jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joanna Jankowska | Andrzej L. Sobolewski
Source: Molecules (review, 2021-08-25)
Field: Chemistry — Physical and Theoretical Chemistry

Modern Theoretical Approaches to Modeling the Excited-State Intramolecular Proton Transfer: An Overview The excited-state intramolecular proton transfer (ESIPT) phenomenon is nowadays widely acknowledged to play a crucial role in many photobiological and photochemical processes. It is an extremely fast transformation, often taking place at sub-100 fs timescales. While its experimental characterization can be highly challenging, a rich manifold of theoretical approaches at different levels is nowadays available to support and guide experimental investigations. In this perspective, we summarize the state-of-the-art quantum-chemical methods, as well as molecular- and quantum-dynamics tools successfully applied in ESIPT process studies, focusing on a critical comparison of their specific properties. Intramolecular force ; Excited state ; Computer science ; Proton ; Quantum ; Quantum chemical ; Process (computing) ; Biochemical engineering ; Chemical physics ; Nanotechnology ; Chemistry ; Materials science ; Physics ; Molecule ; Quantum mechanics ; Engineering Photochemistry and Electron Transfer Studies ; Spectroscopy and Quantum Chemical Studies ; Porphyrin and Phthalocyanine Chemistry
Authors: Ghislaine Vantomme | Jeanne Crassous
Source: Chirality (article, 2021-08-06)
Field: Chemistry — Physical and Theoretical Chemistry

Pasteur and chirality: A story of how serendipity favors the prepared minds During the XIXth century, France has been the theater of many discoveries in stereochemistry thanks to prestigious physicists, chemists, and crystallographers. Among them, Louis Pasteur is certainly the father of molecular chirality (the so-called molecular dissymmetry), and because of the exceptionally favored conditions of his famous discovery on the resolution of tartrates, his findings have often been attributed to serendipity. But let us not forget that "in the fields of observation, chance only favors the prepared minds." Serendipity ; Chemistry ; Chirality (physics) ; Resolution (logic) ; Nanotechnology ; Epistemology ; Philosophy ; Artificial intelligence ; Chiral symmetry ; Physics ; Quantum mechanics History and advancements in chemistry ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Trevor G. Aguirre | Benjamin W. Lamm | Corson L. Cramer | David J. Mitchell
Source: Ceramics International (review, 2021-11-29)
Field: Materials Science — Ceramics and Composites

Zirconium-diboride silicon-carbide composites: A review Zirconium diboride ; Materials science ; Silicon carbide ; Composite material ; Spark plasma sintering ; Chemical vapor infiltration ; Sintering ; Hot pressing ; Zirconium ; Titanium diboride ; Chemical vapor deposition ; Ceramic ; Thermal shock ; Metallurgy ; Nanotechnology Advanced ceramic materials synthesis ; Advanced materials and composites ; Metal and Thin Film Mechanics
Authors: Aikaterini Anastasopoulou | Hiroyasu Furukawa | Brandon R. Barnett | Henry Z. H. Jiang | Jeffrey R. Long | Hanna Breunig
Source: Energy & Environmental Science (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Technoeconomic analysis of metal–organic frameworks for bulk hydrogen transportation The analysis presents a novel method for evaluating the technology status and cost of metal–organic frameworks for H<sub>2</sub>delivery. Metal-organic framework ; Hydrogen ; Hydrogen storage ; Cost analysis ; Waste management ; Environmental science ; Materials science ; Engineering ; Chemistry ; Operations research ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Hydrogen Storage and Materials
Authors: Ruizhen Tian | Jiayun Xu | Quan Luo | Chunxi Hou | Junqiu Liu
Source: Frontiers in Chemistry (review, 2021-02-11)
Field: Materials Science — Materials Chemistry

Rational Design and Biological Application of Antioxidant Nanozymes Nanozyme is a type of nanostructured material with intrinsic enzyme mimicking activity, which has been increasingly studied in the biological field. Compared with natural enzymes, nanozymes have many advantages, such as higher stability, higher design flexibility, and more economical production costs. Nanozymes can be used to mimic natural antioxidant enzymes to treat diseases caused by oxidative stress through reasonable design and modification. Oxidative stress is caused by imbalances in the production and elimination of reactive oxygen species (ROS) and reactive nitrogen species (RNS). This continuous oxidative stress can cause damage to some biomolecules and significant destruction to cell structure and function, leading to many physiological diseases. In this paper, the methods to improve the antioxidant properties of nanozymes were reviewed, and the applications of nanozyme antioxidant in the fields of anti-aging, cell protection, anti-inflammation, wound repair, cancer, traumatic brain injury, and nervous system diseases were introduced. Finally, the future challenges and prospects of nanozyme as an ideal antioxidant were discussed. Oxidative stress ; Antioxidant ; Reactive oxygen species ; Rational design ; Flexibility (engineering) ; Chemistry ; Oxidative damage ; Nanotechnology ; Biochemical engineering ; Biochemistry ; Materials science Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoparticles: synthesis and applications
Authors: Yiwei Li | Yaohong Ma | Éric Lichtfouse | Jin Song | Rui Gong | Jinheng Zhang | Shuo Wang | Leilei Xiao
Source: Journal of Hazardous Materials (article, 2021-07-24)
Field: Materials Science — Polymers and Plastics

In situ electrochemical synthesis of graphene-poly(arginine) composite for p-nitrophenol monitoring Graphene ; Electrochemistry ; In situ ; Composite number ; Nitrophenol ; Materials science ; Chemical engineering ; Chemistry ; Electrode ; Nanotechnology ; Composite material ; Organic chemistry ; Catalysis ; Engineering Conducting polymers and applications ; Electrochemical sensors and biosensors ; Advanced Memory and Neural Computing
Authors: Yazhi Xu | Xudong Wang | Wei Zhang | Lisa Schäfer | Johannes Reindl | Felix vom Bruch | Yuxing Zhou | Valentin Evang | Jiangjing Wang | Volker L. Deringer | E. Ma | Matthias Wuttig | Riccardo Mazzarello
Source: Advanced Materials (article, 2021-01-25)
Field: Materials Science — Materials Chemistry

Materials Screening for Disorder‐Controlled Chalcogenide Crystals for Phase‐Change Memory Applications Tailoring the degree of disorder in chalcogenide phase-change materials (PCMs) plays an essential role in nonvolatile memory devices and neuro-inspired computing. Upon rapid crystallization from the amorphous phase, the flagship Ge-Sb-Te PCMs form metastable rocksalt-like structures with an unconventionally high concentration of vacancies, which results in disordered crystals exhibiting Anderson-insulating transport behavior. Here, ab initio simulations and transport experiments are combined to extend these concepts to the parent compound of Ge-Sb-Te alloys, viz., binary Sb<sub>2</sub> Te<sub>3</sub> , in the metastable rocksalt-type modification. Then a systematic computational screening over a wide range of homologous, binary and ternary chalcogenides, elucidating the critical factors that affect the stability of the rocksalt structure is carried out. The findings vastly expand the family of disorder-controlled main-group chalcogenides toward many more compositions with a tunable bandgap size for demanding phase-change applications, as well as a varying strength of spin-orbit interaction for the exploration of potential topological Anderson insulators. Materials science ; Chalcogenide ; Nanotechnology ; Phase (matter) ; Phase change ; Chalcogenide glass ; Optoelectronics ; Engineering physics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Solid-state spectroscopy and crystallography
Authors: Mingjia Yao | Yuxiang Wang | Xin Li | Ye Sheng | Haiyang Huo | Lili Xi | Jiong Yang | Wenqing Zhang
Source: Scientific Data (article, 2021-09-07)
Field: Materials Science — Materials Chemistry

Materials informatics platform with three dimensional structures, workflow and thermoelectric applications Since the proposal of the "Materials Genome Initiative", several material databases have emerged and advanced many materials fields. In this work, we present the Materials Informatics Platform with Three-Dimensional Structures (MIP-3d). More than 80,000 structural entries, mainly from the inorganic crystal structural database, are included in MIP-3d. Density functional theory calculations are carried out for over 30,000 entries in the database, which contain the relaxed crystal structures, density of states, and band structures. The calculation of the equations of state and sound velocities is performed for over 12,000 entries. Notably, for entries with band gap values larger than 0.3 eV, the band degeneracies for the valence band maxima and the conduction band minima are analysed. The electrical transport properties for approximately 4,400 entries are also calculated and presented in MIP-3d under the constant electron-phonon coupling approximation. The calculations of the band degeneracies and electrical transport properties make MIP-3d a database specifically designed for thermoelectric applications. Workflow ; Thermoelectric effect ; Band gap ; Maxima and minima ; Density functional theory ; Electronic band structure ; Materials science ; Thermoelectric materials ; Crystal structure ; Computer science ; Condensed matter physics ; Database ; Physics ; Crystallography ; Chemistry ; Mathematics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Electronic and Structural Properties of Oxides
Authors: LI Xiu-sheng | Xia Liu | Yingde Li | Deheng Gao | Lianzhen Cao
Source: IEEE Access (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Using Novel Semiconductor Features to Construct Advanced ZnO Nanowires-Based Ultraviolet Photodetectors: A Brief Review Ultraviolet photodetectors have attracted significant research attention in recent years due to their potential applications in civilian and military fields. ZnO nanowires and nanorods have been regarded as the most potential candidates for ultraviolet photodetectors fabrication because of their peculiar characteristics and size effect, which are different from their bulk material. Recently, many novel routes and semiconductor features, such as the surface and interface engineering, the pryo-phototronic effect, the piezo-phototronic effect, the surface plasmon effect and the surface functionalization have been utilized to improve the photoelectric characteristics of ultraviolet photodetectors. Thus, the working mechanism of these effects existing in ZnO nanowires/nanorods-based ultraviolet photodetectors should be investigated in-depth. In this paper, firstly, the hydrothermal method and the chemical vapor deposition method, as two typical synthesis methods of ZnO nanowires are briefly reviewed. Secondly, we focus on reviewing the properties of varied ZnO nanowires/nanorods-based ultraviolet photodetectors constructed using the above mentioned semiconductor features with metal-semiconductor-metal structure, Schottky barrier structure, vertical p-n heterojunction structure and core-shell heterostructure. Furthermore, the most attractive self-powered ultraviolet photodetectors are systematically reviewed. For various ZnO nanowires/nanorods-based ultraviolet photodetectors, we put the emphasis on the working mechanism of semiconductor features to improve the properties of the photodetectors. Finally, we give an outlook on the future development of ZnO nanowires/nanorods-based ultraviolet photodetectors. Photodetector ; Ultraviolet ; Semiconductor ; Optoelectronics ; Nanowire ; Construct (python library) ; Materials science ; Computer science ; Computer network ZnO doping and properties ; Ga2O3 and related materials ; Gas Sensing Nanomaterials and Sensors
Authors: Puja Das | Sayantan Ghosh | Bismita Nayak
Source: Frontiers in Nanotechnology (article, 2021-10-25)
Field: Materials Science — Materials Chemistry

Phyto-fabricated Nanoparticles and Their Anti-biofilm Activity: Progress and Current Status Biofilm is the self-synthesized, mucus-like extracellular polymeric matrix that acts as a key virulence factor in various pathogenic microorganisms, thereby posing a serious threat to human health. It has been estimated that around 80% of hospital-acquired infections are associated with biofilms which are found to be present on both biotic and abiotic surfaces. Antibiotics, the current mainstream treatment strategy for biofilms are often found to be futile in the eradication of these complex structures, and to date, there is no effective therapeutic strategy established against biofilm infections. In this regard, nanotechnology can provide a potential platform for the alleviation of this problem owing to its unique size-dependent properties. Accordingly, various novel strategies are being developed for the synthesis of different types of nanoparticles. Bio-nanotechnology is a division of nanotechnology which is gaining significant attention due to its ability to synthesize nanoparticles of various compositions and sizes using biotic sources. It utilizes the rich biodiversity of various biological components which are biocompatible for the synthesis of nanoparticles. Additionally, the biogenic nanoparticles are eco-friendly, cost-effective, and relatively less toxic when compared to chemically or physically synthesized alternatives. Biogenic synthesis of nanoparticles is a bottom-top methodology in which the nanoparticles are formed due to the presence of biological components (plant extract and microbial enzymes) which act as stabilizing and reducing agents. These biosynthesized nanoparticles exhibit anti-biofilm activity via various mechanisms such as ROS production, inhibiting quorum sensing, inhibiting EPS production, etc. This review will provide an insight into the application of various biogenic sources for nanoparticle synthesis. Furthermore, we have highlighted the potential of phytosynthesized nanoparticles as a promising antibiofilm agent as well as elucidated their antibacterial and antibiofilm mechanism. Biofilm ; Nanotechnology ; Nanoparticle ; Quorum sensing ; Extracellular polymeric substance ; Abiotic component ; Chemistry ; Materials science ; Biology ; Bacteria ; Ecology Nanoparticles: synthesis and applications ; Bacterial biofilms and quorum sensing ; Medicinal Plants and Neuroprotection
Authors: Shoupeng Cao | Hanglong Wu | Imke A. B. Pijpers | Jingxin Shao | Loai K. E. A. Abdelmohsen | David Williams | Jan C. M. van Hest
Source: ACS Nano (article, 2021-10-20)
Field: Materials Science — Materials Chemistry

Cucurbit-Like Polymersomes with Aggregation-Induced Emission Properties Show Enzyme-Mediated Motility Polymersomes that incorporate aggregation-induced emission (AIE) moieties are attractive inherently fluorescent nanoparticles with biomedical application potential for cell/tissue imaging and tracking, as well as phototherapeutics. An intriguing feature that has not been explored yet is their ability to adopt a range of asymmetric morphologies. Structural asymmetry allows nanoparticles to be exploited as active (motile) systems. Here, we present the design and preparation of AIE fluorophore integrated (AIEgenic) cucurbit-shaped polymersome nanomotors with enzyme-powered motility. The cucurbit scaffold was constructed via morphology engineering of biodegradable fluorescent AIE-polymersomes, followed by functionalization with enzymatic machinery via a layer-by-layer (LBL) self-assembly process. Because of the enzyme-mediated decomposition of chemical fuel on the cucurbit-like nanomotor surface, enhanced directed motion was attained, when compared with the spherical counterparts. These cucurbit-shaped biodegradable AIE-nanomotors provide a promising platform for the development of active delivery systems with potential for biomedical applications. Polymersome ; Fluorophore ; Nanotechnology ; Biophysics ; Nanoparticle ; Materials science ; Drug delivery ; Aggregation-induced emission ; Fluorescence ; Motility ; Chemistry ; Polymer ; Amphiphile ; Cell biology ; Biology Luminescence and Fluorescent Materials ; Micro and Nano Robotics ; Supramolecular Self-Assembly in Materials
Authors: Victor Fung | Jiaxin Zhang | Guoxiang Hu | Panchapakesan Ganesh | Bobby G. Sumpter
Source: npj Computational Materials (article, 2021-12-09)
Field: Materials Science — Materials Chemistry

Inverse design of two-dimensional materials with invertible neural networks Abstract The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task. To tackle this problem, we propose an inverse design framework (MatDesINNe) utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property. This approach can be used to generate materials candidates for a designated property, thereby satisfying the highly sought-after goal of inverse design. We then apply this framework to the task of band gap engineering in two-dimensional materials, starting with MoS 2 . Within the design space encompassing six degrees of freedom in applied tensile, compressive and shear strain plus an external electric field, we show the framework can generate novel, high fidelity, and diverse candidates with near-chemical accuracy. We extend this generative capability further to provide insights regarding metal-insulator transition in MoS 2 which are important for memristive neuromorphic applications, among others. This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties. Computer science ; Inverse ; Invertible matrix ; Inverse problem ; Artificial neural network ; Benchmark (surveying) ; Computer engineering ; Theoretical computer science ; Artificial intelligence ; Mathematics 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Cindy Trinh | Dimitrios Meimaroglou | Sandrine Hoppe
Source: Processes (article, 2021-08-20)
Field: Materials Science — Materials Chemistry

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for sensorial analysis. This review is further completed by general guidelines for the selection of an appropriate ML technique given the characteristics of each problem and by a critical discussion of several key issues associated with the development of ML modeling approaches. Accordingly, this paper may serve both the experienced researcher in the field as well as the newcomer. Computer science ; Process (computing) ; Retrosynthetic analysis ; Field (mathematics) ; Artificial intelligence ; Product (mathematics) ; Focus (optics) ; Data science ; Management science ; Machine learning ; Engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Michele Ceriotti | Cecilia Clementi | O. Anatole von Lilienfeld
Source: The Journal of Chemical Physics (article, 2021-04-23)
Field: Materials Science — Materials Chemistry

Machine learning meets chemical physics Over recent years, the use of statistical learning techniques applied to chemical problems has gained substantial momentum. This is particularly apparent in the realm of physical chemistry, where the balance between empiricism and physics-based theory has traditionally been rather in favor of the latter. In this guest Editorial for the special topic issue on "Machine Learning Meets Chemical Physics," a brief rationale is provided, followed by an overview of the topics covered. We conclude by making some general remarks. Empiricism ; Realm ; Epistemology ; Cognitive science ; Data science ; Computer science ; Psychology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yuma Iwasaki | Ryohto Sawada | Eiji Saitoh | Masahiko Ishida
Source: Communications Materials (article, 2021-03-19)
Field: Materials Science — Materials Chemistry

Machine learning autonomous identification of magnetic alloys beyond the Slater-Pauling limit Abstract Discovery of new magnets with high magnetization has always been important in human history because it has given birth to powerful motors and memory devices. Currently, the binary alloy Fe 3 Co 1 exhibits the largest magnetization of any stable alloys explained by the Slater-Pauling rule. A multi-element system is expected to include alloys with magnetization beyond that of Fe 3 Co 1 , but it has been difficult to identify appropriate elements and compositions because of combinatorial explosion. In this work, we identified an alloy with magnetization beyond that of Fe 3 Co 1 by using an autonomous materials search system combining machine learning and ab-initio calculation. After an autonomous and automated exploration in the large material space of multi-element alloys for six weeks, the system unexpectedly indicated that Ir and Pt impurities would enhance the magnetization of FeCo alloys, despite both impurity elements having small magnetic moments. To confirm this experimentally, we synthesized Fe x Co y Ir 1-x-y and Fe x Co y Pt 1-x-y alloys and found that some of them have magnetization beyond that of Fe 3 Co 1 . Magnetization ; Condensed matter physics ; Materials science ; Impurity ; Alloy ; Magnetic moment ; Metallurgy ; Physics ; Magnetic field ; Quantum mechanics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Rahul Bhowmik | Sangwook Sihn | Ruth Pachter | Jonathan P. Vernon
Source: Polymer (article, 2021-02-23)
Field: Materials Science — Materials Chemistry

Prediction of the specific heat of polymers from experimental data and machine learning methods Thermal diffusivity ; Polymer ; Principal component analysis ; Work (physics) ; Materials science ; Thermal ; Experimental data ; Computer science ; Decision tree ; Biological system ; Thermodynamics ; Machine learning ; Artificial intelligence ; Composite material ; Mathematics ; Statistics ; Physics Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Computational Drug Discovery Methods