jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zongbi Bao | Danyan Xie | Ganggang Chang | Hui Wu | Liangying Li | Wei Zhou | Hailong Wang | Zhiguo Zhang | Huabin Xing | Qiwei Yang | Michael J. Zaworotko | Qilong Ren | Banglin Chen
Source: Journal of the American Chemical Society (article, 2018-03-14)
Field: Chemistry — Inorganic Chemistry

Fine Tuning and Specific Binding Sites with a Porous Hydrogen-Bonded Metal-Complex Framework for Gas Selective Separations Research on hydrogen-bonded organic frameworks (HOFs) has been developed for quite a long time; however, those with both established permanent porosities and functional properties are extremely rare due to weak hydrogen-bonding interactions among molecular organic linkers, which are much more fragile and difficult to stabilize. Herein, through judiciously combining the superiority of both the moderately stable coordination bonds in metal-organic frameworks and hydrogen bonds, we have realized a microporous hydrogen-bonded metal-complex or metallotecton framework HOF-21, which not only shows permanent porosity, but also exhibits highly selective separation performance of C<sub>2</sub>H<sub>2</sub>/C<sub>2</sub>H<sub>4</sub> at room temperature. The outstanding separation performance can be ascribed to sieving effect confined by the fine-tuning pores and the superimposed hydrogen-bonding interaction between C<sub>2</sub>H<sub>2</sub> and SiF<sub>6</sub><sup>2-</sup> on both ends as validated by both modeling and neutron powder diffraction experiments. More importantly, the collapsed HOF-21 can be restored by simply immersing it into water or salt solution. To the best of our knowledge, such extraordinary water stability and restorability of HOF-21 were observed for the first time in HOFs, underlying the bright perspective of such new HOF materials for their industrial usage. Chemistry ; Porosity ; Hydrogen ; Metal ; Chemical engineering ; Nanotechnology ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Bryce Meredig | Erin Antono | Carena Church | Maxwell Hutchinson | Julia Ling | Sean Paradiso | Ben Blaiszik | Ian Foster | Brenna M. Gibbons | Jason Hattrick‐Simpers | Apurva Mehta | Logan Ward
Source: Molecular Systems Design & Engineering (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery Traditional machine learning (ML) metrics overestimate model performance for materials discovery. Extrapolation ; Computer science ; Machine learning ; Data science ; Artificial intelligence ; Mathematics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Diederik Roke | Sander J. Wezenberg | Ben L. Feringa
Source: Proceedings of the National Academy of Sciences (article, 2018-04-30)
Field: Chemistry — Organic Chemistry

Molecular rotary motors: Unidirectional motion around double bonds The field of synthetic molecular machines has quickly evolved in recent years, growing from a fundamental curiosity to a highly active field of chemistry. Many different applications are being explored in areas such as catalysis, self-assembled and nanostructured materials, and molecular electronics. Rotary molecular motors hold great promise for achieving dynamic control of molecular functions as well as for powering nanoscale devices. However, for these motors to reach their full potential, many challenges still need to be addressed. In this paper we focus on the design principles of rotary motors featuring a double-bond axle and discuss the major challenges that are ahead of us. Although great progress has been made, further design improvements, for example in terms of efficiency, energy input, and environmental adaptability, will be crucial to fully exploit the opportunities that these rotary motors offer. Molecular motor ; Exploit ; Adaptability ; Nanotechnology ; Molecular machine ; Focus (optics) ; Computer science ; Axle ; Engineering ; Mechanical engineering ; Materials science ; Physics Supramolecular Chemistry and Complexes ; Photoreceptor and optogenetics research ; Advanced NMR Techniques and Applications
Authors: Xiaomei Dong | Huile Jin | Rongyue Wang | Jingjing Zhang | Xin Feng | Chengzhan Yan | Suqin Chen | Shun Wang | Jichang Wang | Jun Lü
Source: Advanced Energy Materials (article, 2018-01-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High Volumetric Capacitance, Ultralong Life Supercapacitors Enabled by Waxberry‐Derived Hierarchical Porous Carbon Materials Abstract Supercapacitors with fast charge/discharge rate and long cycling stability (&gt;50 000 cycles) are attractive for energy storage and mobile power supply. In this paper, a facile strategy is developed to fabricate an Fe 2 O 3 /FeS‐decorated N, S‐codoped hierarchical porous carbon hybrid. Its microstructure and compositions can be readily controlled through adjusting the hydrothermal reaction between waxberry and iron sulfate. The constructed supercapacitors with the as‐prepared carbon materials from this reaction are able to exhibit outstanding capacitive performance with a superfast charge/discharge rate (&lt;1 s), ultralong cycle life (&gt;50 000 cycles, 80 A g −1 ), ultrahigh volumetric capacitance (1320.4 F cm −3 , 0.1 A g −1 ), and high energy density (100.9 W h kg −1 , 221.9 W h L −1 ). The outstanding performance makes it one of the best biomass‐derived supercapacitors. The superior capacitive behavior is likely to arise from the N and S codoping on the surface/edge/skeleton of the carbon microspheres and nanosheet composites coupled with the fast redox reaction of Fe 2 O 3 /FeS. Overall, this research presents a new avenue for developing the next generation of sustainable high‐performance energy storage device. Supercapacitor ; Materials science ; Capacitance ; Carbon fibers ; Energy storage ; Nanosheet ; Capacitive sensing ; Chemical engineering ; Nanotechnology ; Porosity ; Microstructure ; Electrode ; Composite material ; Power (physics) ; Electrical engineering Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Advancements in Battery Materials
Authors: J. Schiebel | Roberto Gaspari | Tobias Wulsdorf | K. Ngo | Christian Sohn | Tobias E. Schrader | Andrea Cavalli | Andreas Ostermann | A. Heine | G. Klebe
Source: Nature Communications (article, 2018-08-28)
Field: Materials Science — Materials Chemistry

Intriguing role of water in protein-ligand binding studied by neutron crystallography on trypsin complexes Hydrogen bonds are key interactions determining protein-ligand binding affinity and therefore fundamental to any biological process. Unfortunately, explicit structural information about hydrogen positions and thus H-bonds in protein-ligand complexes is extremely rare and similarly the important role of water during binding remains poorly understood. Here, we report on neutron structures of trypsin determined at very high resolutions ≤1.5 Å in uncomplexed and inhibited state complemented by X-ray and thermodynamic data and computer simulations. Our structures show the precise geometry of H-bonds between protein and the inhibitors N-amidinopiperidine and benzamidine along with the dynamics of the residual solvation pattern. Prior to binding, the ligand-free binding pocket is occupied by water molecules characterized by a paucity of H-bonds and high mobility resulting in an imperfect hydration of the critical residue Asp189. This phenomenon likely constitutes a key factor fueling ligand binding via water displacement and helps improving our current view on water influencing protein-ligand recognition. Hydrogen bond ; Solvation ; Ligand (biochemistry) ; Crystallography ; Chemistry ; Protein ligand ; Benzamidine ; Molecule ; Protein structure ; Solvation shell ; Biochemistry ; Receptor ; Organic chemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Jiaxuan You | Bowen Liu | Rex Ying | Vijay S. Pande | Jure Leskovec
Source: arXiv (Cornell University) (preprint, 2018-06-07)
Field: Materials Science — Materials Chemistry

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This is especially important in the task of molecular graph generation, whose goal is to discover novel molecules with desired properties such as drug-likeness and synthetic accessibility, while obeying physical laws such as chemical valency. However, designing models to find molecules that optimize desired properties while incorporating highly complex and non-differentiable rules remains to be a challenging task. Here we propose Graph Convolutional Policy Network (GCPN), a general graph convolutional network based model for goal-directed graph generation through reinforcement learning. The model is trained to optimize domain-specific rewards and adversarial loss through policy gradient, and acts in an environment that incorporates domain-specific rules. Experimental results show that GCPN can achieve 61% improvement on chemical property optimization over state-of-the-art baselines while resembling known molecules, and achieve 184% improvement on the constrained property optimization task. Computer science ; Graph ; Differentiable function ; Theoretical computer science ; Reinforcement learning ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xiaolin Li | Yichi Zhang | He Zhao | Craig Burkhart | L. Catherine Brinson | Wei Chen
Source: Scientific Reports (article, 2018-09-03)
Field: Materials Science — Materials Chemistry

A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions Stochastic microstructure reconstruction has become an indispensable part of computational materials science, but ongoing developments are specific to particular material systems. In this paper, we address this generality problem by presenting a transfer learning-based approach for microstructure reconstruction and structure-property predictions that is applicable to a wide range of material systems. The proposed approach incorporates an encoder-decoder process and feature-matching optimization using a deep convolutional network. For microstructure reconstruction, model pruning is implemented in order to study the correlation between the microstructural features and hierarchical layers within the deep convolutional network. Knowledge obtained in model pruning is then leveraged in the development of a structure-property predictive model to determine the network architecture and initialization conditions. The generality of the approach is demonstrated numerically for a wide range of material microstructures with geometrical characteristics of varying complexity. Unlike previous approaches that only apply to specific material systems or require a significant amount of prior knowledge in model selection and hyper-parameter tuning, the present approach provides an off-the-shelf solution to handle complex microstructures, and has the potential of expediting the discovery of new materials. Computer science ; Initialization ; Artificial intelligence ; Convolutional neural network ; Property (philosophy) ; Transfer of learning ; Deep learning ; Pruning ; Algorithm ; Machine learning Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Christel A. S. Bergström | Per Larsson
Source: International Journal of Pharmaceutics (review, 2018-02-06)
Field: Materials Science — Materials Chemistry

Computational prediction of drug solubility in water-based systems: Qualitative and quantitative approaches used in the current drug discovery and development setting In this review we will discuss recent advances in computational prediction of solubility in water-based solvents. Our focus is set on recent advances in predictions of biorelevant solubility in media mimicking the human intestinal fluids and on new methods to predict the thermodynamic cycle rather than prediction of solubility in pure water through quantitative structure property relationships (QSPR). While the literature is rich in QSPR models for both solubility and melting point, a physicochemical property strongly linked to the solubility, recent advances in the modelling of these properties make use of theory and computational simulations to better predict these properties or processes involved therein (e.g. solid state crystal lattice packing, dissociation of molecules from the lattice and solvation). This review serves to provide an update on these new approaches and how they can be used to more accurately predict solubility, and also importantly, inform us on molecular interactions and processes occurring during drug dissolution and solubilisation. Solubility ; Solvation ; Dissolution ; Chemistry ; Quantitative structure–activity relationship ; Drug discovery ; Molecule ; Thermodynamics ; Computational chemistry ; Biochemical engineering ; Organic chemistry ; Stereochemistry ; Physics Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems ; Analytical Chemistry and Chromatography
Authors: Lei Wang | Sergey Kruk | Kirill Koshelev | Ivan I. Kravchenko | Barry Luther‐Davies | Yuri S. Kivshar
Source: Nano Letters (article, 2018-05-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nonlinear Wavefront Control with All-Dielectric Metasurfaces Metasurfaces, two-dimensional lattices of nanoscale resonators, offer unique opportunities for functional flat optics and allow the control of the transmission, reflection, and polarization of a wavefront of light. Recently, all-dielectric metasurfaces reached remarkable efficiencies, often matching or out-performing conventional optical elements. The exploitation of the nonlinear optical response of metasurfaces offers a paradigm shift in nonlinear optics, and dielectric nonlinear metasurfaces are expected to enrich subwavelength photonics by enhancing substantially nonlinear response of natural materials combined with the efficient control of the phase of nonlinear waves. Here, we suggest a novel and rather general approach for engineering the wavefront of parametric waves of arbitrary complexity generated by a nonlinear metasurface. We design all-dielectric nonlinear metasurfaces, achieve a highly efficient wavefront control of a third-harmonic field, and demonstrate the generation of nonlinear beams at a designed angle and the generation of nonlinear focusing vortex beams. Our nonlinear metasurfaces produce phase gradients over a full 0-2π phase range with a 92% diffraction efficiency. Wavefront ; Optics ; Nonlinear system ; Nonlinear optics ; Dielectric ; Polarization (electrochemistry) ; Resonator ; Second-harmonic generation ; Physics ; Photonics ; Metamaterial ; Optoelectronics ; Laser Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: Laura R. Saunders | X. Peter
Source: Macromolecular Bioscience (review, 2018-11-22)
Field: Materials Science — Biomaterials

