jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lei Feng | Susu Zhu | Fei Liu | Yong He | Yidan Bao | Chu Zhang
Source: Plant Methods (review, 2019-08-08)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging for seed quality and safety inspection: a review Hyperspectral imaging has attracted great attention as a non-destructive and fast method for seed quality and safety assessment in recent years. The capability of this technique for classification and grading, viability and vigor detection, damage (defect and fungus) detection, cleanness detection and seed composition determination is illustrated by presentation of applications in quality and safety determination of seed in this review. The summary of hyperspectral imaging technology for seed quality and safety inspection for each category is also presented, including the analyzed spectral range, sample varieties, sample status, sample numbers, features (spectral features, image features, feature extraction methods), signal mode and data analysis strategies. The successful application of hyperspectral imaging in seed quality and safety inspection proves that many routine seed inspection tasks can be facilitated with hyperspectral imaging. Hyperspectral imaging ; Grading (engineering) ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Sample (material) ; Visual inspection ; Image quality ; Computer vision ; Engineering ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Leaf Properties and Growth Measurement
Authors: Kuldeep Mahato | Sahil Nagpal | Mahero Ayesha Shah | Ananya Srivastava | Pawan Kumar Maurya | Shounak Roy | Amit Jaiswal | Renu Singh | Pranjal Chandra
Source: 3 Biotech (review, 2019-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gold nanoparticle surface engineering strategies and their applications in biomedicine and diagnostics Nanotechnology ; Surface modification ; Materials science ; Drug delivery ; Colloidal gold ; Biosensor ; Nanoparticle ; Surface-enhanced Raman spectroscopy ; Polymer ; Chemistry ; Raman spectroscopy ; Raman scattering Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Kai Yang | Xinyi Xu | Benjamin Yang | B. Cook | Herbert Ramos | N. M. Anoop Krishnan | Morten M. Smedskjær | Christian G. Hoover | Mathieu Bauchy
Source: Scientific Reports (article, 2019-06-19)
Field: Materials Science — Ceramics and Composites

Predicting the Young’s Modulus of Silicate Glasses using High-Throughput Molecular Dynamics Simulations and Machine Learning The application of machine learning to predict materials' properties usually requires a large number of consistent data for training. However, experimental datasets of high quality are not always available or self-consistent. Here, as an alternative route, we combine machine learning with high-throughput molecular dynamics simulations to predict the Young's modulus of silicate glasses. We demonstrate that this combined approach offers good and reliable predictions over the entire compositional domain. By comparing the performances of select machine learning algorithms, we discuss the nature of the balance between accuracy, simplicity, and interpretability in machine learning. Silicate ; Throughput ; Silicate glass ; Dynamics (music) ; Computer science ; Molecular dynamics ; Modulus ; Machine learning ; Artificial intelligence ; Materials science ; Composite material ; Chemistry ; Physics ; Engineering ; Chemical engineering ; Computational chemistry ; Telecommunications Glass properties and applications ; Material Dynamics and Properties ; High-pressure geophysics and materials
Authors: James Lazarovits | Shrey Sindhwani | Anthony J. Tavares | Yuwei Zhang | Fayi Song | Julie Audet | Jonathan R. Krieger | Abdullah M. Syed | Benjamin Stordy | Warren C. W. Chan
Source: ACS Nano (article, 2019-06-21)
Field: Materials Science — Biomaterials

Supervised Learning and Mass Spectrometry Predicts the <i>in Vivo</i> Fate of Nanomaterials The surface of nanoparticles changes immediately after intravenous injection because blood proteins adsorb on the surface. How this interface changes during circulation and its impact on nanoparticle distribution within the body is not understood. Here, we developed a workflow to show that the evolution of proteins on nanoparticle surfaces predicts the biological fate of nanoparticles <i>in vivo</i>. This workflow involves extracting nanoparticles at multiple time points from circulation, isolating the proteins off the surface and performing proteomic mass spectrometry. The mass spectrometry protein library served as inputs, while blood clearance and organ accumulation were used as outputs to train a supervised deep neural network that predicts nanoparticle biological fate. In a double-blinded study, we tested the network by predicting nanoparticle spleen and liver accumulation with upward of 94% accuracy. Our neural network discovered that the mechanism of liver and spleen uptake is due to patterns of a multitude of nanoparticle surface adsorbed proteins. There are too many combinations to change these proteins manually using chemical or biological inhibitors to alter clearance. Therefore, we developed a technique that uses the host to act as a bioreactor to prepare nanoparticles with predictable clearance patterns that reduce liver and spleen uptake by 50% and 70%, respectively. These techniques provide opportunities to both predict nanoparticle behavior and also to engineer surface chemistries that are specifically designed by the body. Nanoparticle ; In vivo ; Biodistribution ; Nanotechnology ; Workflow ; Chemistry ; Proteomics ; Spleen ; Materials science ; Biophysics ; Computer science ; Biology ; Biochemistry ; Immunology ; Database Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticles: synthesis and applications
Authors: Mahmoud Elsawy | Stéphane Lanteri | Régis Duvigneau | Gauthier Brière | Mohamed Sabry Mohamed | Patrice Genevet
Source: Scientific Reports (article, 2019-11-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Global optimization of metasurface designs using statistical learning methods Optimization of the performance of flat optical components, also dubbed metasurfaces, is a crucial step towards their implementation in realistic optical systems. Yet, most of the design techniques, which rely on large parameter search to calculate the optical scattering response of elementary building blocks, do not account for near-field interactions that strongly influence the device performance. In this work, we exploit two advanced optimization techniques based on statistical learning and evolutionary strategies together with a fullwave high order Discontinuous Galerkin Time-Domain (DGTD) solver to optimize phase gradient metasurfaces. We first review the main features of these optimization techniques and then show that they can outperform most of the available designs proposed in the literature. Statistical learning is particularly interesting for optimizing complex problems containing several global minima/maxima. We then demonstrate optimal designs for GaN semiconductor phase gradient metasurfaces operating at visible wavelengths. Our numerical results reveal that rectangular and cylindrical nanopillar arrays can achieve more than respectively 88% and 85% of diffraction efficiency for TM polarization and both TM and TE polarization respectively, using only 150 fullwave simulations. To the best of our knowledge, this is the highest blazed diffraction efficiency reported so far at visible wavelength using such metasurface architectures. Maxima and minima ; Computer science ; Solver ; Polarization (electrochemistry) ; Diffraction ; Nanopillar ; Wavelength ; Global optimization ; Exploit ; Optimization problem ; Finite-difference time-domain method ; Optics ; Computational science ; Mathematical optimization ; Materials science ; Physics ; Algorithm ; Nanotechnology ; Mathematics ; Nanostructure Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: He‐Xiu Xu | Guangwei Hu | Ying Li | Lei Han | Jianlin Zhao | Yun-Ming Sun | Yuan Fang | Guangming Wang | Zhi Hao Jiang | Xiaohui Ling | Tie Jun Cui | Cheng‐Wei Qiu
Source: Light Science & Applications (article, 2019-01-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interference-assisted kaleidoscopic meta-plexer for arbitrary spin-wavefront manipulation Achieving simultaneous polarization and wavefront control, especially circular polarization with the auxiliary degree of freedom of light and spin angular momentum, is of fundamental importance in many optical applications. Interferences are typically undesirable in highly integrated photonic circuits and metasurfaces. Here, we propose an interference-assisted metasurface-multiplexer (meta-plexer) that counterintuitively exploits constructive and destructive interferences between hybrid meta-atoms and realizes independent spin-selective wavefront manipulation. Such kaleidoscopic meta-plexers are experimentally demonstrated via two types of single-layer spin-wavefront multiplexers that are composed of spatially rotated anisotropic meta-atoms. One type generates a spin-selective Bessel-beam wavefront for spin-down light and a low scattering cross-section for stealth for spin-up light. The other type demonstrates versatile control of the vortex wavefront, which is also characterized by the orbital angular momentum of light, with frequency-switchable numbers of beams under linearly polarized wave excitation. Our findings offer a distinct interference-assisted concept for realizing advanced multifunctional photonics with arbitrary and independent spin-wavefront features. A variety of applications can be readily anticipated in optical diodes, isolators, and spin-Hall meta-devices without cascading bulky optical elements. Wavefront ; Physics ; Photonics ; Optics ; Angular momentum ; Polarization (electrochemistry) ; Optical vortex ; Beam (structure) ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Random lasers and scattering media
Authors: Jie Chen | Wei Yan | Esther J. Townsend | Jiangtao Feng | Long Pan | Veronica Del Angel Hernandez | Charl F. J. Faul
Source: Angewandte Chemie International Edition (article, 2019-06-17)
Field: Materials Science — Materials Chemistry

Tunable Surface Area, Porosity, and Function in Conjugated Microporous Polymers Simple inorganic salts are used to tune N-containing conjugated microporous polymers (CMPs) synthesized by Buchwald-Hartwig (BH) cross-coupling reactions. Poly(triphenylamine), PTPA, initially shows a broad distribution of micropores, mesopores, and macropores. However, the addition of inorganic salts affects all porous network properties significantly: the pore size distribution is narrowed to the microporous range only, mimicking COFs and MOFs; the BET surface area is radically improved from 58 m<sup>2</sup> g<sup>-1</sup> to 1152 m<sup>2</sup> g<sup>-1</sup> ; and variations of the anion and cation sizes are used to fine-tune the surface area of PTPA, with the surface area showing a gradual decrease with an increase in the ionic radius of salts. The effect of the salt on the physical properties of the polymer is attributed to adjusting and optimizing the Hansen solubility parameters (HSPs) of solvents for the growing polymer, and named the Beijing-Xi'an Jiaotong (BXJ) method. Microporous material ; Conjugated microporous polymer ; Polymer ; Materials science ; Mesoporous material ; BET theory ; Chemical engineering ; Specific surface area ; Porosity ; Conjugated system ; Polymer chemistry ; Ionic bonding ; Solubility ; Adsorption ; Chemistry ; Ion ; Organic chemistry ; Composite material ; Catalysis Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Rama K. Vasudevan | Kamal Choudhary | Apurva Mehta | Ryan P. Smith | Gilad Kusne | Francesca Tavazza | Lukáš Vlček | Maxim Ziatdinov | Sergei V. Kalinin | Jason Hattrick‐Simpers
Source: MRS Communications (article, 2019-07-22)
Field: Materials Science — Materials Chemistry

Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics Underpinning ; Artificial intelligence ; Key (lock) ; Computer science ; Nanotechnology ; Data science ; Explosive material ; Machine learning ; Materials science ; Systems engineering ; Engineering ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Michael J. Willatt | Félix Musil | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2019-04-17)
Field: Materials Science — Materials Chemistry

Atom-density representations for machine learning The applications of machine learning techniques to chemistry and materials science become more numerous by the day. The main challenge is to devise representations of atomic systems that are at the same time complete and concise, so as to reduce the number of reference calculations that are needed to predict the properties of different types of materials reliably. This has led to a proliferation of alternative ways to convert an atomic structure into an input for a machine-learning model. We introduce an abstract definition of chemical environments that is based on a smoothed atomic density, using a bra-ket notation to emphasize basis set independence and to highlight the connections with some popular choices of representations for describing atomic systems. The correlations between the spatial distribution of atoms and their chemical identities are computed as inner products between these feature kets, which can be given an explicit representation in terms of the expansion of the atom density on orthogonal basis functions, that is equivalent to the smooth overlap of atomic positions power spectrum, but also in real space, corresponding to n-body correlations of the atom density. This formalism lays the foundations for a more systematic tuning of the behavior of the representations, by introducing operators that represent the correlations between structure, composition, and the target properties. It provides a unifying picture of recent developments in the field and indicates a way forward toward more effective and computationally affordable machine-learning schemes for molecules and materials. Notation ; Formalism (music) ; Computer science ; Representation (politics) ; Atom (system on chip) ; Basis (linear algebra) ; Artificial intelligence ; Theoretical computer science ; Statistical physics ; Algorithm ; Mathematics ; Physics ; Geometry ; Arithmetic Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2019-04-19)
Field: Materials Science — Materials Chemistry

Unsupervised machine learning in atomistic simulations, between predictions and understanding Automated analyses of the outcome of a simulation have been an important part of atomistic modeling since the early days, addressing the need of linking the behavior of individual atoms and the collective properties that are usually the final quantity of interest. Methods such as clustering and dimensionality reduction have been used to provide a simplified, coarse-grained representation of the structure and dynamics of complex systems from proteins to nanoparticles. In recent years, the rise of machine learning has led to an even more widespread use of these algorithms in atomistic modeling and to consider different classification and inference techniques as part of a coherent toolbox of data-driven approaches. This perspective briefly reviews some of the unsupervised machine-learning methods-that are geared toward classification and coarse-graining of molecular simulations-seen in relation to the fundamental mathematical concepts that underlie all machine-learning techniques. It discusses the importance of using concise yet complete representations of atomic structures as the starting point of the analyses and highlights the risk of introducing preconceived biases when using machine learning to rationalize and understand structure-property relations. Supervised machine-learning techniques that explicitly attempt to predict the properties of a material given its structure are less susceptible to such biases. Current developments in the field suggest that using these two classes of approaches side-by-side and in a fully integrated mode, while keeping in mind the relations between the data analysis framework and the fundamental physical principles, will be key to realizing the full potential of machine learning to help understand the behavior of complex molecules and materials. Artificial intelligence ; Machine learning ; Computer science ; Unsupervised learning Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Computational Drug Discovery Methods
Authors: Fei Li | Jinsong Han | Tian Cao | William Lam | Baoer Fan | Wen Tang | Sijie Chen | Kin Lam Fok | Linxian Li
Source: Proceedings of the National Academy of Sciences (article, 2019-05-20)
Field: Materials Science — Biomaterials

Design of self-assembly dipeptide hydrogels and machine learning via their chemical features Hydrogels that are self-assembled by peptides have attracted great interest for biomedical applications. However, the link between chemical structures of peptides and their corresponding hydrogel properties is still unclear. Here, we showed a combinational approach to generate a structurally diverse hydrogel library with more than 2,000 peptides and evaluated their corresponding properties. We used a quantitative structure-property relationship to calculate their chemical features reflecting the topological and physicochemical properties, and applied machine learning to predict the self-assembly behavior. We observed that the stiffness of hydrogels is correlated with the diameter and cross-linking degree of the nanofiber. Importantly, we demonstrated that the hydrogels support cell proliferation in culture, suggesting the biocompatibility of the hydrogel. The combinatorial hydrogel library and the machine learning approach we developed linked the chemical structures with their self-assembly behavior and can accelerate the design of novel peptide structures for biomedical use. Self-healing hydrogels ; Dipeptide ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Engineering ; Chemical engineering ; Peptide Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Glycosylation and Glycoproteins Research
Authors: Gaochan Wu | Tong Zhao | Dongwei Kang | Jian Zhang | Yuning Song | Vigneshwaran Namasivayam | Jacob Kongsted | Christophe Pannecouque | Erik De Clercq | Vasanthanathan Poongavanam | Xinyong Liu | Peng Zhan
Source: Journal of Medicinal Chemistry (review, 2019-05-03)
Field: Materials Science — Materials Chemistry

Overview of Recent Strategic Advances in Medicinal Chemistry Introducing novel strategies, concepts, and technologies that speed up drug discovery and the drug development cycle is of great importance both in the highly competitive pharmaceutical industry as well as in academia. This Perspective aims to present a "big-picture" overview of recent strategic innovations in medicinal chemistry and drug discovery. Chemistry ; Drug discovery ; Pharmaceutical industry ; Perspective (graphical) ; Drug industry ; Engineering ethics ; Pharmaceutical technology ; Nanotechnology ; Data science ; Management science ; Pharmacology ; Computer science ; Engineering ; Biochemistry Photochromic and Fluorescence Chemistry ; Click Chemistry and Applications ; Computational Drug Discovery Methods
Authors: Xinghuo Wang | Nan Song | Wei Hou | Chunyu Wang | Yan Wang | Jun Tang | Ying‐Wei Yang
Source: Advanced Materials (article, 2019-08-04)
Field: Materials Science — Materials Chemistry

Efficient Aggregation‐Induced Emission Manipulated by Polymer Host Materials Linear copolymer hosts bearing a number of pillar[5]arene dangling side chains are synthesized for the facile construction of highly emissive supramolecular polymer networks (SPNs) upon noncovalently cross-linking with a series of tetraphenyethylene (TPE)-based tetratopic guests terminated with different functional groups through supramolecular host-guest interactions. An extremely high fluorescence quantum yield (98.22%) of the SPNs materials is obtained in tetrahydrofuran (THF) by fine-tuning the parameters, and meanwhile supramolecular light-harvesting systems based on spherical supramolecular nanoparticles are constructed by interweaving 9,10-distyrylanthracene (DSA) and TPE-based guest molecules of aggregation-induced emission (AIE) with the copolymer hosts in the mixed solvent of THF/H<sub>2</sub> O. The present study not only illustrates the restriction of the intramolecular rotations (RIR)-ruled emission enhancement mechanism regulated particularly by macrocyclic arene-containing copolymer hosts, but also suggests a new self-assembly approach to construct high-performance light-harvesting materials. Materials science ; Aggregation-induced emission ; Polymer ; Host (biology) ; Nanotechnology ; Composite material ; Fluorescence Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Molecular Sensors and Ion Detection
Authors: Ranjith Kumar Kankala | Hongbo Zhang | Chenguang Liu | Kiran Reddy Kanubaddi | Chia‐Hung Lee | Shi‐Bin Wang | Wenguo Cui | Hélder A. Santos | Kaili Lin | Ai‐Zheng Chen
Source: Advanced Functional Materials (article, 2019-08-08)
Field: Materials Science — Materials Chemistry

Metal Species–Encapsulated Mesoporous Silica Nanoparticles: Current Advancements and Latest Breakthroughs Abstract Despite their advantageous morphological attributes and attractive physicochemical properties, mesoporous silica nanoparticles (MSNs) are merely supported as carriers or vectors for a reason. Incorporating various metal species in the confined nanospaces of MSNs (M‐MSNs) significantly enriches their mesoporous architecture and diverse functionalities, bringing exciting potentials to this burgeoning field of research. These incorporated guest species offer enormous benefits to the MSN hosts concerning the reduction of their eventual size and the enhancement of their performance and stability, among other benefits. Substantially, the guest species act through contributing to reduced aggregation, augmented durability, ease of long‐term storage, and reduced toxicity, attributes that are of particular interest in diverse fields of biomedicine. In this review, the first aim is to discuss the current advancements and latest breakthroughs in the fabrication of M‐MSNs, emphasizing the pros and cons, the confinement of various metal species in the nanospaces of MSNs, and various factors influencing the encapsulation of metal species in MSNs. Further, an emphasis on potential applications of M‐MSNs in various fields, including in adsorption, catalysis, photoluminescence, and biomedicine, among others, along with a set of examples is provided. Finally, the advances in M‐MSNs with perspectives are summarized. Nanotechnology ; Mesoporous silica ; Materials science ; Mesoporous material ; Nanoparticle ; Biomedicine ; Fabrication ; Computer science ; Catalysis ; Chemistry ; Bioinformatics ; Organic chemistry Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science ; Layered Double Hydroxides Synthesis and Applications