jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ryo Nagai | Ryosuke Akashi | Osamu Sugino
Source: Physical Review Research (article, 2022-02-11)
Field: Materials Science — Materials Chemistry

Machine-learning-based exchange correlation functional with physical asymptotic constraints Density functional theory is the standard theory for computing the electronic structure of materials, which is based on a functional that maps the electron density to the energy. However, a rigorous form of the functional is not known and has been heuristically constructed by interpolating asymptotic constraints known for extreme situations, such as isolated atoms and uniform electron gas. Recent studies have demonstrated that the functional can be effectively approximated using machine learning (ML) approaches. However, most ML models do not satisfy asymptotic constraints. In this paper, by applying a ML model architecture, we demonstrate a neural network-based exchange-correlation functional satisfying physical asymptotic constraints. Calculations reveal that the trained functional is applicable to various materials with an accuracy higher than that of existing functionals, even for materials whose electronic properties are not included in training dataset. Our proposed method thus improves the accuracy and generalization performance of the ML-based functional by combining the advantages of ML and analytical modeling. Density functional theory ; Generalization ; Energy functional ; Orbital-free density functional theory ; Hybrid functional ; Computer science ; Artificial neural network ; Applied mathematics ; Mathematics ; Artificial intelligence ; Mathematical analysis ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Janine George | Guido Petretto | Aakash Ashok Naik | Marco Esters | Adam Jackson | Ryky Nelson | Richard Dronskowski | Gian‐Marco Rignanese | Geoffroy Hautier
Source: ChemPlusChem (article, 2022-06-07)
Field: Materials Science — Materials Chemistry

Automated Bonding Analysis with Crystal Orbital Hamilton Populations Understanding crystalline structures based on their chemical bonding is growing in importance. In this context, chemical bonding can be studied with the Crystal Orbital Hamilton Population (COHP), allowing for quantifying interatomic bond strength. Here we present a new set of tools to automate the calculation of COHP and analyze the results. We use the program packages VASP and LOBSTER, and the Python packages atomate and pymatgen. The analysis produced by our tools includes plots, a textual description, and key data in a machine-readable format. To illustrate those capabilities, we have selected simple test compounds (NaCl, GaN), the oxynitrides BaTaO<sub>2</sub> N, CaTaO<sub>2</sub> N, and SrTaO<sub>2</sub> N, and the thermoelectric material Yb<sub>14</sub> Mn<sub>1</sub> Sb<sub>11</sub> . We show correlations between bond strengths and stabilities in the oxynitrides and the influence of the Mn-Sb bonds on the magnetism in Yb<sub>14</sub> Mn<sub>1</sub> Sb<sub>11</sub> . Our contribution enables high-throughput bonding analysis and will facilitate the use of bonding information for machine learning studies. Python (programming language) ; Chemical bond ; Context (archaeology) ; Crystal structure prediction ; Population ; Computer science ; Materials science ; Crystal structure ; Crystallography ; Nanotechnology ; Computational science ; Chemistry ; Programming language ; Biology Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Yu-Chuan Hsu | Zhenze Yang | Markus J. Buehler
Source: APL Materials (article, 2022-04-01)
Field: Materials Science — Materials Chemistry

Generative design, manufacturing, and molecular modeling of 3D architected materials based on natural language input We describe a method to generate 3D architected materials based on mathematically parameterized human readable word input, offering a direct materialization of language. Our method uses a combination of a vector quantized generative adversarial network and contrastive language-image pre-training neural networks to generate images, which are translated into 3D architectures that are then 3D printed using fused deposition modeling into materials with varying rigidity. The novel materials are further analyzed in a metallic realization as an aluminum-based nano-architecture, using molecular dynamics modeling and thereby providing mechanistic insights into the physical behavior of the material under extreme compressive loading. This work offers a novel way to design, understand, and manufacture 3D architected materials designed from mathematically parameterized language input. Our work features, at its core, a generally applicable algorithm that transforms any 2D image data into hierarchical fully tileable, periodic architected materials. This method can have broader applications beyond language-based materials design and can render other avenues for the analysis and manufacturing of architected materials, including microstructure gradients through parametric modeling. As an emerging field, language-based design approaches can have a profound impact on end-to-end design environments and drive a new understanding of physical phenomena that intersect directly with human language and creativity. It may also be used to exploit information mined from diverse and complex databases and data sources. Computer science ; Parameterized complexity ; Modeling language ; Artificial intelligence ; Algorithm ; Programming language Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Cellular and Composite Structures
Authors: Pin Yu Chew | Aleks Reinhardt
Source: The Journal of Chemical Physics (review, 2022-12-15)
Field: Materials Science — Materials Chemistry

Phase diagrams—Why they matter and how to predict them Understanding the thermodynamic stability and metastability of materials can help us to, for example, gauge whether crystalline polymorphs in pharmaceutical formulations are likely to be durable. It can also help us to design experimental routes to novel phases with potentially interesting properties. In this Perspective, we provide an overview of how thermodynamic phase behavior can be quantified both in computer simulations and machine-learning approaches to determine phase diagrams, as well as combinations of the two. We review the basic workflow of free-energy computations for condensed phases, including some practical implementation advice, ranging from the Frenkel-Ladd approach to thermodynamic integration and to direct-coexistence simulations. We illustrate the applications of such methods on a range of systems from materials chemistry to biological phase separation. Finally, we outline some challenges, questions, and practical applications of phase-diagram determination which we believe are likely to be possible to address in the near future using such state-of-the-art free-energy calculations, which may provide fundamental insight into separation processes using multicomponent solvents. Phase diagram ; Metastability ; Computer science ; Thermodynamic integration ; Workflow ; Statistical physics ; Computation ; Phase (matter) ; CALPHAD ; Range (aeronautics) ; Energy (signal processing) ; Nanotechnology ; Materials science ; Chemistry ; Physics ; Algorithm Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Protein Structure and Dynamics
Authors: Eva Cuypers | Britt S. R. Claes | Rianne Biemans | Natasja G. Lieuwes | Kristine Glunde | Ludwig J. Dubois | Ron M. A. Heeren
Source: Analytical Chemistry (article, 2022-04-12)
Field: Chemistry — Spectroscopy

‘On the Spot’ Digital Pathology of Breast Cancer Based on Single-Cell Mass Spectrometry Imaging The molecular pathology of breast cancer is challenging due to the complex heterogeneity of cellular subtypes. The ability to directly identify and visualize cell subtype distribution at the single-cell level within a tissue section enables precise and rapid diagnosis and prognosis. Here, we applied mass spectrometry imaging (MSI) to acquire and visualize the molecular profiles at the single-cell and subcellular levels of 14 different breast cancer cell lines. We built a molecular library of genetically well-characterized cell lines. Multistep processing, including deep learning, resulted in a breast cancer subtype, the cancer's hormone status, and a genotypic recognition model based on metabolic phenotypes with cross-validation rates of up to 97%. Moreover, we applied our single-cell-based recognition models to complex tissue samples, identifying cell subtypes in tissue context within seconds during measurement. These data demonstrate "on the spot" digital pathology at the single-cell level using MSI, and they provide a framework for fast and accurate high spatial resolution diagnostics and prognostics. Breast cancer ; Computational biology ; Context (archaeology) ; Mass spectrometry imaging ; Chemistry ; Cell ; Single-cell analysis ; Cancer ; Phenotype ; Pathology ; Mass spectrometry ; Biology ; Medicine ; Biochemistry ; Genetics Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Emiliano Martínez‐Periñán | Marcos Martínez‐Fernández | José L. Segura | Encarnación Lorenzo
Source: Sensors (review, 2022-06-23)
Field: Materials Science — Materials Chemistry

