jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Panagiotis Mallis | Alkiviadis Kostakis | Catherine Stavropoulos‐Giokas | Εfstathios Michalopoulos
Source: Bioengineering (review, 2020-12-10)
Field: Materials Science — Biomaterials

Future Perspectives in Small-Diameter Vascular Graft Engineering The increased demands of small-diameter vascular grafts (SDVGs) globally has forced the scientific society to explore alternative strategies utilizing the tissue engineering approaches. Cardiovascular disease (CVD) comprises one of the most lethal groups of non-communicable disorders worldwide. It has been estimated that in Europe, the healthcare cost for the administration of CVD is more than 169 billion €. Common manifestations involve the narrowing or occlusion of blood vessels. The replacement of damaged vessels with autologous grafts represents one of the applied therapeutic approaches in CVD. However, significant drawbacks are accompanying the above procedure; therefore, the exploration of alternative vessel sources must be performed. Engineered SDVGs can be produced through the utilization of non-degradable/degradable and naturally derived materials. Decellularized vessels represent also an alternative valuable source for the development of SDVGs. In this review, a great number of SDVG engineering approaches will be highlighted. Importantly, the state-of-the-art methodologies, which are currently employed, will be comprehensively presented. A discussion summarizing the key marks and the future perspectives of SDVG engineering will be included in this review. Taking into consideration the increased number of patients with CVD, SDVG engineering may assist significantly in cardiovascular reconstructive surgery and, therefore, the overall improvement of patients' life. Vascular Medicine ; Computer science ; Medicine ; Internal medicine ; Intensive care medicine Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Infectious Aortic and Vascular Conditions
Authors: Tural Khudiyev | Jung Tae Lee | Jason R. Cox | Eric Argentieri | Gabriel Loke | Rodger Yuan | Grace H. Noel | Ryoichi Tatara | Yu Yang | Frannie Logan | John D. Joannopoulos | Yang Shao‐Horn | Yoel Fink
Source: Advanced Materials (article, 2020-11-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

100 m Long Thermally Drawn Supercapacitor Fibers with Applications to 3D Printing and Textiles Supercapacitor fibers, with short charging times, long cycle lifespans, and high power densities, hold promise for powering flexible fabric-based electronics. To date, however, only short lengths of functioning fiber supercapacitors have been produced. The primary goal of this study is to introduce a supercapacitor fiber that addresses the remaining challenges of scalability, flexibility, cladding impermeability, and performance at length. This is achieved through a top-down fabrication method in which a macroscale preform is thermally drawn into a fully functional energy-storage fiber. The preform consists of five components: thermally reversible porous electrode and electrolyte gels; conductive polymer and copper microwire current collectors; and an encapsulating hermetic cladding. This process produces 100 m of continuous functional supercapacitor fiber, orders of magnitude longer than any previously reported. In addition to flexibility (5 mm radius of curvature), moisture resistance (100 washing cycles), and strength (68 MPa), these fibers have an energy density of 306 μWh cm<sup>-2</sup> at 3.0 V and ≈100% capacitance retention over 13 000 cycles at 1.6 V. To demonstrate the utility of this fiber, it is machine-woven and used as filament for 3D printing. Supercapacitor ; Materials science ; Cladding (metalworking) ; Composite material ; Fabrication ; Fiber ; Capacitance ; Electrode ; Energy storage ; Electrolyte ; Electrical conductor ; Nanotechnology Supercapacitor Materials and Fabrication ; Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Luis A. Miccio | Gustavo A. Schwartz
Source: Polymer (article, 2020-03-02)
Field: Materials Science — Materials Chemistry

From chemical structure to quantitative polymer properties prediction through convolutional neural networks Polymer ; Convolutional neural network ; Glass transition ; Dispersion (optics) ; Computer science ; Artificial neural network ; Work (physics) ; Biological system ; Materials science ; Artificial intelligence ; Thermodynamics ; Composite material ; Physics ; Optics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Zhi‐Wen Zhao | Marcos del Cueto | Yun Geng | Alessandro Troisi
Source: Chemistry of Materials (article, 2020-08-25)
Field: Materials Science — Materials Chemistry

Effect of Increasing the Descriptor Set on Machine Learning Prediction of Small Molecule-Based Organic Solar Cells In this work, we analyzed a data set formed by 566 donor/acceptor pairs, which are part of organic solar cells recently reported. We explored the effect of different descriptors in machine learning (ML) models to predict the power conversion efficiency (PCE) of these cells. The investigated descriptors are classified into two main categories: structural (topology properties) and physical descriptors (energy levels, molecular size, light absorption, and mixing properties). In line with previous observations, ML predictions are more accurate when using both structural and physical descriptors, as opposed to only using one of them. We observed that ML predictions are also improved by using larger and more varied data sets. Importantly, the structural descriptors are the ones contributing the most to the ML models. Some physical properties are highly correlated with PCE, although they do not improve notably the ML prediction accuracy as they carry information already encoded in the structural descriptors. Given that various descriptors have significantly different computational costs, the analysis presented here can be used as a guide to construct ML models that maximize predictive power and minimize computational costs for screening large sets of candidates. Molecular descriptor ; Biological system ; Set (abstract data type) ; Computer science ; Artificial intelligence ; Organic solar cell ; Machine learning ; Construct (python library) ; Predictive modelling ; Data mining ; Pattern recognition (psychology) ; Quantitative structure–activity relationship ; Photovoltaic system ; Engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Te Ma | Satoru Tsuchikawa | Tetsuya Inagaki
Source: Computers and Electronics in Agriculture (article, 2020-08-15)
Field: Chemistry — Analytical Chemistry

Rapid and non-destructive seed viability prediction using near-infrared hyperspectral imaging coupled with a deep learning approach Hyperspectral imaging ; Principal component analysis ; Artificial intelligence ; Support vector machine ; Computer science ; RGB color model ; Convolutional neural network ; Pattern recognition (psychology) ; Interpretability ; Machine learning Spectroscopy and Chemometric Analyses ; Agricultural Engineering and Mechanization ; Leaf Properties and Growth Measurement
Authors: Behrooz Semnani | Jeremy Flannery | Rubayet Al Maruf | Michal Bajcsy
Source: Light Science & Applications (article, 2020-02-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spin-preserving chiral photonic crystal mirror Chirality refers to a geometric phenomenon in which objects are not superimposable on their mirror image. Structures made of nanoscale chiral elements can exhibit chiroptical effects, such as dichroism for left- and right-handed circularly polarized light, which makes these structures highly suitable for applications ranging from quantum information processing and quantum optics to circular dichroism spectroscopy and molecular recognition. At the same time, strong chiroptical effects have been challenging to achieve even in synthetic optical media, and chiroptical effects for light with normal incidence have been speculated to be prohibited in thin, lossless quasi-two-dimensional structures. Here, we report an experimental realization of a giant chiroptical effect in a thin monolithic photonic crystal mirror. Unlike conventional mirrors, our mirror selectively reflects only one spin state of light while preserving its handedness, with a near-unity level of circular dichroism. The operational principle of the photonic crystal mirror relies on guided-mode resonance (GMR) with a simultaneous excitation of leaky transverse electric (TE-like) and transverse magnetic (TM-like) Bloch modes in the photonic crystal slab. Such modes are not reliant on the suppression of radiative losses through long-range destructive interference, and even small areas of the photonic crystal exhibit robust circular dichroism. Despite its simplicity, the mirror strongly outperforms earlier reported structures and, contrary to a prevailing notion, demonstrates that near-unity reflectivity contrast for opposite helicities is achievable in a quasi-two-dimensional structure. Circular dichroism ; Photonic crystal ; Physics ; Optics ; Chirality (physics) ; Metamaterial ; Guided-mode resonance ; Dichroism ; Magnetic circular dichroism ; Circular polarization ; Optoelectronics ; Symmetry breaking ; Chemistry ; Grating ; Quantum mechanics ; Crystallography ; Diffraction grating ; Spectral line Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research
Authors: Limin Shi | Chenxu Yan | Zhiqian Guo | Weijie Chi | Jingle Wei | Weimin Liu | Xiaogang Liu | He Tian | Weihong Zhu
Source: Nature Communications (article, 2020-02-07)
Field: Materials Science — Materials Chemistry

De novo strategy with engineering anti-Kasha/Kasha fluorophores enables reliable ratiometric quantification of biomolecules Fluorescence-based technologies have revolutionized in vivo monitoring of biomolecules. However, significant technical hurdles in both probe chemistry and complex cellular environments have limited the accuracy of quantifying these biomolecules. Herein, we report a generalizable engineering strategy for dual-emission anti-Kasha-active fluorophores, which combine an integrated fluorescein with chromene (IFC) building block with donor-π-acceptor structural modification. These fluorophores exhibit an invariant near-infrared Kasha emission from the S<sub>1</sub> state, while their anti-Kasha emission from the S<sub>2</sub> state at around 520 nm can be finely regulated via a spirolactone open/closed switch. We introduce bio-recognition moieties to IFC structures, and demonstrate ratiometric quantification of cysteine and glutathione in living cells and animals, using the ratio (S<sub>2</sub>/S<sub>1</sub>) with the S<sub>1</sub> emission as a reliable internal reference signal. This de novo strategy of tuning anti-Kasha-active properties expands the in vivo ratiometric quantification toolbox for highly accurate analysis in both basic life science research and clinical applications. Biomolecule ; Nanotechnology ; Chemistry ; Fluorescence ; Biophysics ; Förster resonance energy transfer ; Materials science ; Physics ; Biology Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Sulfur Compounds in Biology
Authors: Viktor Zaverkin | Johannes Kästner
Source: Journal of Chemical Theory and Computation (article, 2020-07-16)
Field: Materials Science — Materials Chemistry

Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials Machine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost ab initio accuracy and the computational efficiency of empirical potentials. In this work, we propose a machine learning method for constructing high-dimensional potential energy surfaces based on feed-forward neural networks. As input to the neural network, we propose an extendable invariant local molecular descriptor constructed from geometric moments. Their formulation via pairwise distance vectors and tensor contractions allows a very efficient implementation on graphical processing units (GPUs). The atomic species is encoded in the molecular descriptor, which allows the restriction to one neural network for the training of all atomic species in the data set. We demonstrate that the accuracy of the developed approach in representing both chemical and configurational spaces is comparable to the one of several established machine learning models. Due to its high accuracy and efficiency, the proposed machine-learned potentials can be used for any further tasks, for example, the optimization of molecular geometries, the calculation of rate constants, or molecular dynamics. Computer science ; Scalability ; Gaussian ; Artificial intelligence ; Machine learning ; Gaussian process ; Computational chemistry ; Chemistry ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Joonyoung F. Joung | Minhi Han | Minseok Jeong | Sungnam Park
Source: Scientific Data (article, 2020-09-08)
Field: Materials Science — Materials Chemistry

Experimental database of optical properties of organic compounds Experimental databases on the optical properties of organic chromophores are important for the implementation of data-driven chemistry using machine learning. Herein, we present a series of experimental data including various optical properties such as the first absorption and emission maximum wavelengths and their bandwidths (full width at half maximum), extinction coefficient, photoluminescence quantum yield, and fluorescence lifetime. A database of 20,236 data points was developed by collecting the optical properties of organic compounds already reported in the literature. A dataset of 7,016 unique organic chromophores in 365 solvents or in solid state is available in CSV format. Chromophore ; Molar absorptivity ; Photoluminescence ; 3D optical data storage ; Quantum yield ; Absorption (acoustics) ; Fluorescence ; Wavelength ; Attenuation coefficient ; Materials science ; Database ; Extinction (optical mineralogy) ; Photochemistry ; Analytical Chemistry (journal) ; Optoelectronics ; Chemistry ; Computer science ; Optics ; Organic chemistry ; Mineralogy ; Physics Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Conducting polymers and applications
Authors: Ruimin Ma | Yamil J. Colón | Tengfei Luo
Source: ACS Applied Materials & Interfaces (article, 2020-07-02)
Field: Chemistry — Inorganic Chemistry

Transfer Learning Study of Gas Adsorption in Metal–Organic Frameworks Metal-organic frameworks (MOFs) are a class of materials promising for gas adsorption due to their highly tunable nanoporous structures and host-guest interactions. While machine learning (ML) has been leveraged to aid the design or screen of MOFs for different purposes, the needs of big data are not always met, limiting the applicability of ML models trained against small data sets. In this work, we introduce an inductive transfer learning technique to improve the accuracy and applicability of ML models trained with a small amount of MOF adsorption data. This technique leverages potentially shareable knowledge from a source task to improve the models on the target tasks. As demonstrations, a deep neural networks (DNNs) trained on H<sub>2</sub> adsorption data with 13 506 MOF structures at 100 bar and 243 K is used as the source task. When transferring knowledge from the source task to H<sub>2</sub> adsorption at 100 bar and 130 K (one target task), the average predictive accuracy on target tasks was improved from 0.960 (direct training) to 0.991 (transfer learning), and transfer learning works in 89.3% of the cases. We also tested transfer learning across different gas species (i.e., from H<sub>2</sub> to CH<sub>4</sub>), with an average predictive accuracy of CH<sub>4</sub> adsorption being improved from 0.935 (direct training) to 0.980 (transfer learning), and transfer learning works in 82.0% of the cases. More importantly, transfer learning is shown to effectively improve the models on the target tasks with low accuracy from direct training. However, when transferring the knowledge from the source task to Xe/Kr adsorption, the transfer learning does not improve the predictive accuracy significantly and even makes it worse in ∼50.0% of the cases, which is attributed to the lack of common descriptors that is key to the underlying knowledge. Nanoporous ; Transfer of learning ; Task (project management) ; Adsorption ; Computer science ; Artificial neural network ; Metal-organic framework ; Machine learning ; Limiting ; Artificial intelligence ; Materials science ; Bar (unit) ; Transfer (computing) ; Deep learning ; Knowledge transfer ; Nanotechnology ; Systems engineering ; Knowledge management ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Tianyi Chen | Christopher Foo | Shik Chi Edman Tsang
Source: Chemical Science (review, 2020-12-18)
Field: Materials Science — Materials Chemistry

Interstitial and substitutional light elements in transition metals for heterogeneous catalysis The addition of foreign element dopants to monometallic nanoparticle catalysts is of great importance in industrial applications. Both substitutional and interstitial doping of pure metallic phases can give profound effects such as altering electronic and transport properties, lattice parameters, phase transitions, and consequently various physicochemical properties. For transition metal catalysts, this often leads to changes in catalytic activity and selectivity. This article provides an overview of the recent developments regarding the catalytic properties and characterisation of such systems. In particular, the structure-activity relationship for a number of important chemical reactions is summarised and the future prospects of this area are also explored. Transition metal ; Catalysis ; Doping ; Transition (genetics) ; Materials science ; Nanotechnology ; Chemical physics ; Chemistry ; Optoelectronics ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Ammonia Synthesis and Nitrogen Reduction
Authors: Kirill Shmilovich | Rachael A. Mansbach | Hythem Sidky | Olivia E. Dunne | Sayak Subhra Panda | John D. Tovar | Andrew L. Ferguson
Source: The Journal of Physical Chemistry B (article, 2020-03-17)
Field: Materials Science — Biomaterials

Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation Electronically active organic molecules have demonstrated great promise as novel soft materials for energy harvesting and transport. Self-assembled nanoaggregates formed from π-conjugated oligopeptides composed of an aromatic core flanked by oligopeptide wings offer emergent optoelectronic properties within a water-soluble and biocompatible substrate. Nanoaggregate properties can be controlled by tuning core chemistry and peptide composition, but the sequence-structure-function relations remain poorly characterized. In this work, we employ coarse-grained molecular dynamics simulations within an active learning protocol employing deep representational learning and Bayesian optimization to efficiently identify molecules capable of assembling pseudo-1D nanoaggregates with good stacking of the electronically active π-cores. We consider the DXXX-OPV3-XXXD oligopeptide family, where D is an Asp residue and OPV3 is an oligophenylenevinylene oligomer (1,4-distyrylbenzene), to identify the top performing XXX tripeptides within all 20<sup>3</sup> = 8000 possible sequences. By direct simulation of only 2.3% of this space, we identify molecules predicted to exhibit superior assembly relative to those reported in prior work. Spectral clustering of the top candidates reveals new design rules governing assembly. This work establishes new understanding of DXXX-OPV3-XXXD assembly, identifies promising new candidates for experimental testing, and presents a computational design platform that can be generically extended to other peptide-based and peptide-like systems. Tripeptide ; Conjugated system ; Oligopeptide ; Stacking ; Molecular dynamics ; Peptide ; Molecule ; Chemistry ; Nanotechnology ; Self-assembly ; Combinatorial chemistry ; Biomolecule ; Biological system ; Materials science ; Computational chemistry ; Polymer ; Organic chemistry ; Biology ; Biochemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Chemical Synthesis and Analysis
Authors: Laure Bourgeois | Yong Zhang | Zezhong Zhang | Yiqiang Chen | Nikhil V. Medhekar
Source: Nature Communications (article, 2020-03-06)
Field: Materials Science — Materials Chemistry

Transforming solid-state precipitates via excess vacancies Many phase transformations associated with solid-state precipitation look structurally simple, yet, inexplicably, take place with great difficulty. A classic case of difficult phase transformations is the nucleation of strengthening precipitates in high-strength lightweight aluminium alloys. Here, using a combination of atomic-scale imaging, simulations and classical nucleation theory calculations, we investigate the nucleation of the strengthening phase θ' onto a template structure in the aluminium-copper alloy system. We show that this transformation can be promoted in samples exhibiting at least one nanoscale dimension, with extremely high nucleation rates for the strengthening phase as well as for an unexpected phase. This template-directed solid-state nucleation pathway is enabled by the large influx of surface vacancies that results from heating a nanoscale solid. Template-directed nucleation is replicated in a bulk alloy as well as under electron irradiation, implying that this difficult transformation can be facilitated under the general condition of sustained excess vacancy concentrations. Nucleation ; Precipitation ; Materials science ; Phase (matter) ; Alloy ; Nanoscopic scale ; Vacancy defect ; Chemical physics ; Aluminium ; Atomic units ; Transformation (genetics) ; Crystallography ; Nanotechnology ; Thermodynamics ; Metallurgy ; Chemistry ; Physics Microstructure and mechanical properties ; Fusion materials and technologies ; Aluminum Alloy Microstructure Properties