jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Theresia Arbring Sjöström | Magnus Berggren | Erik O. Gabrielsson | Per Olof Janson | David J. Poxson | Maria Seitanidou | Daniel T. Simon
Source: Advanced Materials Technologies (article, 2018-03-12)
Field: Materials Science — Polymers and Plastics

A Decade of Iontronic Delivery Devices Abstract In contrast to electronic systems, biology rarely uses electrons as the signal to regulate functions, but rather ions and molecules of varying size. Due to the unique combination of both electronic and ionic/molecular conductivity in conjugated polymers and polyelectrolytes, these materials have emerged as an excellent tool for translating signals between these two realms, hence the field of organic bioelectronics. Since organic bioelectronics relies on the electron‐mediated transport and compensation of ions (or the ion‐mediated transport and compensation of electrons), a great deal of effort has been devoted to the development of so‐called “iontronic” components to effect precise substance delivery/transport, that is, components where ions are the dominant charge carrier and where ionic–electronic coupling defines device functionality. This effort has resulted in a range of technologies including ionic resistors, diodes, transistors, and basic logic circuits for the precisely controlled transport and delivery of biologically active chemicals. This Research News article presents a brief overview of some of these “ion pumping” technologies, how they have evolved over the last decade, and a discussion of applications in vitro, in vivo, and in plantae. Bioelectronics ; Nanotechnology ; Ionic bonding ; Materials science ; Ion ; Chemistry ; Biosensor Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Neuroscience and Neural Engineering
Authors: J. L. Niedziela | Dipanshu Bansal | Andrew F. May | Jingxuan Ding | Tyson Lanigan-Atkins | G. Ehlers | D. L. Abernathy | Ayman Said | Olivier Delaire
Source: Nature Physics (article, 2018-09-27)
Field: Materials Science — Materials Chemistry

Selective breakdown of phonon quasiparticles across superionic transition in CuCrSe2 Anharmonicity ; Phonon ; Condensed matter physics ; Quasiparticle ; Physics ; Thermal conduction ; Thermal conductivity ; Scattering ; Phase transition ; Phonon scattering ; Quantum mechanics ; Superconductivity Advanced Thermoelectric Materials and Devices ; Material Dynamics and Properties ; High-pressure geophysics and materials
Authors: Sergio Carrasco
Source: Biosensors (review, 2018-10-16)
Field: Chemistry — Inorganic Chemistry

Metal-Organic Frameworks for the Development of Biosensors: A Current Overview This review focuses on the fabrication of biosensors using metal-organic frameworks (MOFs) as recognition and/or transducer elements. A brief introduction discussing the importance of the development of new biosensor schemes is presented, describing these coordination polymers, their properties, applications, and the main advantages and drawbacks for the final goal. The increasing number of publications regarding the characteristics of these materials and the new micro- and nanofabrication techniques allowing the preparation of more accurate, robust, and sensitive biosensors are also discussed. This work aims to offer a new perspective from the point of view of materials science compared to other reviews focusing on the transduction mechanism or the nature of the analyte. A few examples are discussed depending on the starting materials, the integration of the MOF as a part of the biosensor and, in a deep detail, the fabrication procedure. Biosensor ; Nanotechnology ; Computer science ; Fabrication ; Biochemical engineering ; Materials science ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Advanced Nanomaterials in Catalysis
Authors: Wei Chen | Aik Rui Tan | Andrew L. Ferguson
Source: The Journal of Chemical Physics (article, 2018-05-23)
Field: Materials Science — Materials Chemistry

Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design Auto-associative neural networks ("autoencoders") present a powerful nonlinear dimensionality reduction technique to mine data-driven collective variables from molecular simulation trajectories. This technique furnishes explicit and differentiable expressions for the nonlinear collective variables, making it ideally suited for integration with enhanced sampling techniques for accelerated exploration of configurational space. In this work, we describe a number of sophistications of the neural network architectures to improve and generalize the process of interleaved collective variable discovery and enhanced sampling. We employ circular network nodes to accommodate periodicities in the collective variables, hierarchical network architectures to rank-order the collective variables, and generalized encoder-decoder architectures to support bespoke error functions for network training to incorporate prior knowledge. We demonstrate our approach in blind collective variable discovery and enhanced sampling of the configurational free energy landscapes of alanine dipeptide and Trp-cage using an open-source plugin developed for the OpenMM molecular simulation package. Computer science ; Sampling (signal processing) ; Autoencoder ; Variable (mathematics) ; Artificial intelligence ; Artificial neural network ; Theoretical computer science ; Machine learning ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Gihan Panapitiya | Guillermo Avendaño-Franco | Pengju Ren | Xiaodong Wen | Yongwang Li | James P. Lewis
Source: Journal of the American Chemical Society (article, 2018-11-08)
Field: Materials Science — Materials Chemistry

Machine-Learning Prediction of CO Adsorption in Thiolated, Ag-Alloyed Au Nanoclusters We propose a machine-learning model, based on the random-forest method, to predict CO adsorption in thiolate protected nanoclusters. Two phases of feature selection and training, based initially on the Au<sub>25</sub> nanocluster, are utilized in our model. One advantage to a machine-learning approach is that correlations in defined features disentangle relationships among the various structural parameters. For example, in Au<sub>25</sub>, we find that features based on the distribution of Ag atoms relative to the CO adsorption site are the most important in predicting adsorption energies. Our machine-learning model is easily extended to other Au-based nanoclusters, and we demonstrate predictions about CO adsorption on Ag-alloyed Au<sub>36</sub> and Au<sub>133</sub> nanoclusters. Nanoclusters ; Adsorption ; Chemistry ; Random forest ; Feature (linguistics) ; Feature selection ; Machine learning ; Nanotechnology ; Physical chemistry ; Computer science ; Materials science ; Organic chemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Machine Learning in Materials Science
Authors: Shirin Saberianpour | Morteza Heidarzadeh | Mohammad Hossein Geranmayeh | Hossein Hosseinkhani | Reza Rahbarghazi‬ | Mohammad Nouri
Source: Journal of Biological Engineering (review, 2018-12-01)
Field: Materials Science — Biomaterials

Tissue engineering strategies for the induction of angiogenesis using biomaterials Angiogenesis is touted as a fundamental procedure in the regeneration and restoration of different tissues. The induction of de novo blood vessels seems to be vital to yield a successful cell transplantation rate loaded on various scaffolds. Scaffolds are natural or artificial substances that are considered as one of the means for delivering, aligning, maintaining cell connection in a favor of angiogenesis. In addition to the potential role of distinct scaffold type on vascularization, the application of some strategies such as genetic manipulation, and conjugation of pro-angiogenic factors could intensify angiogenesis potential. In the current review, we focused on the status of numerous scaffolds applicable in the field of vascular biology. Also, different strategies and priming approaches useful for the induction of pro-angiogenic signaling pathways were highlighted. Angiogenesis ; Regeneration (biology) ; Tissue engineering ; Scaffold ; Cell biology ; Regenerative medicine ; Transplantation ; Neovascularization ; Biology ; Computational biology ; Biomedical engineering ; Stem cell ; Medicine ; Cancer research ; Surgery Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; 3D Printing in Biomedical Research
Authors: Manuel Iglesias | Luis A. Oro
Source: Chemical Society Reviews (review, 2018-01-01)
Field: Chemistry — Organic Chemistry

A leap forward in iridium–NHC catalysis: new horizons and mechanistic insights This review summarises the most recent advances in Ir-NHC catalysis while revisiting all the classical reactions in which this type of catalyst has proved to be active. The influence of the ligand system and, in particular, the impact of the NHC ligand on the activity and selectivity of the reaction have been analysed, accompanied by an examination of the great variety of catalytic cycles hitherto reported. The reaction mechanisms so far proposed are described and commented on for each individual process. Moreover, some general considerations that attempt to explain the influence of the NHC from a mechanistic viewpoint are presented at the end of the review. The first sections are dedicated to the most widely explored reactions that use Ir-NHCs, i.e., hydrogenation and transfer hydrogenation, for which a general overview that tries to compile all the Ir-NHC catalysts hitherto reported for these processes is provided. The next sections deal with hydrogen borrowing, hydrosilylation, water splitting, dehydrogenation (of alcohols, alkanes, aminoboranes and formic acid), hydrogen isotope exchange (HIE), signal amplification by reversible exchange and C-H bond functionalisation (silylation and borylation). The last section compiles a series of reactions somewhat less explored for Ir-NHC catalysts that include the hydroalkynylation of imines, hydroamination, diboration of olefins, hydrolysis and methanolysis of silanes, arylation of aldehydes with boronic acids, addition of aroyl chlorides to alkynes, visible light driven reactions, isomerisation of alkenes, asymmetric intramolecular allylic amination and reactions that employ heterometallic catalysts containing at least one Ir-NHC unit. Iridium ; New horizons ; Chemistry ; Catalysis ; Nanotechnology ; Astrobiology ; Biochemical engineering ; Physics ; Materials science ; Organic chemistry ; Aerospace engineering ; Engineering Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis ; Nanomaterials for catalytic reactions
Authors: Aldo Glielmo | Claudio Zeni | Alessandro De Vita
Source: Physical review. B./Physical review. B (article, 2018-05-24)
Field: Materials Science — Materials Chemistry

Efficient nonparametric<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-body force fields from machine learning The authors present a scheme to construct classical $n$-body force fields using Gaussian Process (GP) Regression, appropriately mapped over explicit n-body functions (M-FFs). The procedure is possible, and will yield accurate forces, whenever prior knowledge allows to restrict the interactions to a finite order $n$, so that the ``universal approximator'' resolving power of standard GPs or Neural Networks is not needed. Under these conditions, the proposed construction preserves flexibility of training, systematically improvable accuracy, and a clear framework for validation of the underlying machine learning technique. Moreover, the M-FFs are as fast as classical parametrized potentials, since they avoid lengthy summations over database entries or weight parameters. Kernel (algebra) ; Nonparametric statistics ; Series (stratigraphy) ; Algorithm ; Gaussian ; Gaussian process ; Character (mathematics) ; Exponential function ; Computer science ; Machine learning ; Artificial intelligence ; Mathematics ; Discrete mathematics ; Geometry ; Mathematical analysis ; Physics ; Statistics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Protein Structure and Dynamics
Authors: Yanchun Deng | Yong Sik Ok | Dinesh Mohan | Charles U. Pittman | Xiaomin Dou
Source: Environmental Research (article, 2018-11-22)
Field: Materials Science — Materials Chemistry

Carbamazepine removal from water by carbon dot-modified magnetic carbon nanotubes Adsorption ; Magnetite ; Chemical engineering ; Carbon nanotube ; Materials science ; Carbon fibers ; Fourier transform infrared spectroscopy ; Activated carbon ; Chemistry ; Raman spectroscopy ; Composite number ; Organic chemistry ; Nanotechnology ; Composite material Covalent Organic Framework Applications ; Carbon and Quantum Dots Applications ; Nanomaterials for catalytic reactions
Authors: Alán Aspuru‐Guzik | Roland Lindh | Markus Reiher
Source: ACS Central Science (review, 2018-02-06)
Field: Materials Science — Materials Chemistry

The Matter Simulation (R)evolution To date, the program for the development of methods and models for atomistic and continuum simulation directed toward chemicals and materials has reached an incredible degree of sophistication and maturity. Currently, one can witness an increasingly rapid emergence of advances in computing, artificial intelligence, and robotics. This drives us to consider the future of computer simulation of matter from the molecular to the human length and time scales in a radical way that deliberately dares to go beyond the foreseeable next steps in any given discipline. This perspective article presents a view on this future development that we believe is likely to become a reality during our lifetime. Sophistication ; Computer science ; Witness ; Perspective (graphical) ; Artificial intelligence ; Maturity (psychological) ; Robotics ; Data science ; Political science ; Sociology ; Law ; Robot ; Social science Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Computational Drug Discovery Methods
Authors: Penghao Li | Peng Li | Matthew R. Ryder | Zhichang Liu | Charlotte L. Stern | Omar K. Farha | J. Fraser Stoddart
Source: Angewandte Chemie International Edition (article, 2018-12-11)
Field: Chemistry — Inorganic Chemistry

Interpenetration Isomerism in Triptycene‐Based Hydrogen‐Bonded Organic Frameworks We describe an example of "interpenetration isomerism" in three-dimensional hydrogen-bonded organic frameworks. By exploiting the crystallization conditions for a peripherally extended triptycene H<sub>6</sub> PET, we can modulate the interpenetration of the assembled frameworks, yielding a two-fold interpenetrated structure PETHOF-1 and a five-fold interpenetrated structure PETHOF-2 as interpenetration isomers. In PETHOF-1, two individual nets are related by inversion symmetry and form an interwoven topology with a large guest-accessible volume of about 80 %. In PETHOF-2, five individual nets are related by translational symmetry and are stacked in an alternating fashion. The activated materials show permanent porosity with Brunauer-Emmett-Teller surface areas exceeding 1100 m<sup>2</sup> g<sup>-1</sup> . Synthetic control over the framework interpenetration could serve as a new strategy to construct complex supramolecular architectures from simple organic building blocks. Triptycene ; Supramolecular chemistry ; Crystallization ; Materials science ; Hydrogen bond ; Porosity ; Crystallography ; Translational symmetry ; Topology (electrical circuits) ; Crystal structure ; Nanotechnology ; Chemistry ; Molecule ; Organic chemistry ; Composite material ; Mathematics ; Combinatorics ; Physics ; Condensed matter physics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials
Authors: Peter Bjørn Jørgensen | Murat Mesta | Suranjan Shil | Juan Maria García Lastra | Karsten W. Jacobsen | Kristian S. Thygesen | Mikkel N. Schmidt
Source: The Journal of Chemical Physics (article, 2018-06-06)
Field: Materials Science — Materials Chemistry