Self‐Healing Supramolecular Hydrogels for Tissue Engineering Applications Self-healing supramolecular hydrogels have emerged as a novel class of biomaterials that combine hydrogels with supramolecular chemistry to develop highly functional biomaterials with advantages including native tissue mimicry, biocompatibility, and injectability. These properties are endowed by the reversibly cross-linked polymer network of the hydrogel. These hydrogels have great potential for realizing yet to be clinically translated tissue engineering therapies. This review presents methods of self-healing supramolecular hydrogel formation and their uses in tissue engineering as well as future perspectives. Self-healing hydrogels ; Tissue engineering ; Biocompatibility ; Supramolecular chemistry ; Supramolecular polymers ; Nanotechnology ; Chemistry ; Biomedical engineering ; Materials science ; Polymer chemistry ; Medicine ; Organic chemistry Supramolecular Self-Assembly in Materials ; Hydrogels: synthesis, properties, applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Aditya Kamath | Rodrigo A. Vargas–Hernández | Roman V. Krems | Tucker Carrington | Sergei Manzhos
Source: The Journal of Chemical Physics (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy For molecules with more than three atoms, it is difficult to fit or interpolate a potential energy surface (PES) from a small number of (usually ab initio) energies at points. Many methods have been proposed in recent decades, each claiming a set of advantages. Unfortunately, there are few comparative studies. In this paper, we compare neural networks (NNs) with Gaussian process (GP) regression. We re-fit an accurate PES of formaldehyde and compare PES errors on the entire point set used to solve the vibrational Schrödinger equation, i.e., the only error that matters in quantum dynamics calculations. We also compare the vibrational spectra computed on the underlying reference PES and the NN and GP potential surfaces. The NN and GP surfaces are constructed with exactly the same points, and the corresponding spectra are computed with the same points and the same basis. The GP fitting error is lower, and the GP spectrum is more accurate. The best NN fits to 625/1250/2500 symmetry unique potential energy points have global PES root mean square errors (RMSEs) of 6.53/2.54/0.86 cm<sup>-1</sup>, whereas the best GP surfaces have RMSE values of 3.87/1.13/0.62 cm<sup>-1</sup>, respectively. When fitting 625 symmetry unique points, the error in the first 100 vibrational levels is only 0.06 cm<sup>-1</sup> with the best GP fit, whereas the spectrum on the best NN PES has an error of 0.22 cm<sup>-1</sup>, with respect to the spectrum computed on the reference PES. This error is reduced to about 0.01 cm<sup>-1</sup> when fitting 2500 points with either the NN or GP. We also find that the GP surface produces a relatively accurate spectrum when obtained based on as few as 313 points. Kriging ; Energy spectrum ; Artificial neural network ; Gaussian process ; Quality (philosophy) ; Regression ; Gaussian ; Energy (signal processing) ; Process (computing) ; Statistics ; Regression analysis ; Spectrum (functional analysis) ; Artificial intelligence ; Pattern recognition (psychology) ; Statistical physics ; Machine learning ; Computer science ; Biological system ; Mathematics ; Physics ; Biology ; Quantum mechanics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Neural Networks and Applications
Authors: Soo‐Young Kang | Zhaoxia Ji | Ling‐Fang Tseng | Sara A. Turner | Dinara A. Villanueva | Rhiannon Johnson | Ariana Albano | Róbert Langer
Source: Advanced Materials (article, 2018-03-15)
Field: Materials Science — Polymers and Plastics

Design and Synthesis of Waterborne Polyurethanes Waterborne polyurethanes (WBPUs) have attracted increasing attention in a wide range of industrial applications because of their versatile properties as well as ecofriendly nature. Although extensive research has been carried out on WBPU synthesis, the roles of some of the key synthesis components remain unclear. In this study, through systematically controlling and fine tuning the precursor compositions and reaction conditions, over 300 WBPUs are synthesized. This research enables the roles of several key components that govern WBPU physicochemical properties and ultimately the potential WBPU applications to be identified. Using hair styling as an example, it is demonstrated that only the WBPUs with an optimal range of properties (e.g., Young's modulus >150 MPa, elongation at break: 15-300%, moisture uptake <10%) can achieve strong styling performance. To further improve the natural-feel sensory benefits in the final styling products, a number of fatty acids with different carbon chain lengths or unsaturation levels are incorporated into WBPUs. Among the ten fatty acids studied, linoleic acid is identified as the most preferred additive. Both in vitro and in vivo testing demonstrate that WBPUs with optimal properties are promising materials for developing strong, long-lasting styling products with natural feel. Polyurethane ; Degree of unsaturation ; Materials science ; Carbon chain ; Elongation ; Polymer science ; Fatty acid ; Nanotechnology ; Chemical engineering ; Organic chemistry ; Composite material ; Polymer chemistry ; Chemistry Polymer composites and self-healing ; Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization
Authors: Yentl Swolfs | Ignace Verpoest | Larissa Gorbatikh
Source: International Materials Reviews (article, 2018-04-25)
Field: Materials Science — Polymers and Plastics

Recent advances in fibre-hybrid composites: materials selection, opportunities and applications Fibre-hybrid composites are composed of two or more fibre types in a matrix. Such composites offer more design freedom than non-hybrid composites. The aim is often to alleviate the drawbacks of one of the fibre types while keeping the benefits of the other. The hybridisation can also lead to synergetic effects or to properties that neither of the constituents possess. Even though fibre-hybrid composites are attractive, they also pose more challenges in terms of materials selection than conventional, single fibre type composites. This review analyses the mechanisms for synergetic effects provides guidance on the fibre and matrix selection and describes recent opportunities and trends. It finishes by describing the current applications, and by contrasting how the industrial use is different from the academic research. Composite material ; Selection (genetic algorithm) ; Materials science ; Matrix (chemical analysis) ; Material selection ; Composite number ; Computer science ; Artificial intelligence Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Cellular and Composite Structures