jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mengyun Wang | June Sang Lee | Samarth Aggarwal | Nikolaos Farmakidis | Yuhan He | Tangsheng Cheng | Harish Bhaskaran
Source: Advanced Science (article, 2023-01-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Varifocal Metalens Using Tunable and Ultralow‐loss Dielectrics The field of flat optics that uses nanostructured, so-called metasurfaces, has seen remarkable progress over the last decade. Chalcogenide phase-change materials (PCMs) offer a promising platform for realizing reconfigurable metasurfaces, as their optical properties can be reversibly tuned. Yet, demonstrations of phase-change metalenses to date have employed material compositions such as Ge<sub>2</sub> Sb<sub>2</sub> Te<sub>5</sub> , which show high absorption in the visible to near-IR wavelengths particularly in their crystalline state, limiting the applicability. Here, by using a low-loss PCM Sb<sub>2</sub> Se<sub>3</sub> , for the first time, active polarization-insensitive phase-change metalenses at near-IR wavelengths with comparable efficiencies in both material states are shown. An active metalens with a tunable focusing intensity of 95% and a focusing efficiency of 23% is demonstrated. A varifocal metalens is then demonstrated with a tunable focal length from 41 to 123 µm with comparable focusing efficiency (5.7% and 3%). The ultralow-loss nature of the material introduces exciting new possibilities for optical communications, multi-depth imaging, beam steering, optical routing, and holography. Materials science ; Chalcogenide ; Optics ; Dielectric ; Optoelectronics ; Holography ; Polarization (electrochemistry) ; Phase change ; Wavelength ; Beam steering ; Limiting ; Beam (structure) ; Engineering physics ; Physics ; Chemistry ; Engineering Metamaterials and Metasurfaces Applications ; Phase-change materials and chalcogenides ; Photonic and Optical Devices
Authors: Kaitlyn T. Keasler | Mary E. Zick | Emily Stacy | Jaehwan Kim | Jung‐Hoon Lee | Lida Aeindartehran | Tomče Runčevski | Phillip J. Milner
Source: Science (article, 2023-09-28)
Field: Chemistry — Inorganic Chemistry

Handling fluorinated gases as solid reagents using metal-organic frameworks Fluorine is an increasingly common substituent in pharmaceuticals and agrochemicals because it improves the bioavailability and metabolic stability of organic molecules. Fluorinated gases represent intuitive building blocks for the late-stage installation of fluorinated groups, but they are generally overlooked because they require the use of specialized equipment. We report a general strategy for handling fluorinated gases as benchtop-stable solid reagents using metal-organic frameworks (MOFs). Gas-MOF reagents are prepared on gram-scale and used to facilitate fluorovinylation and fluoroalkylation reactions. Encapsulation of gas-MOF reagents within wax enables stable storage on the benchtop and controlled release into solution upon sonication, which represents a safer alternative to handling the gas directly. Furthermore, our approach enables high-throughput reaction development with these gases. Reagent ; Metal-organic framework ; Chemistry ; Fluorine ; Organic molecules ; Nanotechnology ; Organic chemistry ; Materials science ; Molecule Metal-Organic Frameworks: Synthesis and Applications ; Fluorine in Organic Chemistry ; Luminescence and Fluorescent Materials
Authors: Wenpeng Ma | Jan‐Lukas Kirchhoff | Carsten Strohmann | Bastian Grabe | Charles C. J. Loh
Source: Journal of the American Chemical Society (article, 2023-11-30)
Field: Chemistry — Organic Chemistry

Cooperative Bifurcated Chalcogen Bonding and Hydrogen Bonding as Stereocontrolling Elements for Selective Strain-Release Septanosylation The exploitation of noncovalent interactions (NCIs) is emerging as a vital handle in tackling broad stereoselectivity challenges in synthesis. In particular, there has been significant recent interest in the harnessing of unconventional NCIs to surmount difficult selectivity challenges in glycosylations. Herein, we disclose the exploitation of an unconventional bifurcated chalcogen bonding and hydrogen bonding (HB) network, which paves the way for a robust catalytic strategy into biologically useful seven-membered ring sugars. Through <sup>13</sup>C nuclear magnetic resonance (NMR) in situ monitoring, NMR titration experiments, and density functional theory (DFT) modeling, we propose a remarkable contemporaneous activation of multiple functional groups consisting of a bifurcated chalcogen bonding mechanism working hand-in-hand with HB activation. Significantly, the ester moiety installed on the glycosyl donor is critical in the establishment of the postulated ternary complex for stereocontrol. Through the <sup>13</sup>C kinetic isotopic effect and kinetic studies, our data corroborated that a dissociative S<sub>N</sub>i-type mechanism forms the stereocontrolling basis for the excellent α-selectivity. Chemistry ; Chalcogen ; Hydrogen bond ; Moiety ; Density functional theory ; Non-covalent interactions ; Intermolecular force ; Selectivity ; Computational chemistry ; Ternary complex ; Ternary operation ; Combinatorial chemistry ; Stereochemistry ; Catalysis ; Molecule ; Crystallography ; Organic chemistry Carbohydrate Chemistry and Synthesis ; Microbial Natural Products and Biosynthesis ; Chemical Synthesis and Analysis
Authors: Jun Luo | Xiao Luo | Yonghai Gan | Xiaoming Xu | Bin Xu | Zhuang Liu | Chengcheng Ding | Yibin Cui | Cheng Sun
Source: Nanomaterials (review, 2023-07-28)
Field: Chemistry — Inorganic Chemistry

Advantages of Bimetallic Organic Frameworks in the Adsorption, Catalysis and Detection for Water Contaminants The binary metal organic framework (MOF) is composed of two heterometallic ions bonded to an organic ligand. Compared with monometallic MOFs, bimetallic MOFs have greatly improved in terms of structure, porosity, active site, adsorption, selectivity, and stability, which has attracted wide attention. At present, many effective strategies have been designed for the synthesis of bimetallic MOF-based nanomaterials with specific morphology, structure, and function. The results show that bimetallic MOF-based nanocomposites could achieve multiple synergistic effects, which will greatly improve their research in the fields of adsorption, catalysis, energy storage, sensing, and so on. In this review, the main preparation methods of bimetallic MOFs-based materials are summarized, with emphasis on their applications in adsorption, catalysis, and detection of target pollutants in water environments, and perspectives on the future development of bimetallic MOFs-based nanomaterials in the field of water are presented. Bimetallic strip ; Nanomaterials ; Metal-organic framework ; Adsorption ; Materials science ; Catalysis ; Nanotechnology ; Nanocomposite ; Porosity ; Synergistic catalysis ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Covalent Organic Framework Applications
Authors: Sharanya Paramshetti | Mohit Angolkar | Adel Al Fatease | Sultan M. Alshahrani | Umme Hani | Ankitha Garg | Gundawar Ravi | Riyaz Ali M. Osmani
Source: Pharmaceutics (review, 2023-04-10)
Field: Materials Science — Polymers and Plastics

Revolutionizing Drug Delivery and Therapeutics: The Biomedical Applications of Conductive Polymers and Composites-Based Systems The first conductive polymers (CPs) were developed during the 1970s as a unique class of organic substances with properties that are electrically and optically comparable to those of inorganic semiconductors and metals while also exhibiting the desirable traits of conventional polymers. CPs have become a subject of intensive research due to their exceptional qualities, such as high mechanical and optical properties, tunable electrical characteristics, ease of synthesis and fabrication, and higher environmental stability than traditional inorganic materials. Although conducting polymers have several limitations in their pure state, coupling with other materials helps overcome these drawbacks. Owing to the fact that various types of tissues are responsive to stimuli and electrical fields has made these smart biomaterials attractive for a range of medical and biological applications. For various applications, including the delivery of drugs, biosensors, biomedical implants, and tissue engineering, electrical CPs and composites have attracted significant interest in both research and industry. These bimodalities can be programmed to respond to both internal and external stimuli. Additionally, these smart biomaterials have the ability to deliver drugs in various concentrations and at an extensive range. This review briefly discusses the commonly used CPs, composites, and their synthesis processes. Further highlights the importance of these materials in drug delivery along with their applicability in various delivery systems. Nanotechnology ; Drug delivery ; Materials science ; Conductive polymer ; Polymer ; Smart material ; Electrically conductive ; Composite material Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Electrochemical sensors and biosensors
Authors: Lingyun Zhang | Li Zhang | Rongbo Xie | Yibo Ni | Xiaoyu Wu | Yuanmu Yang | Fei Xing | Xiaoguang Zhao | Zheng You
Source: Advanced Science (article, 2023-06-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Highly Tunable Cascaded Metasurfaces for Continuous Two‐Dimensional Beam Steering Cascaded metasurfaces can exhibit powerful dynamic light manipulation by mechanically tuning the far-field interactions in the layers. However, in most current designs, the metasurfaces are separated by gaps smaller than a wavelength to form a total phase profile, representing the direct accumulation of the phase profiles of each layer. Such small gap sizes may not only conflict with the far-field conditions but also pose great difficulties for practical implementations. To overcome this limitation, a design paradigm taking advantage of a ray-tracing scheme that allows the cascaded metasurfaces to operate optimally at easily achievable gap sizes is proposed. Enabled by the relative lateral translation of two cascaded metasurfaces, a continuous two-dimensional (2D) beam-steering device for 1064 nm light is designed as a proof of concept. Simulation results demonstrate tuning ranges of ±45° for biaxial deflection angles within ±3.5 mm biaxial translations, while keeping the divergence of deflected light less than 0.007°. The experimental results agree well with theoretical predictions, and a uniform optical efficiency is observed. The generializeddesign paradigm can pave a way towards myriad tunable cascaded metasurface devices for various applications, including but not limited to light detection and ranging (LiDAR) and free space optical communication. Beam steering ; Optics ; Beam divergence ; Deflection (physics) ; Computer science ; Translation (biology) ; Wavelength ; Materials science ; Beam (structure) ; Optoelectronics ; Physics ; Laser beams ; Laser ; Beam diameter Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Optical Wireless Communication Technologies
Authors: Chen Li | Kun Zheng
Source: InfoMat (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Methods, progresses, and opportunities of materials informatics Abstract As an implementation tool of data intensive scientific research methods, machine learning (ML) can effectively shorten the research and development (R&amp;D) cycle of new materials by half or even more. ML shows great potential in the combination with other scientific research technologies, especially in the processing and classification of large amounts of material data from theoretical calculation and experimental characterization. It is very important to systematically understand the research ideas of material informatics to accelerate the exploration of new materials. Here, we provide a comprehensive introduction to the most commonly used ML modeling methods in material informatics with classic cases. Then, we review the latest progresses of prediction models, which focus on new processing–structure–properties–performance (PSPP) relationships in some popular material systems, such as perovskites, catalysts, alloys, two‐dimensional materials, and polymers. In addition, we summarize the recent pioneering researches in innovation of material research technology, such as inverse design, ML interatomic potentials, and microtopography characterization assistance, as new research directions of material informatics. Finally, we comprehensively provide the most significant challenges and outlooks related to the future innovation and development in the field of material informatics. This review provides a critical and concise appraisal for the applications of material informatics, and a systematic and coherent guidance for material scientists to choose modeling methods based on required materials and technologies. Materials informatics ; Informatics ; Characterization (materials science) ; Computer science ; Management science ; Data science ; Field (mathematics) ; Engineering informatics ; Health informatics ; Systems engineering ; Nanotechnology ; Engineering ; Materials science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Tongtong Wang | Zhe Chen | Weibo Gong | Fei Xu | Xin Song | Xin He | Maohong Fan
Source: ACS Omega (review, 2023-06-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrospun Carbon Nanofibers and Their Applications in Several Areas Carbon nanofibers (CNFs) have a broad spectrum of applications, including sensor manufacturing, electrochemical catalysis, and energy storage. Among different manufacturing methods, electrospinning, due to its simplicity and efficiency, has emerged as one of the most powerful commercial large-scale production techniques. Numerous researchers have been attracted to improving the performance of CNFs and exploring new potential applications. This paper first discusses the working theory of manufacturing electrospun CNFs. Next, the current efforts in upgrading the properties of CNFs, such as pore architecture, anisotropy, electrochemistry, and hydrophilicity, are discussed. The corresponding applications due to the superior performances of CNFs are subsequently elaborated. Finally, the future development of CNFs is discussed. Carbon nanofiber ; Electrospinning ; Nanotechnology ; Materials science ; Nanofiber ; Simplicity ; Carbon nanotube ; Composite material ; Polymer Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Robert Ahmed | Christna Ahler
Source: Journal of Computational Intelligence in Materials Science (review, 2023-06-30)
Field: Materials Science — Materials Chemistry

A Review on Background and Applications of Machine Learning in Materials Research In recent decades, Artificial Intelligence (AI) has garnered considerable interest owing to its potential to facilitate greater levels of automation and speed up overall output. There has been a significant increase in the quantity of training data sets, processing capacity, and deep learning techniques that are all favorable to the widespread use of AI in fields like material science. Attempting to learn anything new by trial and error is a slow and ineffective approach. Therefore, AI, and particularly machine learning, may hasten the process by gleaning rules from information and constructing predictive models. In traditional computational chemistry, human scientists give the formulae, and the computer just crunches the numbers. In this article, we take a look back at the ways in which artificial intelligence has been put to use in the creation of new materials, such as in their design, performance prediction, and synthesis. In these programs, an emphasis is placed on the specifics of AI methodology implementation and the benefits gained over more traditional approaches. The last section elaborates, from both an algorithmic and an infrastructural perspective, where AI is headed in the future. Artificial intelligence ; Computer science ; Process (computing) ; Automation ; Machine learning ; Perspective (graphical) ; Deep learning ; Data science ; Engineering Machine Learning in Materials Science
Authors: Jingxi Li | Tianyi Gan | Yifan Zhao | Bijie Bai | Che‐Yung Shen | Songyu Sun | Mona Jarrahi | Aydogan Özcan
Source: Science Advances (article, 2023-04-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unidirectional imaging using deep learning–designed materials A unidirectional imager would only permit image formation along one direction, from an input field-of-view (FOV) A to an output FOV B, and in the reverse path, B → A, the image formation would be blocked. We report the first demonstration of unidirectional imagers, presenting polarization-insensitive and broadband unidirectional imaging based on successive diffractive layers that are linear and isotropic. After their deep learning-based training, the resulting diffractive layers are fabricated to form a unidirectional imager. Although trained using monochromatic illumination, the diffractive unidirectional imager maintains its functionality over a large spectral band and works under broadband illumination. We experimentally validated this unidirectional imager using terahertz radiation, well matching our numerical results. We also created a wavelength-selective unidirectional imager, where two unidirectional imaging operations, in reverse directions, are multiplexed through different illumination wavelengths. Diffractive unidirectional imaging using structured materials will have numerous applications in, e.g., security, defense, telecommunications, and privacy protection. Monochromatic color ; Optics ; Broadband ; Terahertz radiation ; Computer science ; Multiplexing ; Wavelength ; Polarization (electrochemistry) ; Physics ; Optoelectronics ; Telecommunications Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Photonic and Optical Devices
Authors: Jeff J. Guo | Bojana Ranković | Philippe Schwaller
Source: CHIMIA International Journal for Chemistry (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

Bayesian Optimization for Chemical Reactions Reaction optimization is challenging and traditionally delegated to domain experts who iteratively propose increasingly optimal experiments. Problematically, the reaction landscape is complex and often requires hundreds of experiments to reach convergence, representing an enormous resource sink. Bayesian optimization (BO) is an optimization algorithm that recommends the next experiment based on previous observations and has recently gained considerable interest in the general chemistry community. The application of BO for chemical reactions has been demonstrated to increase efficiency in optimization campaigns and can recommend favorable reaction conditions amidst many possibilities. Moreover, its ability to jointly optimize desired objectives such as yield and stereoselectivity makes it an attractive alternative or at least complementary to domain expert-guided optimization. With the democratization of BO software, the barrier of entry to applying BO for chemical reactions has drastically lowered. The intersection between the paradigms will see advancements at an ever-rapid pace. In this review, we discuss how chemical reactions can be transformed into machine-readable formats which can be learned by machine learning (ML) models. We present a foundation for BO and how it has already been applied to optimize chemical reaction outcomes. The important message we convey is that realizing the full potential of ML-augmented reaction optimization will require close collaboration between experimentalists and computational scientists. Bayesian optimization ; Computer science ; Pace ; Intersection (aeronautics) ; Optimization problem ; Biochemical engineering ; Machine learning ; Mathematical optimization ; Algorithm ; Mathematics ; Engineering Machine Learning in Materials Science ; Machine Learning and Data Classification ; Machine Learning and Algorithms
Authors: Erfan Azqadan | Hamid Jahed | Arash Arami
Source: Acta Materialia (article, 2023-10-05)
Field: Materials Science — Materials Chemistry

Predictive microstructure image generation using denoising diffusion probabilistic models The rapid progress in artificial intelligence (AI) based image generation led to groundbreaking achievements, like OpenAI's DALL-E 2, showcasing state-of-the-art generative models in deep learning and computer vision. Recently, the Denoising Diffusion Probabilistic Model (DDPM) has emerged as a strong contender, excelling in generating high-resolution images with complex features similar to those found in real-world images. In this study, we investigate DDPM's potential as both generator and predictor of scanning electron microscope (SEM) images, encompassing both known and unseen microstructural conditions. To rigorously evaluate DDPM, we curated a comprehensive dataset comprising 27 distinct cast-forged AZ80 magnesium alloy components with varied process parameters and microstructure features. Some conditions were held back during training to test DDPM's predictive abilities for unseen scenarios. Our study demonstrates the model's remarkable capacity to capture the inherent physical relationships between process parameters and microstructure features. We scrutinize the synthesized images alongside real-world SEM counterparts, undertaking a comprehensive analysis of various morphological properties. Remarkably, the results show the model's performance, with an average error of 6.36% ± 0.42 for measured microstructural properties in seen conditions and an equally impressive 6.67% ± 0.85 for unseen conditions. This study envisions a transformative shift in materials science, as advanced AI predictive models offer new potential to streamline the laborious process of microstructure image generation. Microstructure ; Materials science ; Artificial intelligence ; Probabilistic logic ; Diffusion ; Process (computing) ; Scanning electron microscope ; Machine learning ; Computer science ; Pattern recognition (psychology) ; Metallurgy ; Physics ; Thermodynamics Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Aluminum Alloy Microstructure Properties