Machine learning-based screening of complex molecules for polymer solar cells Polymer solar cells admit numerous potential advantages including low energy payback time and scalable high-speed manufacturing, but the power conversion efficiency is currently lower than for their inorganic counterparts. In a Phenyl-C_61-Butyric-Acid-Methyl-Ester (PCBM)-based blended polymer solar cell, the optical gap of the polymer and the energetic alignment of the lowest unoccupied molecular orbital (LUMO) of the polymer and the PCBM are crucial for the device efficiency. Searching for new and better materials for polymer solar cells is a computationally costly affair using density functional theory (DFT) calculations. In this work, we propose a screening procedure using a simple string representation for a promising class of donor-acceptor polymers in conjunction with a grammar variational autoencoder. The model is trained on a dataset of 3989 monomers obtained from DFT calculations and is able to predict LUMO and the lowest optical transition energy for unseen molecules with mean absolute errors of 43 and 74 meV, respectively, without knowledge of the atomic positions. We demonstrate the merit of the model for generating new molecules with the desired LUMO and optical gap energies which increases the chance of finding suitable polymers by more than a factor of five in comparison to the randomised search used in gathering the training set. HOMO/LUMO ; Polymer solar cell ; Polymer ; Materials science ; Band gap ; Acceptor ; Density functional theory ; Molecule ; Computer science ; Chemistry ; Computational chemistry ; Physics ; Optoelectronics ; Organic chemistry ; Quantum mechanics ; Composite material Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Computational Drug Discovery Methods
Authors: Nadja A. Simeth | Stefano Crespi | Maurizio Fagnoni | Burkhard König
Source: Journal of the American Chemical Society (article, 2018-02-06)
Field: Materials Science — Materials Chemistry

Tuning the Thermal Isomerization of Phenylazoindole Photoswitches from Days to Nanoseconds The growing interest in light-driven molecular switches and optical oscillators led to the development of molecules that are able to interconvert from a stable to a metastable configuration upon photochemical triggering and to return to the thermodynamically stable form as soon as the light stimulus is removed. Controlling a wide range of back-isomerization lifetimes in the dark is a crucial goal for potential application of these compounds such as molecular machines. We herein present a novel class of easily synthesizable azo photoswitches based on the arylazoindole core. Most notably, minimal modifications of the core, such as methylation, dramatically change the Z-to-E thermal isomerization rate from days (Me in position 1) to the nanosecond range (Me in position 2). Moreover, fine-tuning of the Z-to-E lifetimes can be achieved choosing a proper dimethyl sulfoxide-water (or buffered water) solvent mixture. The photochemical and thermal mechanisms have been elucidated by a thorough computational and spectroscopic analysis. This allowed to detect three different pathways of thermal isomerization and to identify the hydrazone tautomer of the phenylazoindole as the major actor in the fast Z-E thermal isomerization of the NH-substituted switch in protic media. Isomerization ; Chemistry ; Photochemistry ; Nanosecond ; Molecular switch ; Tautomer ; Metastability ; Thermal ; Molecule ; Stereochemistry ; Organic chemistry ; Laser Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Luminescence and Fluorescent Materials
Authors: Zhilei Wang | Yoshitaka Adachi
Source: Materials Science and Engineering A (article, 2018-12-15)
Field: Materials Science — Materials Chemistry

Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach Microstructure ; Inverse ; Property (philosophy) ; Ultimate tensile strength ; Process (computing) ; Materials science ; Computer science ; Work (physics) ; Inverse problem ; Mechanical engineering ; Machine learning ; Composite material ; Mathematics ; Engineering ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Masaki Yamawaki | Masato Ohnishi | Shenghong Ju | Junichiro Shiomi
Source: Science Advances (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Multifunctional structural design of graphene thermoelectrics by Bayesian optimization Materials development often confronts a dilemma as it needs to satisfy multifunctional, often conflicting, demands. For example, thermoelectric conversion requires high electrical conductivity, a high Seebeck coefficient, and low thermal conductivity, despite the fact that these three properties are normally closely correlated. Nanostructuring techniques have been shown to break the correlations to some extent; however, optimal design has been a major challenge due to the extraordinarily large degrees of freedom in the structures. By taking graphene nanoribbons (GNRs) as a representative thermoelectric material, we carried out structural optimization by alternating multifunctional (phonon and electron) transport calculations and Bayesian optimization to resolve the trade-off. As a result, we have achieved multifunctional structural optimization with an efficiency more than five times that achieved by random search. The obtained GNRs with optimized antidots significantly enhance the thermoelectric figure of merit by up to 11 times that of the pristine GNR. Knowledge of the optimal structure further provides new physical insights that independent tuning of electron and phonon transport properties can be realized by making use of zigzag edge states and aperiodic nanostructuring. The demonstrated optimization framework is also useful for other multifunctional problems in various applications. Thermoelectric materials ; Thermoelectric effect ; Materials science ; Zigzag ; Thermal conductivity ; Seebeck coefficient ; Figure of merit ; Bayesian optimization ; Graphene ; Nanotechnology ; Phonon ; Condensed matter physics ; Physics ; Mathematical optimization ; Optoelectronics ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials