jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Trevon Badloe | Inki Kim | Yeseul Kim | Joohoon Kim | Junsuk Rho
Source: Advanced Science (article, 2021-09-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrically Tunable Bifocal Metalens with Diffraction‐Limited Focusing and Imaging at Visible Wavelengths Tunable optical devices powered by metasurfaces provide a new path for functional planar optics. In particular, lenses with tunable focal lengths can play a key role in various fields with applications in imaging, displays, and augmented and virtual reality devices. Here, the authors demonstrate an electrically controllable bifocal metalens at visible wavelengths by incorporating a metasurface designed to focus light at two different focal lengths, with liquid crystals to actively manipulate the focal length of the metalens through the application of an external bias. By utilizing hydrogenated amorphous silicon that is optimized to provide an extremely low extinction coefficient in the visible regime, the metalens is highly efficient with measured focusing efficiencies of around 44%. They numerically design and experimentally realize and characterize tunable focusing and demonstrate electrically tunable active imaging at visible wavelengths using the bifocal metalens combined with liquid crystals. Diffraction limited focusing and imaging is verified through the analysis of the measured optical intensities at the focal points and the modulation transfer function. The bifocal metalens is used to demonstrate electrically modulated focus switching between the two designed focal planes, to display images of positive and negative target objects. Optics ; Focal length ; Materials science ; Wavelength ; Planar ; Diffraction ; Optoelectronics ; Optical transfer function ; Cardinal point ; Focus (optics) ; Physics ; Computer science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Energy Harvesting in Wireless Networks
Authors: David Montes de Oca Zapiain | James A. Stewart | Rémi Dingreville
Source: npj Computational Materials (article, 2021-01-04)
Field: Materials Science — Materials Chemistry

Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods Abstract The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships. Field (mathematics) ; Computer science ; Phase field models ; Artificial intelligence ; Machine learning ; Phase (matter) ; Spinodal decomposition ; Surrogate model ; Autoregressive model ; Artificial neural network ; Algorithm ; Mathematics Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Sharath Kandambeth | Vinayak S. Kale | Osama Shekhah | Husam N. Alshareef | Mohamed Eddaoudi
Source: Advanced Energy Materials (article, 2021-05-05)
Field: Materials Science — Materials Chemistry

2D Covalent‐Organic Framework Electrodes for Supercapacitors and Rechargeable Metal‐Ion Batteries Abstract Covalent‐organic frameworks (COFs) represent a new frontier of crystalline porous organic materials with framework structures in 2D or 3D domains, which make them promising for many applications. Herein, the fundamental structural design aspects of 2D‐COFs are reviewed, which position them as suitable electrodes for electrochemical energy storage. The ordered π–π stacked arrangement of the organic building blocks in juxtaposed layers provides a pathway for efficient electronic charge transport; the 2D structure provides a pathway for enhanced ionic diffusion, which enhances ionic transport. Importantly, the tunable pore size enables 2D‐COFs to accommodate mobile ions with different sizes and charges, positioning them as prospect materials for various types of batteries. Distinctively, the ability to functionalize their pore system with a periodic array of redox active species, enriching their potential redox chemistry, provides a pathway to control the redox and capacitive contributions to the charge storage mechanism. The strong covalently linked framework backbone of COFs is an additional merit for achieving long cycle life, and stability against the “leaching out” problem of active molecules in strong electrolytes as observed in other organic materials applied in energy storage devices. Supercapacitor ; Electrochemical energy storage ; Materials science ; Nanotechnology ; Ionic bonding ; Energy storage ; Electrochemistry ; Electrolyte ; Covalent bond ; Electrode ; Redox ; Ion ; Chemistry ; Organic chemistry Covalent Organic Framework Applications ; Advanced battery technologies research ; Advanced Battery Materials and Technologies
Authors: Lwazi Ndlwana | Naledi Raleie | K. Mogolodi Dimpe | Hezron F.O. Ogutu | Ekemena O. Oseghe | Machawe M. Motsa | Titus A.M. Msagati | Bhekie B. Mamba
Source: Materials (review, 2021-09-06)
Field: Materials Science — Materials Chemistry

Sustainable Hydrothermal and Solvothermal Synthesis of Advanced Carbon Materials in Multidimensional Applications: A Review The adoption of green technology is very important to protect the environment and thus there is a need for improving the existing methods for the fabrication of carbon materials. As such, this work proposes to discuss, interrogate, and propose viable hydrothermal, solvothermal, and other advanced carbon materials synthesis methods. The synthesis approaches for advanced carbon materials to be interrogated will include the synthesis of carbon dots, carbon nanotubes, nitrogen/titania-doped carbons, graphene quantum dots, and their nanocomposites with solid/polymeric/metal oxide supports. This will be performed with a particular focus on microwave-assisted solvothermal and hydrothermal synthesis due to their favourable properties such as rapidity, low cost, and being green/environmentally friendly. These methods are regarded as important for the current and future synthesis and modification of advanced carbon materials for application in energy, gas separation, sensing, and water treatment. Simultaneously, the work will take cognisance of methods reducing the fabrication costs and environmental impact while enhancing the properties as a direct result of the synthesis methods. As a direct result, the expectation is to impart a significant contribution to the scientific body of work regarding the improvement of the said fabrication methods. Materials science ; Nanotechnology ; Carbon fibers ; Graphene ; Environmentally friendly ; Fabrication ; Hydrothermal circulation ; Hydrothermal synthesis ; Nanocomposite ; Chemical engineering ; Composite number ; Composite material ; Engineering Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Advanced Photocatalysis Techniques
Authors: Jinxing Li | Hiroya Nishikawa | Junichi Kougo | Junchen Zhou | Shuqi Dai | Wentao Tang | Xiuhu Zhao | Yuki Hisai | Mingjun Huang | Satoshi Aya
Source: Science Advances (article, 2021-04-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Development of ferroelectric nematic fluids with giant-ε dielectricity and nonlinear optical properties Superhigh-ε materials that exhibit exceptionally high dielectric permittivity are recognized as potential candidates for a wide range of next-generation photonic and electronic devices. In general, achieving a high-ε state requires low material symmetry, as most known high-ε materials are symmetry-broken crystals. There are few reports on fluidic high-ε dielectrics. Here, we demonstrate how small molecules with high polarity, enabled by rational molecular design and machine learning analyses, enable the development of superhigh-ε fluid materials (dielectric permittivity, ε > 10<sup>4</sup>) with strong second harmonic generation and macroscopic spontaneous polar ordering. The polar structures are confirmed to be identical for all the synthesized materials. Furthermore, adapting this strategy to high-molecular weight systems allows us to generalize this approach to polar polymeric materials, creating polar soft matters with spontaneous symmetry breaking. Polar ; Ferroelectricity ; Dielectric ; Photonics ; Nonlinear system ; Permittivity ; Second-harmonic generation ; Nonlinear optics ; Liquid crystal Liquid Crystal Research Advancements ; Dielectric materials and actuators ; Advanced Materials and Mechanics
Authors: Ava P. Soleimany | Alexander Amini | Samuel Goldman | Daniela Rus | Sangeeta N. Bhatia | Connor W. Coley
Source: ACS Central Science (article, 2021-07-27)
Field: Materials Science — Materials Chemistry

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery While neural networks achieve state-of-the-art performance for many molecular modeling and structure-property prediction tasks, these models can struggle with generalization to out-of-domain examples, exhibit poor sample efficiency, and produce uncalibrated predictions. In this paper, we leverage advances in evidential deep learning to demonstrate a new approach to uncertainty quantification for neural network-based molecular structure-property prediction at no additional computational cost. We develop both evidential 2D message passing neural networks and evidential 3D atomistic neural networks and apply these networks across a range of different tasks. We demonstrate that evidential uncertainties enable (1) calibrated predictions where uncertainty correlates with error, (2) sample-efficient training through uncertainty-guided active learning, and (3) improved experimental validation rates in a retrospective virtual screening campaign. Our results suggest that evidential deep learning can provide an efficient means of uncertainty quantification useful for molecular property prediction, discovery, and design tasks in the chemical and physical sciences. Leverage (statistics) ; Artificial intelligence ; Computer science ; Artificial neural network ; Property (philosophy) ; Machine learning ; Deep learning ; Range (aeronautics) ; Generalization ; Deep neural networks ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zhongkang Han | Debalaya Sarker | Runhai Ouyang | Aliaksei Mazheika | Yi Gao | Sergey V. Levchenko
Source: Nature Communications (article, 2021-03-23)
Field: Materials Science — Materials Chemistry

Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence Single-atom-alloy catalysts (SAACs) have recently become a frontier in catalysis research. Simultaneous optimization of reactants' facile dissociation and a balanced strength of intermediates' binding make them highly efficient catalysts for several industrially important reactions. However, discovery of new SAACs is hindered by lack of fast yet reliable prediction of catalytic properties of the large number of candidates. We address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Besides consistently predicting efficiency of the experimentally studied SAACs, we identify more than 200 yet unreported promising candidates. Some of these candidates are more stable and efficient than the reported ones. We have also introduced a novel approach to a qualitative analysis of complex symbolic regression models based on the data-mining method subgroup discovery. Our study demonstrates the importance of data analytics for avoiding bias in catalysis design, and provides a recipe for finding best SAACs for various applications. Computer science ; Parameterized complexity ; Catalysis ; Analytics ; Dissociation (chemistry) ; Atom (system on chip) ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Data mining ; Materials science ; Algorithm ; Organic chemistry ; Parallel computing Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Hanyan Wu | Chi‐Yuan Yang | Qifan Li | Nagesh B. Kolhe | Xenofon Strakosas | Marc‐Antoine Stoeckel | Ziang Wu | Wen‐Long Jin | Marios Savvakis | Renee Kroon | Deyu Tu | Han Young Woo | Magnus Berggren | Samson A. Jenekhe | Simone Fabiano
Source: Advanced Materials (article, 2021-10-20)
Field: Materials Science — Polymers and Plastics

Influence of Molecular Weight on the Organic Electrochemical Transistor Performance of Ladder‐Type Conjugated Polymers Organic electrochemical transistors (OECTs) hold promise for developing a variety of high-performance (bio-)electronic devices/circuits. While OECTs based on p-type semiconductors have achieved tremendous progress in recent years, n-type OECTs still suffer from low performance, hampering the development of power-efficient electronics. Here, it is demonstrated that fine-tuning the molecular weight of the rigid, ladder-type n-type polymer poly(benzimidazobenzophenanthroline) (BBL) by only one order of magnitude (from 4.9 to 51 kDa) enables the development of n-type OECTs with record-high geometry-normalized transconductance (g<sub>m,norm</sub> ≈ 11 S cm<sup>-1</sup> ) and electron mobility × volumetric capacitance (µC* ≈ 26 F cm<sup>-1</sup> V<sup>-1</sup> s<sup>-1</sup> ), fast temporal response (0.38 ms), and low threshold voltage (0.15 V). This enhancement in OECT performance is ascribed to a more efficient intermolecular charge transport in high-molecular-weight BBL than in the low-molecular-weight counterpart. OECT-based complementary inverters are also demonstrated with record-high voltage gains of up to 100 V V<sup>-1</sup> and ultralow power consumption down to 0.32 nW, depending on the supply voltage. These devices are among the best sub-1 V complementary inverters reported to date. These findings demonstrate the importance of molecular weight in optimizing the OECT performance of rigid organic mixed ionic-electronic conductors and open for a new generation of power-efficient organic (bio-)electronic devices. Materials science ; Transistor ; Transconductance ; Nanotechnology ; Electrochemistry ; Capacitance ; Organic electronics ; Optoelectronics ; Semiconductor ; Voltage ; Electrical engineering ; Electrode ; Chemistry Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Organic Electronics and Photovoltaics
Authors: Francesco Rigodanza | Max Burian | Francesca Arcudi | Luka Đorđević∞ | Heinz Amenitsch | Maurizio Prato
Source: Nature Communications (article, 2021-05-11)
Field: Materials Science — Materials Chemistry

Snapshots into carbon dots formation through a combined spectroscopic approach The design of novel carbon dots with ad hoc properties requires a comprehensive understanding of their formation mechanism, which is a complex task considering the number of variables involved, such as reaction time, structure of precursors or synthetic protocol employed. Herein, we systematically investigated the formation of carbon nanodots by tracking structural, chemical and photophysical features during the hydrothermal synthesis. We demonstrate that the formation of carbon nanodots consists of 4 consecutive steps: (i) aggregation of small organic molecules, (ii) formation of a dense core with an extended shell, (iii) collapse of the shell and (iv) aromatization of the core. In addition, we provide examples of routes towards tuning the core-shell design, synthesizing five novel carbon dots that all consist of an electron-dense core covered by an amine rich ligand shell. Nanodot ; Carbon fibers ; Nanotechnology ; Shell (structure) ; Molecule ; Materials science ; Hydrothermal circulation ; Chemistry ; Chemical physics ; Chemical engineering ; Organic chemistry Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Kalliopi Kousi | Chenyang Tang | Ian S. Metcalfe | Dragos Neagu
Source: Small (review, 2021-03-31)
Field: Materials Science — Materials Chemistry

Emergence and Future of Exsolved Materials Abstract Supported nanoparticle systems have received increased attention over the last decades because of their potential for high activity levels when applied to chemical conversions, although, because of their nanoscale nature, they tend to exhibit problems with long‐term durability. Over the last decade, the discovery of the so‐called exsolution concept has addressed many of these challenges and opened many other opportunities to material design by providing a relatively simple, single‐step, synthetic pathway to produce supported nanoparticles that combine high stability against agglomeration and poisoning with high activity across multiple areas of application. Here, the trends that define the development of the exsolution concept are reviewed in terms of design, functionality, tunability, and applicability. To support this, the number of studies dedicated to both fundamental and application‐related studies, as well as the types of metallic nanoparticles and host or support lattices employed, are examined. Exciting future directions of research are also highlighted. Nanotechnology ; Economies of agglomeration ; Computer science ; Nanoparticle ; Biochemical engineering ; Materials science ; Data science ; Engineering ; Chemical engineering Catalytic Processes in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Liu Yun | Oladapo Christopher Esan | Zhefei Pan | Liang An
Source: Energy and AI (article, 2021-01-24)
Field: Materials Science — Materials Chemistry

Machine learning for advanced energy materials The screening of advanced materials coupled with the modeling of their quantitative structural-activity relationships has recently become one of the hot and trending topics in energy materials due to the diverse challenges, including low success probabilities, high time consumption, and high computational cost associated with the traditional methods of developing energy materials. Following this, new research concepts and technologies to promote the research and development of energy materials become necessary. The latest advancements in artificial intelligence and machine learning have therefore increased the expectation that data-driven materials science would revolutionize scientific discoveries towards providing new paradigms for the development of energy materials. Furthermore, the current advances in data-driven materials engineering also demonstrate that the application of machine learning technology would not only significantly facilitate the design and development of advanced energy materials but also enhance their discovery and deployment. In this article, the importance and necessity of developing new energy materials towards contributing to the global carbon neutrality are presented. A comprehensive introduction to the fundamentals of machine learning is also provided, including open-source databases, feature engineering, machine learning algorithms, and analysis of machine learning model. Afterwards, the latest progress in data-driven materials science and engineering, including alkaline ion battery materials, photovoltaic materials, catalytic materials, and carbon dioxide capture materials, is discussed. Finally, relevant clues to the successful applications of machine learning and the remaining challenges towards the development of advanced energy materials are highlighted. Software deployment ; Computer science ; Artificial intelligence ; Machine learning ; Energy consumption ; Systems engineering ; Data science ; Engineering ; Software engineering ; Electrical engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: Robert B. Wexler | Gopalakrishnan Sai Gautam | Ellen B. Stechel | Emily A. Carter
Source: Journal of the American Chemical Society (article, 2021-08-16)
Field: Materials Science — Materials Chemistry

Factors Governing Oxygen Vacancy Formation in Oxide Perovskites The control of oxygen vacancy (V<sub>O</sub>) formation is critical to advancing multiple metal-oxide-perovskite-based technologies. We report the construction of a compact linear model for the neutral V<sub>O</sub> formation energy in ABO<sub>3</sub> perovskites that reproduces, with reasonable fidelity, Hubbard-<i>U</i>-corrected density functional theory calculations based on the state-of-the-art, strongly constrained and appropriately normed exchange-correlation functional. We obtain a mean absolute error of 0.45 eV for perovskites stable at 298 K, an accuracy that holds across a large, electronically diverse set of ABO<sub>3</sub> perovskites. Our model considers perovskites containing alkaline-earth metals (Ca, Sr, and Ba) and lanthanides (La and Ce) on the A-site and 3<i>d</i> transition metals (Ti, V, Cr, Mn, Fe, Co, and Ni) on the B-site in six different crystal systems (cubic, tetragonal, orthorhombic, hexagonal, rhombohedral, and monoclinic) common to perovskites. Physically intuitive metrics easily extracted from existing experimental thermochemical data or via inexpensive quantum mechanical calculations, including crystal bond dissociation energies and (solid phase) reduction potentials, are key components of the model. Beyond validation of the model against known experimental trends in materials used in solid oxide fuel cells, the model yields new candidate perovskites not contained in our training data set, such as (Bi,Y)(Fe,Co)O<sub>3</sub>, which we predict may have favorable thermochemical water-splitting properties. The confluence of sufficient accuracy, efficiency, and interpretability afforded by our model not only facilitates high-throughput computational screening for any application that requires the precise control of V<sub>O</sub> concentrations but also provides a clear picture of the dominant physics governing V<sub>O</sub> formation in metal-oxide perovskites. Chemistry ; Perovskite (structure) ; Tetragonal crystal system ; Orthorhombic crystal system ; Density functional theory ; Oxide ; Vacancy defect ; Crystal structure ; Chemical physics ; Crystallography ; Computational chemistry Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials