jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jamie M. Cameron | G. Guillemot | Theodor Galambos | Sharad S. Amin | Elizabeth Hampson | Kevin Mall Haidaraly | Graham N. Newton | Guillaume Izzet
Source: Chemical Society Reviews (review, 2021-12-10)
Field: Materials Science — Materials Chemistry

Supramolecular assemblies of organo-functionalised hybrid polyoxometalates: from functional building blocks to hierarchical nanomaterials This review provides a comprehensive overview of recent advances in the supramolecular organisation and hierarchical self-assembly of organo-functionalised hybrid polyoxometalates (hereafter referred to as hybrid POMs), and their emerging role as multi-functional building blocks in the construction of new nanomaterials. Polyoxometalates have long been studied as a fascinating outgrowth of traditional metal-oxide chemistry, where the unusual position they occupy between individual metal oxoanions and solid-state bulk oxides imbues them with a range of attractive properties (<i>e.g.</i> solubility, high structural modularity and tuneable properties/reactivity). Specifically, the capacity for POMs to be covalently coupled to an effectively limitless range of organic moieties has opened exciting new avenues in their rational design, while the combination of distinct organic and inorganic components facilitates the formation of complex molecular architectures and the emergence of new, unique functionalities. Here, we present a detailed discussion of the design opportunities afforded by hybrid POMs, where fine control over their size, topology and their covalent and non-covalent interactions with a range of other species and/or substrates makes them ideal building blocks in the assembly of a broad range of supramolecular hybrid nanomaterials. We review both direct self-assembly approaches (encompassing both solution and solid-state approaches) and the non-covalent interactions of hybrid POMs with a range of suitable substrates (including cavitands, carbon nanotubes and biological systems), while giving key consideration to the underlying driving forces in each case. Ultimately, this review aims to demonstrate the enormous potential that the rational assembly of hybrid POM clusters shows for the development of next-generation nanomaterials with applications in areas as diverse as catalysis, energy-storage and molecular biology, while providing our perspective on where the next major developments in the field may emerge. Supramolecular chemistry ; Nanotechnology ; Nanomaterials ; Hybrid material ; Rational design ; Polyoxometalate ; Covalent bond ; Materials science ; Modularity (biology) ; Design elements and principles ; Carbon nanotube ; Chemistry ; Computer science ; Molecule ; Organic chemistry ; Catalysis Polyoxometalates: Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications
Authors: F. Pelin Kinik | Andres Ortega‐Guerrero | Daniele Ongari | Christopher P. Ireland | Berend Smit
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Pyrene-based metal organic frameworks: from synthesis to applications Pyrene is one of the most widely investigated aromatic hydrocarbons given to its unique optical and electronic properties. Hence, pyrene-based ligands have been attractive for the synthesis of metal-organic frameworks (MOFs) in the last few years. In this review, we will focus on the most important characteristics of pyrene, in addition to the development and synthesis of pyrene-based molecules as bridging ligands to be used in MOF structures. We will summarize the synthesis attempts, as well as the post-synthetic modifications of pyrene-based MOFs by the incorporation of metals or ligands in the structure. The discussion of promising results of such MOFs in several applications; including luminescence, photocatalysis, adsorption and separation, heterogeneous catalysis, electrochemical applications and bio-medical applications will be highlighted. Finally, some insights and future prospects will be given based on the studies discussed in the review. This review will pave the way for the researchers in the field for the design and development of novel pyrene-based structures and their utilization for different applications. Pyrene ; Photocatalysis ; Metal-organic framework ; Adsorption ; Catalysis ; Electrochemistry ; Nanotechnology ; Chemistry ; Luminescence ; Materials science ; Combinatorial chemistry ; Environmental chemistry ; Organic chemistry ; Electrode ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gas Sensing Nanomaterials and Sensors
Authors: Ghanshyam Pilania
Source: Computational Materials Science (article, 2021-03-10)
Field: Materials Science — Materials Chemistry

Machine learning in materials science: From explainable predictions to autonomous design Computer science ; Field (mathematics) ; Data science ; Artificial intelligence ; Big data ; Machine learning ; Domain (mathematical analysis) ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Evan P. Stater | Ali Yasin Sonay | Cassidy Hart | Jan Grimm
Source: Nature Nanotechnology (review, 2021-11-01)
Field: Materials Science — Materials Chemistry

The ancillary effects of nanoparticles and their implications for nanomedicine Nanomedicine ; Nanotechnology ; Nanoparticle ; Computer science ; Materials science Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics
Authors: Melanie Platzer | Sandra Kiese | Thomas Herfellner | Ute Schweiggert‐Weisz | Oliver Miesbauer | Peter Eisner
Source: Molecules (article, 2021-02-25)
Field: Chemistry — Organic Chemistry

Common Trends and Differences in Antioxidant Activity Analysis of Phenolic Substances Using Single Electron Transfer Based Assays Numerous assays were developed to measure the antioxidant activity, but each has limitations and the results obtained by different methods are not always comparable. Popular examples are the DPPH and ABTS assay. Our aim was to study similarities and differences of these two assay regarding the measured antioxidant potentials of 24 phenolic compounds using the same measurement and evaluation methods. This should allow conclusions to be drawn as to whether one of the assays is more suitable for measuring specific subgroups like phenolic acids, flavonols, flavanones, dihydrochalcones or flavanols. The assays showed common trends for the mean values of most of the subgroups. Some dihydrochalcones and flavanones did not react with the DPPH radical in contrast to the ABTS radical, leading to significant differences. Therefore, to determine the antioxidant potential of dihydrochalcone or flavanone-rich extracts, the ABTS assay should be preferred. We found that the results of the flavonoids in the DPPH assay were dependent on the Bors criteria, whereas the structure–activity relationship in the ABTS assay was not clear. For the phenolic acids, the results in the ABTS assay were only high for pyrogallol structures, while the DPPH assay was mainly determined by the number of OH groups. ABTS ; DPPH ; Flavonols ; Chemistry ; Antioxidant ; Flavanone ; Pyrogallol ; Chromatography ; Food science ; Flavonoid ; Organic chemistry Free Radicals and Antioxidants ; Antioxidant Activity and Oxidative Stress ; Phytochemicals and Antioxidant Activities
Authors: Yury Lysogorskiy | Cas van der Oord | Anton Bochkarev | Sarath Menon | Matteo Rinaldi | Thomas Hammerschmidt | Matous Mrovec | Aidan P. Thompson | Gábor Cśanyi | Christoph Ortner | Ralf Drautz
Source: npj Computational Materials (article, 2021-06-28)
Field: Materials Science — Materials Chemistry

Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon Abstract The atomic cluster expansion is a general polynomial expansion of the atomic energy in multi-atom basis functions. Here we implement the atomic cluster expansion in the performant C++ code that is suitable for use in large-scale atomistic simulations. We briefly review the atomic cluster expansion and give detailed expressions for energies and forces as well as efficient algorithms for their evaluation. We demonstrate that the atomic cluster expansion as implemented in shifts a previously established Pareto front for machine learning interatomic potentials toward faster and more accurate calculations. Moreover, general purpose parameterizations are presented for copper and silicon and evaluated in detail. We show that the Cu and Si potentials significantly improve on the best available potentials for highly accurate large-scale atomistic simulations. Cluster expansion ; Cluster (spacecraft) ; Atomic units ; Silicon ; Coupled cluster ; Copper ; Atom (system on chip) ; Computer science ; Computational science ; Scale (ratio) ; Statistical physics ; Atomic physics ; Materials science ; Physics ; Parallel computing ; Quantum mechanics ; Optoelectronics ; Molecule ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Victor Fung | Guoxiang Hu | Panchapakesan Ganesh | Bobby G. Sumpter
Source: Nature Communications (article, 2021-01-04)
Field: Materials Science — Materials Chemistry

Machine learned features from density of states for accurate adsorption energy prediction Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descriptors or features, in lieu of DFT, to accurately predict catalytic properties, such as adsorption energies. Here, we demonstrate an approach to predict energies using a convolutional neural network-based machine learning model to automatically obtain key features from the electronic density of states (DOS). The model, DOSnet, is evaluated for a diverse set of adsorbates and surfaces, yielding a mean absolute error on the order of 0.1 eV. In addition, DOSnet can provide physically meaningful predictions and insights by predicting responses to external perturbations to the electronic structure without additional DFT calculations, paving the way for the accelerated discovery of materials and catalysts by exploration of the electronic space. Adsorption ; Energy (signal processing) ; Computer science ; Density functional theory ; Chemistry ; Physics ; Computational chemistry ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Zhihang Wang | Paul Erhart | Tao Li | Zhaoyang Zhang | Diego Sampedro | Zhiyu Hu | Hermann A. Wegner | Olaf Brummel | Jörg Libuda | Mogens Brøndsted Nielsen | Kasper Moth‐Poulsen
Source: Joule (article, 2021-11-22)
Field: Materials Science — Materials Chemistry

Storing energy with molecular photoisomers Some molecular photoisomers can be isomerized to a metastable high-energy state by exposure to light. These molecules can then be thermally or catalytically converted back to their initial state, releasing heat in the process. Such a reversible photochemical process has been considered for developing molecular solar thermal (MOST) systems. In this review, we introduce the concept, criteria, and state-of-the-art of MOST systems, with an emphasis on the three most promising molecular systems: norbornadiene/quadricyclane, E/Z-azobenzene, and dihydroazulene/vinylheptafulvene. After discussing the fundamental working principles, we focus on molecular design strategies for improving solar energy storage performance, remaining challenges, and potential focus areas. Finally, we summarize the current molecular incorporation into functional devices and conclude with a perspective on challenges and future directions. Azobenzene ; Quadricyclane ; Norbornadiene ; Nanotechnology ; Molecular switch ; Metastability ; Molecular machine ; Materials science ; Focus (optics) ; Molecule ; Chemistry ; Physics ; Organic chemistry ; Optics Photochromic and Fluorescence Chemistry ; TiO2 Photocatalysis and Solar Cells ; Transition Metal Oxide Nanomaterials
Authors: Anthony Wang | Steven K. Kauwe | Ryan Murdock | Taylor D. Sparks
Source: npj Computational Materials (article, 2021-05-28)
Field: Materials Science — Materials Chemistry

Compositionally restricted attention-based network for materials property predictions Abstract In this paper, we demonstrate an application of the Transformer self-attention mechanism in the context of materials science. Our network, the Compositionally Restricted Attention-Based network (), explores the area of structure-agnostic materials property predictions when only a chemical formula is provided. Our results show that ’s performance matches or exceeds current best-practice methods on nearly all of 28 total benchmark datasets. We also demonstrate how ’s architecture lends itself towards model interpretability by showing different visualization approaches that are made possible by its design. We feel confident that and its attention-based framework will be of keen interest to future materials informatics researchers. Interpretability ; Computer science ; Property (philosophy) ; Benchmark (surveying) ; Visualization ; Data science ; Context (archaeology) ; Informatics ; Transformer ; Network architecture ; Artificial intelligence ; Data mining ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Muhammad Ali Tahir | Nicoleta Elena Dina | Hanyun Cheng | Ventsislav K. Valev | Liwu Zhang
Source: Nanoscale (review, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis In recent years, bioanalytical surface-enhanced Raman spectroscopy (SERS) has blossomed into a fast-growing research area. Owing to its high sensitivity and outstanding multiplexing ability, SERS is an effective analytical technique that has excellent potential in bioanalysis and diagnosis, as demonstrated by its increasing applications in vivo. SERS allows the rapid detection of molecular species based on direct and indirect strategies. Because it benefits from the tunable surface properties of nanostructures, it finds a broad range of applications with clinical relevance, such as biological sensing, drug delivery and live cell imaging assays. Of particular interest are early-stage-cancer detection and the fast detection of pathogens. Here, we present a comprehensive survey of SERS-based assays, from basic considerations to bioanalytical applications. Our main focus is on SERS-based pathogen detection methods as point-of-care solutions for early bacterial infection detection and chronic disease diagnosis. Additionally, various promising in vivo applications of SERS are surveyed. Furthermore, we provide a brief outlook of recent endeavours and we discuss future prospects and limitations for SERS, as a reliable approach for rapid and sensitive bioanalysis and diagnosis. Bioanalysis ; Nanotechnology ; Surface-enhanced Raman spectroscopy ; Materials science ; Raman spectroscopy ; Raman scattering ; Physics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Cheng‐Wei Qiu | Tan Zhang | Guangwei Hu | Yuri S. Kivshar
Source: Nano Letters (review, 2021-06-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quo Vadis, Metasurfaces? The full manipulation of intrinsic properties of electromagnetic waves has become the central target in various modern optical technologies. Optical metasurfaces have been suggested for a complete control of light-matter interaction with subwavelength structures, and they have been explored widely in the past decade for creating next-generation multifunctional flat-optics devices. The current studies of metasurfaces have reached a mature stage where common materials, basic optical physics, and conventional engineering tools have been explored extensively for various applications such as light bending, metalenses, metaholograms, and many others. A natural question is where the future research on metasurfaces will be going: Quo vadis, metasurfaces? In this Mini Review, we provide perspectives on the future developments of optical metasurfaces. Specifically, we highlight recent progresses on hybrid metasurfaces employing low-dimensional materials and discuss biomedical, computational, and quantum applications of metasurfaces, followed by discussions of challenges and foreseeing the future of metasurface physics and engineering. Nanotechnology ; Metamaterial ; Optical materials ; Engineering physics ; Computer science ; Physics ; Materials science ; Optics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Energy Harvesting in Wireless Networks
Authors: Mohsen Yoosefzadeh-Najafabadi | Hugh J. Earl | Dan Tulpan | John Sulik | Milad Eskandari
Source: Frontiers in Plant Science (article, 2021-01-12)
Field: Chemistry — Analytical Chemistry

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean Recent substantial advances in high-throughput field phenotyping have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits at early growth stages. Nevertheless, the implementation of large datasets generated by high-throughput phenotyping tools such as hyperspectral reflectance in cultivar development programs is still challenging due to the essential need for intensive knowledge in computational and statistical analyses. In this study, the robustness of three common machine learning (ML) algorithms, multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were evaluated for predicting soybean (<i>Glycine max</i>) seed yield using hyperspectral reflectance. For this aim, the hyperspectral reflectance data for the whole spectra ranged from 395 to 1005 nm, which were collected at the R4 and R5 growth stages on 250 soybean genotypes grown in four environments. The recursive feature elimination (RFE) approach was performed to reduce the dimensionality of the hyperspectral reflectance data and select variables with the largest importance values. The results indicated that R5 is more informative stage for measuring hyperspectral reflectance to predict seed yields. The 395 nm reflectance band was also identified as the high ranked band in predicting the soybean seed yield. By considering either full or selected variables as the input variables, the ML algorithms were evaluated individually and combined-version using the ensemble-stacking (E-S) method to predict the soybean yield. The RF algorithm had the highest performance with a value of 84% yield classification accuracy among all the individual tested algorithms. Therefore, by selecting RF as the metaClassifier for E-S method, the prediction accuracy increased to 0.93, using all variables, and 0.87, using selected variables showing the success of using E-S as one of the ensemble techniques. This study demonstrated that soybean breeders could implement E-S algorithm using either the full or selected spectra reflectance to select the high-yielding soybean genotypes, among a large number of genotypes, at early growth stages. Hyperspectral imaging ; Random forest ; Support vector machine ; Reflectivity ; Artificial intelligence ; Dimensionality reduction ; Machine learning ; Feature selection ; Computer science ; Multilayer perceptron ; Robustness (evolution) ; Algorithm ; Remote sensing ; Mathematics ; Pattern recognition (psychology) ; Artificial neural network ; Biology Spectroscopy and Chemometric Analyses ; Soybean genetics and cultivation ; Genetics and Plant Breeding
Authors: Sree Satya Bharati Moram | S. Venugopal Rao
Source: Opto-Electronic Advances (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Flexible SERS substrates for hazardous materials detection: recent advances This article reviews the most recent advances in the development of flexible substrates used as surface-enhanced Raman scattering (SERS) platforms for detecting several hazardous materials (e.g., explosives, pesticides, drugs, and dyes). Different flexible platforms such as papers/filter papers, fabrics, polymer nanofibers, and cellulose fibers have been investigated over the last few years and their SERS efficacies have been evaluated. We start with an introduction of the importance of hazardous materials trace detection followed by a summary of different SERS methodologies with particular attention on flexible substrates and their advantages over the nanostructures and nanoparticle-based solid/hybrid substrates. The potential of flexible SERS substrates, in conjunction with a simple portable Raman spectrometer, is the power to enable practical/on-field/point of interest applications primarily because of their low-cost and easy sampling. Nanotechnology ; Hazardous waste ; Materials science ; Nanofiber ; Raman scattering ; Raman spectroscopy ; Engineering ; Optics ; Waste management Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Flore Mekki‐Berrada | Zekun Ren | Tan Huang | Wai Kuan Wong | Zheng Fang | Jiaxun Xie | Siyu Tian | J. Senthilnath | Zackaria Mahfoud | Daniil Bash | Kedar Hippalgaonkar | Saif A. Khan | Tonio Buonassisi | Qianxiao Li | Xiaonan Wang
Source: npj Computational Materials (article, 2021-04-20)
Field: Materials Science — Materials Chemistry

Two-step machine learning enables optimized nanoparticle synthesis Abstract In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum. Bayesian optimization ; Computer science ; Absorbance ; Artificial neural network ; Gaussian process ; Nanomaterials ; Throughput ; Deep learning ; Process (computing) ; Microfluidics ; Kriging ; Artificial intelligence ; Nanoparticle ; Biological system ; Machine learning ; Gaussian ; Nanotechnology ; Materials science ; Chemistry ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Neng Li | Jiahe Peng | Wee‐Jun Ong | Tingting Ma | Arramel Arramel | Peng Zhang | Jizhou Jiang | Xiaofang Yuan | Chuanfang Zhang
Source: Matter (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

MXenes: An Emerging Platform for Wearable Electronics and Looking Beyond MXenes ; Electronics ; Nanotechnology ; Wearable computer ; Wearable technology ; Supercapacitor ; Triboelectric effect ; Materials science ; Computer science ; Engineering ; Electrical engineering ; Embedded system ; Capacitance ; Chemistry MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Memory and Neural Computing