Electrochemical (Bio)Sensors Based on Covalent Organic Frameworks (COFs) Covalent organic frameworks (COFs) are defined as crystalline organic polymers with programmable topological architectures using properly predesigned building blocks precursors. Since the development of the first COF in 2005, many works are emerging using this kind of material for different applications, such as the development of electrochemical sensors and biosensors. COF shows superb characteristics, such as tuneable pore size and structure, permanent porosity, high surface area, thermal stability, and low density. Apart from these special properties, COF's electrochemical behaviour can be modulated using electroactive building blocks. Furthermore, the great variety of functional groups that can be inserted in their structures makes them interesting materials to be conjugated with biological recognition elements, such as antibodies, enzymes, DNA probe, aptamer, etc. Moreover, the possibility of linking them with other special nanomaterials opens a wide range of possibilities to develop new electrochemical sensors and biosensors. Covalent bond ; Electrochemistry ; Nanotechnology ; Materials science ; Combinatorial chemistry ; Chemistry ; Electrode ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Mudasir Younis Sofi | Dinesh C. Gupta
Source: Scientific Reports (article, 2022-11-14)
Field: Materials Science — Materials Chemistry

Scrutinized the inherent spin half-metallicity and thermoelectric response of f-electron-based RbMO3 (M = Np, Pu) perovskites: a computational assessment In the hunt for novel materials, we present self-consistent ab initio simulations of the structural stability, electronic profile, and transport properties of f-electron-based RbMO<sub>3</sub> (M = Np, Pu) perovskites within the context of density functional theory. The structural stability and thermodynamic concerns are fixed by relaxing the crystal structure and computing the energy of formation, respectively. Furthermore, the decisive physical features of given materials have been outlined using the optimised lattice constant retrieved from structural optimizations. The ground state magnetic phase stability is ascertained by minimizing Birch Murnaghan's equation of state in distinct magnetic phases, upholding the ferromagnetic phase (FM) as the ground state magnetic phase, which is further backed by positive Curie Wiess constant values. To specify the electronic structure, a mix of the two approximations GGA and GGA + mBJ has been executed, both of which assert the half-metallic character, culminating in 100% spin polarisation at the Fermi level. The study of the magnetic moment and Curie temperature of each material has further been assessed in the present study. Apart from half-metallicity, the thermoelectric response of the present materials is quantified by exploring the chemical potential dependency of several transport parameters like Seebeck coefficient, electrical and thermal conductivity, power factor, etc. Moreover, the thermoelectric competence has been tested using a zT calculation, adapting values of 1.01 and 0.987 at 300 K for RbNpO<sub>3</sub> and RbPuO<sub>3</sub>, respectively. The high electronic zT at encompassing temperatures uncovers the significant utility of these materials in both low-and high-temperature thermoelectric device applications. In essence, the comprehensive survey of these alloys could certainly open up their possibilities in spintronics, thermoelectric, and solid-state (RTG) device applications. Metallicity ; Spin (aerodynamics) ; Electron ; Thermoelectric effect ; Condensed matter physics ; Physics ; Materials science ; Quantum mechanics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Magnetic and transport properties of perovskites and related materials
Authors: Hend M. Ahmed | Meram S. Abdelrahman | Naser Gad Al-Balakocy | Zhen Wen | Tawfik A. Khattab
Source: Journal of Polymers and the Environment (article, 2022-09-29)
Field: Materials Science — Materials Chemistry

Preparation of Photochromic and Photoluminescent Nonwoven Fibrous Mat from Recycled Polyester Waste Abstract Photochromic and photoluminescent clothes can be described as smart textiles that alter their color and emission spectra upon exposure to a light stimulus. Recycled nonwoven polyester fabrics screen-printed with rare-earth strontium aluminate nanoparticles were developed to introduce photochromic and photoluminescent properties. Both spinning and preparation of nonwoven fibrous mat was performed industrially starting from recycled polyester waste. Aqueous-based phosphor-binder nanocomposites containing different concentrations of inorganic phosphor with excellent thermal and photostability were applied directly onto nonwoven polyester fabrics. The screen-printing process produced a uniform photochromic and photoluminescent film onto the nonwoven polyester surface that showed strong green emission color (440 nm) under UV light even at lower phosphor concentrations (0.5 wt%) in the printing paste. The excitation wavelength of the printed nonwoven polyester samples was monitored at 382 nm. Long-persistent greenish-yellow phosphorescence was detected in the dark at higher phosphor concentrations. The morphological microscopic data of phosphor nanoparticles and printed nonwoven polyester fabrics were collected using various analytical methods. TEM analysis of phosphor nanoparticles designated diameters of 4–11 nm, whereas XRD analysis indicated a crystal size of 9 nm. The printed cloth exhibited a quick and reversible photochromic emission when exposed to ultraviolet light. The ultraviolet protection, antimicrobial and superhydrophobic properties were improved with increasing the pigment concentration in the printing paste. The static contact and slide angles improved in the ranges of 108.6°–132.6°, and 12°–7°, respectively. The effects of increasing the phosphor concentration in the printing paste on the comfort features and colorfastness were examined. Materials science ; Polyester ; Phosphor ; Photoluminescence ; Photochromism ; Ultraviolet ; Composite material ; Nonwoven fabric ; Ultraviolet light ; Chemical engineering ; Nanotechnology ; Optoelectronics ; Fiber Photochromic and Fluorescence Chemistry ; Pigment Synthesis and Properties ; Photopolymerization techniques and applications
Authors: Md. Mominur Rhaman | Md. Rezaul Islam | Shopnil Akash | Mobasharah Mim | Md Noor A Alam | Eugenie Nepovimová | Martin Vališ | Kamil Kuča | Rohit Sharma
Source: Frontiers in Cell and Developmental Biology (review, 2022-09-02)
Field: Materials Science — Biomaterials

Exploring the role of nanomedicines for the therapeutic approach of central nervous system dysfunction: At a glance In recent decades, research scientists, molecular biologists, and pharmacologists have placed a strong emphasis on cutting-edge nanostructured materials technologies to increase medicine delivery to the central nervous system (CNS). The application of nanoscience for the treatment of neurodegenerative diseases (NDs) such as Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), Huntington's disease (HD), brain cancer, and hemorrhage has the potential to transform care. Multiple studies have indicated that nanomaterials can be used to successfully treat CNS disorders in the case of neurodegeneration. Nanomedicine development for the cure of degenerative and inflammatory diseases of the nervous system is critical. Nanoparticles may act as a drug transporter that can precisely target sick brain sub-regions, boosting therapy success. It is important to develop strategies that can penetrate the blood-brain barrier (BBB) and improve the effectiveness of medications. One of the probable tactics is the use of different nanoscale materials. These nano-based pharmaceuticals offer low toxicity, tailored delivery, high stability, and drug loading capacity. They may also increase therapeutic effectiveness. A few examples of the many different kinds and forms of nanomaterials that have been widely employed to treat neurological diseases include quantum dots, dendrimers, metallic nanoparticles, polymeric nanoparticles, carbon nanotubes, liposomes, and micelles. These unique qualities, including sensitivity, selectivity, and ability to traverse the BBB when employed in nano-sized particles, make these nanoparticles useful for imaging studies and treatment of NDs. Multifunctional nanoparticles carrying pharmacological medications serve two purposes: they improve medication distribution while also enabling cell dynamics imaging and pharmacokinetic study. However, because of the potential for wide-ranging clinical implications, safety concerns persist, limiting any potential for translation. The evidence for using nanotechnology to create drug delivery systems that could pass across the BBB and deliver therapeutic chemicals to CNS was examined in this study. Central nervous system ; Neuroscience ; Medicine ; Intensive care medicine ; Biology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Karin Hochrainer | Wei Yang
Source: Circulation Research (article, 2022-04-14)
Field: Chemistry — Spectroscopy

Stroke Proteomics: From Discovery to Diagnostic and Therapeutic Applications Stroke remains a leading cause of death and disability, with limited therapeutic options and suboptimal tools for diagnosis and prognosis. High throughput technologies such as proteomics generate large volumes of experimental data at once, thus providing an advanced opportunity to improve the status quo by facilitating identification of novel therapeutic targets and molecular biomarkers. Proteomics studies in animals are largely designed to decipher molecular pathways and targets altered in brain tissue after stroke, whereas studies in human patients primarily focus on biomarker discovery in biofluids and, more recently, in thrombi and extracellular vesicles. Here, we offer a comprehensive review of stroke proteomics studies conducted in both animal and human specimen and present our view on limitations, challenges, and future perspectives in the field. In addition, as a unique resource for the scientific community, we provide extensive lists of all proteins identified in proteomic studies as altered by stroke and perform postanalysis of animal data to reveal stroke-related cellular processes and pathways. Proteomics ; Biomarker ; Biomarker discovery ; Stroke (engine) ; DECIPHER ; Identification (biology) ; Proteogenomics Advanced Proteomics Techniques and Applications ; S100 Proteins and Annexins ; Extracellular vesicles in disease
Authors: Prashanth Gopala Krishna | Prabhu Chandra Mishra | M. Mutthuraju | Manoj Gadewar | Prashanth Paduvarahalli Ananthaswamy | Srilatha Rao | S. R. Boselin Prabhu | K.V. Yatish | H. G. Nagendra | Mahmoud Moustafa | Mohammed Ali Alshehri | Saurabh Kumar Jha | Bharat Lal | Sreeja Mole S S
Source: Frontiers in Chemistry (review, 2022-09-02)
Field: Materials Science — Materials Chemistry

Photocatalytic Activity Induced by Metal Nanoparticles Synthesized by Sustainable Approaches: A Comprehensive Review Nanotechnology is a fast-expanding area with a wide range of applications in science, engineering, health, pharmacy, and other fields. Among many techniques that are employed toward the production of nanoparticles, synthesis using green technologies is the simplest and environment friendly. Nanoparticles produced from plant extracts have become a very popular subject of study in recent decades due to their diverse advantages such as low-cost synthesis, product stability, and ecofriendly protocols. These merits have prompted the development of nanoparticles from a variety of sources, including bacteria, fungi, algae, proteins, enzymes, etc., allowing for large-scale production with minimal contamination. However, nanoparticles obtained from plant extracts and phytochemicals exhibit greater reduction and stabilization and hence have proven the diversity of properties, like catalyst/photocatalyst, magnetic, antibacterial, cytotoxicity, circulating tumor deoxy ribo nucleic acid (CT-DNA) binding, gas sensing, etc. In the current scenario, nanoparticles can also play a critical role in cleaning wastewater and making it viable for a variety of operations. Nano-sized photocatalysts have a great scope toward the removal of large pollutants like organic dyes, heavy metals, and pesticides in an eco-friendly and sustainable manner from industrial effluents. Thus, in this review article, we discuss the synthesis of several metal nanoparticles using diverse plant extracts, as well as their characterization via techniques like UV-vis (ultraviolet-visible), XRD (X-ray diffraction), SEM (scanning electron microscopy), TEM (transmission electron microscopy), FTIR (Fourier transform infrared spectroscopy), etc., and catalytic activity on various hazardous systems. Photocatalysis ; Nanoparticle ; Nanotechnology ; Materials science ; Green chemistry ; Catalysis ; Environmental pollution ; Fourier transform infrared spectroscopy ; Chemistry ; Chemical engineering ; Organic chemistry ; Environmental science Nanoparticles: synthesis and applications ; TiO2 Photocatalysis and Solar Cells ; Advanced Nanomaterials in Catalysis
Authors: Linying Cai | Yegang Lü | Honghui Zhu
Source: Optics and Lasers in Engineering (article, 2022-12-16)
Field: Materials Science — Materials Chemistry

Performance enhancement of on-chip optical switch and memory using Ge2Sb2Te5 slot-assisted microring resonator Resonator ; Optoelectronics ; Photonics ; Materials science ; Extinction ratio ; Free spectral range ; Amorphous solid ; Optical switch ; Neuromorphic engineering ; Insertion loss ; Flash memory ; Optics ; Computer science ; Physics ; Computer hardware ; Wavelength Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Neural Networks and Reservoir Computing