy and valence on activating dihydrogen in H2 storage. MXenes ; Adsorption ; Density functional theory ; Valence (chemistry) ; Valence bond theory ; Hydrogen bond ; Bond length ; Materials science ; Chemical physics ; Graph theory ; Computational chemistry ; Statistical physics ; Chemistry ; Condensed matter physics ; Physics ; Molecule ; Physical chemistry ; Nanotechnology ; Mathematics ; Quantum mechanics ; Combinatorics MXene and MAX Phase Materials ; Hydrogen Storage and Materials ; Graphene research and applications
Authors: Chang Xu | Shiqiang Guan | Xufeng Dong | Hao Huang | Min Qi
Source: Research Square (preprint, 2023-01-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polyaniline Sandwiched Ultra-strong PVA/PAA Hybrid Hydrogel for Low-temperature Resistant Supercapacitors and Strain Sensors Abstract Hydrogels are a class of promising candidate materials for the preparation of emerging quasi-solid-state supercapacitor electrolytes. However, the inherent defects of poor mechanical strength, easy fragmentation, and bad low-temperature tolerance seriously hinder their further practical application. Herein, a polyaniline sandwiched polyvinyl alcohol/polyacrylic acid hybrid hydrogel (PVA-PAA-PANI) electrolyte with high strength and ideal low-temperature resistance was constructed by combining the Hofmeister effect and in-situ polymerization. This PVA-PAA-PANI hydrogel can provide amazing comprehensive mechanical properties, including 3.5 MPa of breaking strength, 700% of elongation at break, and 11.5 MJ/m 3 of toughness, and exhibits satisfactory fatigue resistance. This PVA-PAA-PANI hydrogel-based supercapacitor has a specific capacitance of 32.4 mF/cm 2 and achieves 61% capacitance retention even under the harsh cold environment of -20°C. Notably, the capacitance retention reaches 79.2% of the initial state after suffering more than 5000 continuous charge/discharge cycles at -20°C. This PVA-PAA-PANI hydrogel also has good linear sensing behavior and can accurately detect different limb movements and even breathing or speech, demonstrating ideal strain sensing properties. This work develops new strategies for the preparation of high-strength hydrogels for energy storage devices and sensors, and promotes their application in real-life applications. Supercapacitor ; Materials science ; Capacitance ; Self-healing hydrogels ; Electrolyte ; Polyaniline ; Polyvinyl alcohol ; Polyacrylic acid ; Composite material ; Polymerization ; Nanotechnology ; Chemical engineering ; Polymer ; Electrode ; Polymer chemistry ; Chemistry Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications