jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qidong Lin | Liang Zhang | Yaolong Zhang | Bin Jiang
Source: Journal of Chemical Theory and Computation (article, 2021-04-27)
Field: Materials Science — Materials Chemistry

Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials Neural network (NN) potential energy surfaces (PESs) have been widely used in atomistic simulations with ab initio accuracy. While constructing NN PESs, their training data points are often sampled by molecular dynamics trajectories. This strategy can be however inefficient for reactive systems involving rare events. Here, we develop an uncertainty-driven active learning strategy to automatically and efficiently generate high-dimensional NN-based reactive potentials, taking a gas-surface reaction as an example. The difference between two independent NN models is used as a simple and differentiable uncertainty metric, allowing us to quickly search in the uncertainty space and place new samples at which the PES is less reliable. By interfacing this algorithm with the first-principles simulation package, we demonstrate that a globally accurate NN potential of the H<sub>2</sub> + Ag(111) system can be constructed with merely ∼150 data points. This PES can be further refined to describe H<sub>2</sub> dissociation on Ag(100) by adding ∼130 more configurations on this facet. The entire process is completely automatic and self-terminated once the relative error criterion is fulfilled. Impressively, data points sampled by this uncertainty-driven strategy are substantially fewer than by the traditional trajectory-based sampling. The final NN PES not only converges well the quantum dissociation probability of the molecule but also well-reproduces the phonon properties of the substrate and is capable of describing surface temperature effects. These results show the potential of this active learning approach in developing high-dimensional NN reactive potentials in gas and condensed phases. Computer science ; Artificial neural network ; Interfacing ; Potential energy surface ; Ab initio ; Chemical space ; Data point ; Quantum ; Statistical physics ; Biological system ; Artificial intelligence ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Davide Tisi | Linfeng Zhang | Riccardo Bertossa | Handong Wang | Roberto Car | Stefano Baroni
Source: Physical review. B./Physical review. B (article, 2021-12-13)
Field: Materials Science — Materials Chemistry

Heat transport in liquid water from first-principles and deep neural network simulations We compute the thermal conductivity of water within linear response theory from equilibrium molecular dynamics simulations, by adopting two different approaches. In one, the potential energy surface (PES) is derived on the fly from the electronic ground state of density functional theory (DFT) and the corresponding analytical expression is used for the energy flux. In the other, the PES is represented by a deep neural network (DNN) trained on DFT data, whereby the PES has an explicit local decomposition and the energy flux takes a particularly simple expression. By virtue of a gauge invariance principle, established by Marcolongo, Umari, and Baroni, the two approaches should be equivalent if the PES were reproduced accurately by the DNN model. We test this hypothesis by calculating the thermal conductivity, at the GGA (PBE) level of theory, using the direct formulation and its DNN proxy, finding that both approaches yield the same conductivity, in excess of the experimental value by approximately 60%. Besides being numerically much more efficient than its direct DFT counterpart, the DNN scheme has the advantage of being easily applicable to more sophisticated DFT approximations, such as meta-GGA and hybrid functionals, for which it would be hard to derive analytically the expression of the energy flux. We find in this way that a DNN model, trained on meta-GGA (SCAN) data, reduces the deviation from experiment of the predicted thermal conductivity by about 50%, leaving the question open as to whether the residual error is due to deficiencies of the functional, to a neglect of nuclear quantum effects in the atomic dynamics, or, likely, to a combination of the two. Thermal conductivity ; Density functional theory ; Artificial neural network ; Statistical physics ; Physics ; Applied mathematics ; Mathematics ; Thermodynamics ; Computer science ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: P.J. Garcı́a Nieto | Esperanza García–Gonzalo | José P. Paredes–Sánchez
Source: Neural Computing and Applications (article, 2021-07-20)
Field: Materials Science — Materials Chemistry

Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques Abstract This study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model. Mars Exploration Program ; Elastic net regularization ; Multivariate adaptive regression splines ; Ridge ; Computer science ; Artificial intelligence ; Regression ; Support vector machine ; Algorithm ; Materials science ; Machine learning ; Regression analysis ; Bayesian multivariate linear regression ; Feature selection ; Geology ; Mathematics ; Physics ; Statistics Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Energy Load and Power Forecasting
Authors: Puneet Mishra | Dário Passos
Source: Infrared Physics & Technology (article, 2021-08-04)
Field: Chemistry — Analytical Chemistry

Deep calibration transfer: Transferring deep learning models between infrared spectroscopy instruments Calibration transfer (CT) is required when a model developed on one instrument needs to be transferred and used on a new instrument. Several methods are available in the chemometrics domain to transfer the multivariate calibrations developed using modelling techniques such as partial least-square regression. However, recently deep learning (DL) models are gaining popularity to model spectral data. The traditional multivariate CT methods are not suitable to transfer a deep learning model which is based on neural networks architectures. Hence, this study presents the concept of deep calibration transfer (CT) for transferring a DL model made on one instrument onto a new instrument. The deep CT is based on the concept of transfer learning from the DL domain. To show it, two different CT cases are presented. The first case is the CT between benchtop FT-NIR (Fourier Transform Near Infrared) instruments, and the second case is the CT between handheld NIR (Near Infrared) instruments. In both the demonstrated cases, the transfer was performed standard-free i.e., no common standard samples were used to estimate any transfer function. The results showed that with deep CT, the DL models made on one instrument can be easily adapted and transferred to a new instrument. The main benefit of the deep CT is that it is a standard free approach and does not require any standard sample measurements. Such a standard free approach to transfer DL models between instruments can support a widespread sharing of chemometric DL models between the scientific practitioners. Calibration ; Computer science ; Transfer of learning ; Transfer function ; Deep learning ; Partial least squares regression ; Artificial intelligence ; Chemometrics ; Near-infrared spectroscopy ; Machine learning ; Optics ; Mathematics ; Statistics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: James L. Webb | Luca Troise | Nikolaj Winther Hansen | Christoffer Olsson | Adam M. Wojciechowski | Jocelyn Achard | Ovidiu Brinza | Robert Staacke | Michael Kieschnick | Jan Meijer | Axel Thielscher | Jean‐Françóis Perrier | Kirstine Berg‐Sørensen | Alexander Huck | Ulrik L. Andersen
Source: Scientific Reports (article, 2021-01-28)
Field: Materials Science — Materials Chemistry

Detection of biological signals from a live mammalian muscle using an early stage diamond quantum sensor The ability to perform noninvasive and non-contact measurements of electric signals produced by action potentials is essential in biomedicine. A key method to do this is to remotely sense signals by the magnetic field they induce. Existing methods for magnetic field sensing of mammalian tissue, used in techniques such as magnetoencephalography of the brain, require cryogenically cooled superconducting detectors. These have many disadvantages in terms of high cost, flexibility and limited portability as well as poor spatial and temporal resolution. In this work we demonstrate an alternative technique for detecting magnetic fields generated by the current from action potentials in living tissue using nitrogen vacancy centres in diamond. With 50 pT/[Formula: see text] sensitivity, we show the first measurements of magnetic sensing from mammalian tissue with a diamond sensor using mouse muscle optogenetically activated with blue light. We show these proof of principle measurements can be performed in an ordinary, unshielded lab environment and that the signal can be easily recovered by digital signal processing techniques. Although as yet uncompetitive with probe electrophysiology in terms of sensitivity, we demonstrate the feasibility of sensing action potentials via magnetic field in mammals using a diamond quantum sensor, as a step towards microscopic imaging of electrical activity in a biological sample using nitrogen vacancy centres in diamond. Diamond ; Stage (stratigraphy) ; Quantum ; Computer science ; Computational biology ; Biology ; Materials science ; Physics ; Paleontology ; Quantum mechanics Diamond and Carbon-based Materials Research ; Analytical Chemistry and Sensors ; Carbon Nanotubes in Composites
Authors: Joana C. Antunes | Joana Domingues | Catarina S. Miranda | A. Francisca G. Silva | Natália C. Homem | M. T. Pessoa de Amorim | Helena P. Felgueiras
Source: Marine Drugs (review, 2021-06-23)
Field: Materials Science — Biomaterials

Bioactivity of Chitosan-Based Particles Loaded with Plant-Derived Extracts for Biomedical Applications: Emphasis on Antimicrobial Fiber-Based Systems Marine-derived chitosan (CS) is a cationic polysaccharide widely studied for its bioactivity, which is mostly attached to its primary amine groups. CS is able to neutralize reactive oxygen species (ROS) from the microenvironments in which it is integrated, consequently reducing cell-induced oxidative stress. It also acts as a bacterial peripheral layer hindering nutrient intake and interacting with negatively charged outer cellular components, which lead to an increase in the cell permeability or to its lysis. Its biocompatibility, biodegradability, ease of processability (particularly in mild conditions), and chemical versatility has fueled CS study as a valuable matrix component of bioactive small-scaled organic drug-delivery systems, with current research also showcasing CS's potential within tridimensional sponges, hydrogels and sutures, blended films, nanofiber sheets and fabric coatings. On the other hand, renewable plant-derived extracts are here emphasized, given their potential as eco-friendly radical scavengers, microbicidal agents, or alternatives to antibiotics, considering that most of the latter have induced bacterial resistance because of excessive and/or inappropriate use. Loading them into small-scaled particles potentiates a strong and sustained bioactivity, and a controlled release, using lower doses of bioactive compounds. A pH-triggered release, dependent on CS's protonation/deprotonation of its amine groups, has been the most explored stimulus for that control. However, the use of CS derivatives, crosslinking agents, and/or additional stabilization processes is enabling slower release rates, following extract diffusion from the particle matrix, which can find major applicability in fiber-based systems within ROS-enriched microenvironments and/or spiked with microbes. Research on this is still in its infancy. Yet, the few published studies have already revealed that the composition, along with an adequate drug release rate, has an important role in controlling an existing infection, forming new tissue, and successfully closing a wound. A bioactive finishing of textiles has also been promoting high particle infiltration, superior washing durability, and biological response. Chemistry ; Chitosan ; Biocompatibility ; Drug delivery ; Antimicrobial ; Controlled release ; Reactive oxygen species ; Biophysics ; Nanofiber ; Polysaccharide ; Chemical engineering ; Combinatorial chemistry ; Nanotechnology ; Organic chemistry ; Materials science ; Biochemistry Nanocomposite Films for Food Packaging ; Electrospun Nanofibers in Biomedical Applications ; Antimicrobial agents and applications
Authors: Donghui Zhang | Jingjing Liu | Qi Chen | Weinan Jiang | Yibing Wang | Jiayang Xie | Kaiqian Ma | Chao Shi | Haodong Zhang | Minzhang Chen | Jianglin Wan | Pengcheng Ma | Jingcheng Zou | Wenjing Zhang | Feng Zhou | Runhui Liu
Source: Nature Communications (article, 2021-11-03)
Field: Materials Science — Surfaces, Coatings and Films

A sandcastle worm-inspired strategy to functionalize wet hydrogels Hydrogels have been extensively used in many fields. Current synthesis of functional hydrogels requires incorporation of functional molecules either before or during gelation via the pre-organized reactive site along the polymer chains within hydrogels, which is tedious for polymer synthesis and not flexible for different types of hydrogels. Inspired by sandcastle worm, we develop a simple one-step strategy to functionalize wet hydrogels using molecules bearing an adhesive dibutylamine-DOPA-lysine-DOPA tripeptide. This tripeptide can be easily modified with various functional groups to initiate diverse types of polymerizations and provide functional polymers with a terminal adhesive tripeptide. Such functional molecules enable direct modification of wet hydrogels to acquire biological functions such as antimicrobial, cell adhesion and wound repair. The strategy has a tunable functionalization degree and a stable attachment of functional molecules, which provides a tool for direct and convenient modification of wet hydrogels to provide them with diverse functions and applications. Self-healing hydrogels ; Tripeptide ; Polymer ; Surface modification ; Functional polymers ; Materials science ; Biomolecule ; Molecule ; Nanotechnology ; Combinatorial chemistry ; Chemistry ; Polymer chemistry ; Polymerization ; Organic chemistry ; Peptide ; Biochemistry Polymer Surface Interaction Studies ; Silk-based biomaterials and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Demelza Wright | Qianqi Lin | Dénes Berta | Tamás Földes | Andreas Wagner | Jack Griffiths | Charlie Readman | Edina Rosta | Erwin Reisner | Jeremy J. Baumberg
Source: Nature Catalysis (article, 2021-02-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Mechanistic study of an immobilized molecular electrocatalyst by in situ gap-plasmon-assisted spectro-electrochemistry Terpyridine ; Electrocatalyst ; Catalysis ; Plasmon ; Electrochemistry ; Redox ; Molecule ; Nanotechnology ; Surface plasmon resonance ; Surface-enhanced Raman spectroscopy ; Nickel ; Chemistry ; Raman spectroscopy ; Materials science ; Density functional theory ; Raman scattering ; Photochemistry ; Electrode ; Nanoparticle ; Metal ; Inorganic chemistry ; Physical chemistry ; Organic chemistry ; Computational chemistry ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Molecular Junctions and Nanostructures ; Electrochemical Analysis and Applications
Authors: Dié Tang | Lun Zhang | Xiaoyu Zhang | Liqiang Xu | Ke Li | Aimin Zhang
Source: ACS Applied Materials & Interfaces (article, 2021-12-29)
Field: Materials Science — Polymers and Plastics

Bio-Mimetic Actuators of a Photothermal-Responsive Vitrimer Liquid Crystal Elastomer with Robust, Self-Healing, Shape Memory, and Reconfigurable Properties Soft actuators with apparent uniqueness in exhibiting complex shape morphing are highly desirable for artificial intelligence applications. However, for the majority of soft actuators, in general, it is challenging to achieve versatility, durability, and configurability simultaneously. Enormous works are devoted to meet the multifunctional smart actuators, to little effect. Herein, self-healing and bio-mimetic smart actuators are proposed based on azobenzene chromophores and dynamic disulfide bonds. Benefiting from the dynamic and drivable vitrimer liquid crystal elastomer (V-LCE) materials, a series of actuators with single or compound dynamic three-dimensional structures were fabricated, which were capable of double-stimuli response and complex "bionic" motions, such as the blooming of a flower, grasping and loosening an object, and so forth. Moreover, these flexible actuators showed fascinating properties, such as high robustness, excellent elasticity-plasticity shape-memory properties (<i>R</i><sub>f</sub> and <i>R</i><sub>r</sub> are close to 100%), easily reconfigurable property, and self-healing. This smart V-LCE provides a guideline to design and fabricate soft versatility actuators, which has prospects for developing smart bionic and artificial intelligence devices. Actuator ; Materials science ; Smart material ; Shape-memory alloy ; Soft robotics ; Self-healing ; Morphing ; Liquid crystal ; Elastomer ; Neuromorphic engineering ; Artificial muscle ; Biomimetics ; Nanotechnology ; Azobenzene ; Shape-memory polymer ; Robustness (evolution) ; Stretchable electronics ; Shape change ; Computer science ; Electronics ; Artificial intelligence ; Polymer ; Composite material ; Optoelectronics ; Artificial neural network ; Engineering ; Electrical engineering Polymer composites and self-healing ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: Hao Yang | Zhaoxuan Feng | Xiaoling Teng | Lu Guan | Han Hu | Mingbo Wu
Source: InfoMat (article, 2021-03-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Three‐dimensional printing of high‐mass loading electrodes for energy storage applications Abstract Nanostructured materials afford a promising potential for many energy storage applications because of their extraordinary electrochemical properties. However, the remarkable electrochemical energy storage performance could only be harvested at a relatively low mass‐loading via the traditional electrode fabrication process, and the scale of these materials into commercial‐level mass‐loading remains a daunting challenge because the ion diffusion kinetics deteriorates rapidly along with the increased thickness of the electrodes. Very recently, three‐dimensional (3D) printing, a promising additive manufacturing technology, has been considered as an emerging method to address the aforementioned issues where the 3D printed electrodes could possess elaborately regulated architectures and rationally organized porosity. As a result, the outstanding electrochemical performance has been widely observed in energy storage devices made of 3D printed electrodes of high‐mass loading. In this review, we systemically introduce the basic working principles of various 3D printing technologies and their practical applications to manufacture high‐mass loading electrodes for energy storage devices. Challenges and perspectives in 3D printing technologies for the construction of electrodes at the current stage are also outlined, aiming to offer some useful opinions for further development for this prosperous field. image 3D printing ; Energy storage ; Fabrication ; Electrode ; Materials science ; Nanotechnology ; Electrochemical energy storage ; Porosity ; Mass storage ; Process (computing) ; Electrochemistry ; Process engineering ; Computer science ; Mechanical engineering ; Supercapacitor ; Composite material ; Engineering ; Computer hardware ; Chemistry Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Dan‐Ning Hu | Min Zeng | Yafei Sun | Jinying Yuan | Yen Wei
Source: SusMat (article, 2021-05-25)
Field: Materials Science — Biomaterials

