jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Darren P. Broom | C. J. Webb | George S. Fanourgakis | George E. Froudakis | Pantelis N. Trikalitis | Michael Hirscher
Source: International Journal of Hydrogen Energy (article, 2019-02-22)
Field: Chemistry — Inorganic Chemistry

Concepts for improving hydrogen storage in nanoporous materials Hydrogen storage in nanoporous materials has been attracting a great deal of attention in recent years, as high gravimetric H2 capacities, exceeding 10 wt% in some cases, can be achieved at 77 K using materials with particularly high surface areas. However, volumetric capacities at low temperatures, and both gravimetric and volumetric capacities at ambient temperature, need to be improved before such adsorbents become practically viable. This article therefore discusses approaches to increasing the gravimetric and volumetric hydrogen storage capacities of nanoporous materials, and maximizing the usable capacity of a material between the upper storage and delivery pressures. In addition, recent advances in machine learning and data science provide an opportunity to apply this technology to the search for new materials for hydrogen storage. The large number of possible component combinations and substitutions in various porous materials, including Metal-Organic Frameworks (MOFs), is ideally suited to a machine learning approach; so this is also discussed, together with some new material types that could prove useful in the future for hydrogen storage applications. Gravimetric analysis ; Hydrogen storage ; Nanoporous ; USable ; Process engineering ; Adsorption ; Computer science ; Hydrogen ; Materials science ; Nanotechnology ; Porosity ; Chemistry ; Composite material ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Martin A. Hubbe | Saeid Azizian | Sigrid Douven
Source: BioResources (review, 2019-08-01)
Field: Chemistry — Physical and Theoretical Chemistry

Implications of apparent pseudo-second-order adsorption kinetics onto cellulosic materials: A review The pseudo-second-order (PSO) kinetic model has become among the most popular ways to fit rate data for adsorption of metal ions, dyes, and other compounds from aqueous solution onto cellulose-based materials. This review first considers published evidence regarding the validity of the mechanistic assumptions underlying application of the PSO model to adsorption kinetics. A literal interpretation of the model requires an assumption that different adsorption sites on a solid substrate randomly collide with each other during a rate-limiting mechanistic step. Because of problems revealed by the literature regarding the usual assumptions associated with the PSO model, this review also considers how else to account for good fits of adsorption data to the PSO model. Studies have shown that adsorption behavior that fits the PSO model well often can be explained by diffusion-based mechanisms. Hypothetical data generated using the assumption of pseudo-first-order rate behavior has been shown to fit the PSO model very well. In light of published evidence, adsorption kinetics of cellulosic materials is expected to mainly depend on diffusionlimited processes, as affected by heterogeneous distributions of pore sizes and continual partitioning of solute species between a dissolved state and a fixed state of adsorption. Kinetics ; Cellulosic ethanol ; Adsorption ; Materials science ; Order (exchange) ; Chemical engineering ; Composite material ; Polymer science ; Cellulose ; Thermodynamics ; Physical chemistry ; Chemistry ; Physics ; Engineering ; Classical mechanics Electrostatics and Colloid Interactions ; Surfactants and Colloidal Systems ; Electrochemical Analysis and Applications
Authors: Brent L. Nannenga | Tamir Gonen
Source: Nature Methods (review, 2019-04-29)
Field: Materials Science — Materials Chemistry

The cryo-EM method microcrystal electron diffraction (MicroED) Cryo-electron microscopy ; Context (archaeology) ; Nanotechnology ; Diffraction ; Electron diffraction ; Materials science ; Physics ; Chemistry ; Optics ; Biology Enzyme Structure and Function ; RNA modifications and cancer ; Biochemical and Molecular Research
Authors: Stefan Chmiela | Huziel E. Sauceda | Igor Poltavsky | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: Computer Physics Communications (article, 2019-02-28)
Field: Materials Science — Materials Chemistry

sGDML: Constructing accurate and data efficient molecular force fields using machine learning We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., 2017) and i-PI (Kapil et al., 2019) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations. Computer science ; Force field (fiction) ; Computational science ; Artificial intelligence ; Machine learning ; Statistical physics ; Algorithm ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Patrick Wonner | Alexander Dreger | Lukas Vogel | Elric Engelage | Stefan M. Huber
Source: Angewandte Chemie International Edition (article, 2019-09-19)
Field: Chemistry — Organic Chemistry

Chalcogen Bonding Catalysis of a Nitro‐Michael Reaction Chalcogen bonding is the non-covalent interaction between Lewis acidic chalcogen substituents and Lewis bases. Herein, we present the first application of dicationic tellurium-based chalcogen bond donors in the nitro-Michael reaction between trans-β-nitrostyrene and indoles. This also constitutes the first activation of nitro derivatives by chalcogen bonding (and halogen bonding). The catalysts showed rate accelerations of more than a factor of 300 compared to strongly Lewis acidic hydrogen bond donors. Several comparison experiments, titrations, and DFT calculations support a chalcogen-bonding-based mode of activation of β-nitrostyrene. Chalcogen ; Chemistry ; Lewis acids and bases ; Catalysis ; Hydrogen bond ; Nitro ; Tellurium ; Covalent bond ; Halogen bond ; Polymer chemistry ; Organic chemistry ; Molecule Organic Chemistry Cycloaddition Reactions ; Chemical Synthesis and Reactions ; Sulfur-Based Synthesis Techniques
Authors: Zhen Bi | Noah F. Q. Yuan | Liang Fu
Source: Physical review. B./Physical review. B (article, 2019-07-31)
Field: Materials Science — Materials Chemistry

Designing flat bands by strain Magic angle twisted bilayer graphene (MATBG) is an exciting new system for flat electronic bands and correlated physics. However, heterostrains are ubiquitous in the TBG samples, and their role on the electronic properties has not been clear. Here, the authors show that heterostrains generically broaden the flat bands in MATBG. This explains many puzzling results from transport and scanning tunneling spectroscopy experiments. Furthermore, one can utilize the heterostrain to engineer highly flexible moir\'e flat bands in bilayer transition metal dichalcogenides, suggesting another platform to be exploited. Strain (injury) ; Materials science ; Biology ; Anatomy 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Song Gao | Jingwei Hou | Zeyu Deng | Tiesheng Wang | Sebastian Beyer | Ana Guilherme Buzanich | Joseph J. Richardson | Aditya Rawal | Robert Seidel | Muhammad Yazid Bin Zulkifli | Weiwei Li | Thomas D. Bennett | Anthony K. Cheetham | Kang Liang | Vicki Chen
Source: Chem (article, 2019-05-08)
Field: Chemistry — Inorganic Chemistry

Improving the Acidic Stability of Zeolitic Imidazolate Frameworks by Biofunctional Molecules Zeolitic imidazolate framework ; Biomolecule ; Chemistry ; Imidazolate ; Molecule ; Adsorption ; Catalysis ; Chemical stability ; Synergistic catalysis ; Ligand (biochemistry) ; Combinatorial chemistry ; Inorganic chemistry ; Metal-organic framework ; Organic chemistry ; Biochemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Sulfur Compounds in Biology
Authors: Seoin Back | Junwoong Yoon | Nianhan Tian | Wen Zhong | Kevin Tran | Zachary W. Ulissi
Source: The Journal of Physical Chemistry Letters (article, 2019-07-16)
Field: Materials Science — Materials Chemistry

Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts High-throughput screening of catalysts can be performed using density functional theory calculations to predict catalytic properties, often correlated with adsorbate binding energies. However, more complete investigations would require an order of 2 more calculations compared to the current approach, making the computational cost a bottleneck. Recently developed machine-learning methods have been demonstrated to predict these properties from hand-crafted features but have struggled to scale to large composition spaces or complex active sites. Here, we present an application of a deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information. The model effectively learns the most important surface features to predict binding energies. Our method predicts CO and H binding energies after training with 12 000 data for each adsorbate with a mean absolute error of 0.15 eV for a diverse chemical space. Our method is also capable of creating saliency maps that determine atomic contributions to binding energies. Bottleneck ; Convolutional neural network ; Binding energy ; Throughput ; Computer science ; Voronoi diagram ; Artificial neural network ; Density functional theory ; Chemical space ; Surface (topology) ; Catalysis ; Chemistry ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics ; Atomic physics ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Sara P. Morcillo
Source: Angewandte Chemie International Edition (review, 2019-06-06)
Field: Chemistry — Organic Chemistry

Radical‐Promoted C−C Bond Cleavage: A Deconstructive Approach for Selective Functionalization Just as "Deconstructivism" appeared as a novel movement in architecture in the 1980s, deconstructive approaches have recently emerged as excellent strategies for scaffold hopping modifications in chemistry. The deconstruction and functionalization of cyclic molecules mainly involves the cleavage of the carbon-carbon (C-C) bond followed by the construction of new bonds. The cleavage of inert C-C single bonds, especially in unstrained cycles, and their subsequent functionalization is still one of the most sought-after challenges in chemistry. In this vein, radical-mediated strategies provide an excellent approach for achieving this aim. This minireview is an outline of the history of homolytic cleavage and highlights the recent advances in exploring new chemical space by deconstructive functionalization. Surface modification ; Cleavage (geology) ; Bond cleavage ; Chemistry ; Inert ; Deconstruction (building) ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Organic chemistry ; Catalysis ; Engineering Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Niyaz Mohammad Mahmoodi | Mina Oveisi | Ali Taghizadeh | Mohsen Taghizadeh
Source: Carbohydrate Polymers (article, 2019-09-22)
Field: Materials Science — Biomaterials

Synthesis of pearl necklace-like ZIF-8@chitosan/PVA nanofiber with synergistic effect for recycling aqueous dye removal Adsorption ; Nanofiber ; Aqueous solution ; Langmuir adsorption model ; Polyvinyl alcohol ; Materials science ; Chitosan ; Chemical engineering ; Crystal violet ; Nuclear chemistry ; Chemistry ; Composite material ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Membrane Separation Technologies ; Supercapacitor Materials and Fabrication
Authors: Dirk Lenzen | Jingjing Zhao | Sebastian‐Johannes Ernst | Mohammad Wahiduzzaman | A. Ken Inge | Dominik Fröhlich | Hongyi Xu | Hans‐Jörg Bart | Christoph Janiak | Stefan K. Henninger | Guillaume Maurin | Xiaodong Zou | Norbert Stock
Source: Nature Communications (article, 2019-07-09)
Field: Chemistry — Inorganic Chemistry

A metal–organic framework for efficient water-based ultra-low-temperature-driven cooling Efficient use of energy for cooling applications is a very important and challenging field in science. Ultra-low temperature actuated (T<sub>driving</sub> < 80 °C) adsorption-driven chillers (ADCs) with water as the cooling agent are one environmentally benign option. The nanoscale metal-organic framework [Al(OH)(C<sub>6</sub>H<sub>2</sub>O<sub>4</sub>S)] denoted CAU-23 was discovered that possess favorable properties, including water adsorption capacity of 0.37 g<sub>H2O</sub>/g<sub>sorbent</sub> around p/p<sub>0</sub> = 0.3 and cycling stability of at least 5000 cycles. Most importantly the material has a driving temperature down to 60 °C, which allows for the exploitation of yet mostly unused temperature sources and a more efficient use of energy. These exceptional properties are due to its unique crystal structure, which was unequivocally elucidated by single crystal electron diffraction. Monte Carlo simulations were performed to reveal the water adsorption mechanism at the atomic level. With its green synthesis, CAU-23 is an ideal material to realize ultra-low temperature driven ADC devices. Adsorption ; Metal-organic framework ; Materials science ; Sorbent ; Chiller ; Water cooling ; Chemical engineering ; Nanotechnology ; Chemistry ; Thermodynamics ; Physical chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Thermal Expansion and Ionic Conductivity ; Layered Double Hydroxides Synthesis and Applications
Authors: Bruno Andreotti | Jacco H. Snoeijer
Source: Annual Review of Fluid Mechanics (article, 2019-08-19)
Field: Materials Science — Surfaces, Coatings and Films

Statics and Dynamics of Soft Wetting The laws of wetting are well known for drops on rigid surfaces but change dramatically when the substrate is soft and deformable. The combination of wetting and the intricacies of soft polymeric interfaces have provided many rich examples of fluid–structure interactions, both in terms of phenomenology and from a fundamental perspective. In this review we discuss experimental and theoretical progress on the statics and dynamics of soft wetting. In this context we critically revisit the foundations of capillarity, such as the nature of solid surface tension, the microscopic mechanics near the contact line, and the dissipative mechanisms that lead to unexpected spreading dynamics. Wetting ; Statics ; Phenomenology (philosophy) ; Dissipative system ; Wetting transition ; Context (archaeology) ; Mechanics ; Surface tension ; Physics ; Classical mechanics ; Materials science ; Geology ; Thermodynamics Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Fluid Dynamics and Thin Films
Authors: Helge S. Stein | John M. Gregoire
Source: Chemical Science (review, 2019-01-01)
Field: Materials Science — Materials Chemistry

Progress and prospects for accelerating materials science with automated and autonomous workflows Accelerating materials research by integrating automation with artificial intelligence is increasingly recognized as a grand scientific challenge to discover and develop materials for emerging and future technologies. While the solid state materials science community has demonstrated a broad range of high throughput methods and effectively leveraged computational techniques to accelerate individual research tasks, revolutionary acceleration of materials discovery has yet to be fully realized. This perspective review presents a framework and ontology to outline a materials experiment lifecycle and visualize materials discovery workflows, providing a context for mapping the realized levels of automation and the next generation of autonomous loops in terms of scientific and automation complexity. Expanding autonomous loops to encompass larger portions of complex workflows will require integration of a range of experimental techniques as well as automation of expert decisions, including subtle reasoning about data quality, responses to unexpected data, and model design. Recent demonstrations of workflows that integrate multiple techniques and include autonomous loops, combined with emerging advancements in artificial intelligence and high throughput experimentation, signal the imminence of a revolution in materials discovery. Workflow ; Automation ; Data science ; Computer science ; Emerging technologies ; Systems engineering ; Software engineering ; Engineering ; Artificial intelligence ; Database Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced X-ray and CT Imaging
Authors: Wenjuan Zhang | Ying Huai | Zhiping Miao | Airong Qian | Yonghua Wang
Source: Frontiers in Pharmacology (review, 2019-07-11)
Field: Chemistry — Analytical Chemistry

Systems Pharmacology for Investigation of the Mechanisms of Action of Traditional Chinese Medicine in Drug Discovery As a traditional medical intervention in Asia and a complementary and alternative medicine in western countries, traditional Chinese medicine (TCM) has attracted global attention in the life science field. TCM provides extensive natural resources for medicinal compounds, and these resources are generally regarded as effective and safe for use in drug discovery. However, owing to the complexity of compounds and their related multiple targets of TCM, it remains difficult to dissect the mechanisms of action of herbal medicines at a holistic level. To solve the issue, in the review, we proposed a novel approach of systems pharmacology to identify the bioactive compounds, predict their related targets, and illustrate the molecular mechanisms of action of TCM. With a predominant focus on the mechanisms of actions of TCM, we also highlighted the application of the systems pharmacology approach for the prediction of drug combination and dynamic analysis, the synergistic effects of TCMs, formula dissection, and theory analysis. In summary, the systems pharmacology method contributes to understand the complex interactions among biological systems, drugs, and complex diseases from a network perspective. Consequently, systems pharmacology provides a novel approach to promote drug discovery in a precise manner and a systems level, thus facilitating the modernization of TCM. Systems pharmacology ; Drug discovery ; Traditional Chinese medicine ; Action (physics) ; Systems biology ; Pharmacology ; Drug ; Drug action ; Clinical pharmacology ; Medicine ; Traditional medicine ; Computational biology ; Bioinformatics ; Biology ; Alternative medicine Chromatography in Natural Products ; Computational Drug Discovery Methods ; Pharmacological Effects of Natural Compounds
Authors: Vera Ignjatović | Philipp E. Geyer | Krishnan K. Palaniappan | Jessica E. Chaaban | Gilbert S. Omenn | Mark S. Baker | Eric W. Deutsch | Jochen M. Schwenk
Source: Journal of Proteome Research (review, 2019-10-01)
Field: Chemistry — Spectroscopy

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice. Proteomics ; Proteome ; Human plasma ; Workflow ; Pipeline (software) ; Data science ; Mass spectrometry ; Computational biology ; Computer science ; Bioinformatics ; Chemistry ; Biology ; Chromatography ; Biochemistry ; Database Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Keith A. Brown | Sarah Brittman | Nicolò Maccaferri | Deep Jariwala | Umberto Celano
Source: Nano Letters (article, 2019-12-05)
Field: Materials Science — Materials Chemistry

Machine Learning in Nanoscience: Big Data at Small Scales Recent advances in machine learning (ML) offer new tools to extract new insights from large data sets and to acquire small data sets more effectively. Researchers in nanoscience are experimenting with these tools to tackle challenges in many fields. In addition to ML's advancement of nanoscience, nanoscience provides the foundation for neuromorphic computing hardware to expand the implementation of ML algorithms. In this Mini Review, we highlight some recent efforts to connect the ML and nanoscience communities by focusing on three types of interaction: (1) using ML to analyze and extract new insights from large nanoscience data sets, (2) applying ML to accelerate material discovery, including the use of active learning to guide experimental design, and (3) the nanoscience of memristive devices to realize hardware tailored for ML. We conclude with a discussion of challenges and opportunities for future interactions between nanoscience and ML researchers. Nanotechnology ; Big data ; Data science ; Materials science ; Computer science ; Data mining Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Ghislaine Vantomme | E. W. Meijer
Source: Science (article, 2019-03-28)
Field: Materials Science — Biomaterials

The construction of supramolecular systems Self-assembly must be transformed into multistep synthesis to create complex structures Supramolecular chemistry ; Chemistry ; Computer science ; Molecule ; Organic chemistry Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Silicone and Siloxane Chemistry