Cellulose‐based hydrogels regulated by supramolecular chemistry Abstract Supramolecular hydrogels based on cellulose attract increasing attention because of their novel structures and broad potential applications. In this review, hydrogels composed of cellulose are summarized according to category of supramolecular interactions in the networks including hydrogen bonds, electrostatic interactions, host‐guest interactions, and others. Supramolecular cellulose‐based hydrogels constructed by noncovalent bonding usually exhibit environmental friendliness, designing flexibility and diverse functions, and their properties are variable with incorporating different interactions in hydrogel networks. Moreover, with proper structures and networks, the supramolecular cellulose‐based hydrogels are adaptable in diverse fields of research and practical applications, such as self‐healing, shape memory, drug delivery, and some other renewable/sustainable materials. The future developments and challenges of the supramolecular hydrogels based on cellulose are discussed as well. Self-healing hydrogels ; Supramolecular chemistry ; Cellulose ; Hydrogen bond ; Flexibility (engineering) ; Materials science ; Nanotechnology ; Non-covalent interactions ; Supramolecular polymers ; Polymer science ; Chemistry ; Polymer chemistry ; Molecule ; Organic chemistry Supramolecular Self-Assembly in Materials ; Hydrogels: synthesis, properties, applications ; Polydiacetylene-based materials and applications
Authors: Enrique García‐Bordejé | Ana M. Benito | Wolfgang K. Maser
Source: Advances in Colloid and Interface Science (review, 2021-04-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graphene aerogels via hydrothermal gelation of graphene oxide colloids: Fine-tuning of its porous and chemical properties and catalytic applications Graphene ; Hydrothermal circulation ; Chemical engineering ; Materials science ; Porosity ; Oxide ; Colloid ; Catalysis ; Nanotechnology ; Chemistry ; Composite material ; Organic chemistry ; Metallurgy Supercapacitor Materials and Fabrication ; Nanomaterials for catalytic reactions ; Graphene research and applications
Authors: Niyou Wang | Jerry Ying Hsi Fuh | S. Thameem Dheen | A. Senthil Kumar
Source: Bio-Design and Manufacturing (review, 2021-01-03)
Field: Materials Science — Materials Chemistry

Synthesis methods of functionalized nanoparticles: a review Nanotechnology ; Biocompatibility ; Computer science ; Nanoparticle ; Materials science ; Biochemical engineering ; Engineering Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Laser-Ablation Synthesis of Nanoparticles