jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuanang Bian | Shuai Zhang | Yunxuan Zhao | Run Shi | Tierui Zhang
Source: InfoMat (article, 2021-05-07)
Field: Materials Science — Materials Chemistry

Layered double hydroxide‐based photocatalytic materials toward renewable solar fuels production Abstract Photocatalysis is an ideal and promising green technology to drive numerous chemical reactions for valued chemicals production under very mild conditions, thereby providing solutions to global energy and environment issues related to burning fossil fuels. Over the past decade, layered double hydroxides (LDHs), as the members in two‐dimensional materials family, have attracted much attention due to their many advantages in photocatalysis, such as facile synthesis, low cost and powerful tunability of composition. In this review, we provide a synthetic overview of recent research advances of LDH‐based photocatalysts, with the main discussion of the design strategies to improve their photocatalytic performance, including component control, defect engineering, hybridization, and topological transformation. Structure‐performance correlations and tailor‐made material synthesis strategies are elaborated to discuss how to realize high‐performance LDH‐based photocatalysts for three important reactions (i.e., water splitting, CO 2 conversion, and N 2 reduction) to generate desirable solar fuels. Further, the remaining challenges and future perspectives of LDH‐based photocatalysts are summed up, aiming to inspire brand new solutions for pushing forward the development of LDH‐based photocatalysis. image Photocatalysis ; Hydroxide ; Layered double hydroxides ; Renewable energy ; Fossil fuel ; Nanotechnology ; Materials science ; Solar energy ; Process engineering ; Biochemical engineering ; Chemical engineering ; Catalysis ; Waste management ; Engineering ; Chemistry Layered Double Hydroxides Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Audrey Olivier | Michael D. Shields | Lori Graham‐Brady
Source: Computer Methods in Applied Mechanics and Engineering (article, 2021-08-18)
Field: Materials Science — Materials Chemistry

Bayesian neural networks for uncertainty quantification in data-driven materials modeling Uncertainty quantification ; Computer science ; Extrapolation ; Machine learning ; Artificial neural network ; Probabilistic logic ; Bayesian inference ; Inference ; Interpolation (computer graphics) ; Bayesian probability ; Artificial intelligence ; Algorithm ; Data mining ; Mathematics Machine Learning in Materials Science ; Probabilistic and Robust Engineering Design ; Model Reduction and Neural Networks
Authors: Caio G. Otoni | Henriette Monteiro Cordeiro de Azeredo | Bruno D. Mattos | Marco Beaumont | Daniel S. Côrrea | Orlando J. Rojas
Source: Advanced Materials (review, 2021-09-12)
Field: Materials Science — Biomaterials

The Food–Materials Nexus: Next Generation Bioplastics and Advanced Materials from Agri‐Food Residues The most recent strategies available for upcycling agri-food losses and waste (FLW) into functional bioplastics and advanced materials are reviewed and the valorization of food residuals are put in perspective, adding to the water-food-energy nexus. Low value or underutilized biomass, biocolloids, water-soluble biopolymers, polymerizable monomers, and nutrients are introduced as feasible building blocks for biotechnological conversion into bioplastics. The latter are demonstrated for their incorporation in multifunctional packaging, biomedical devices, sensors, actuators, and energy conversion and storage devices, contributing to the valorization efforts within the future circular bioeconomy. Strategies are introduced to effectively synthesize, deconstruct and reassemble or engineer FLW-derived monomeric, polymeric, and colloidal building blocks. Multifunctional bioplastics are introduced considering the structural, chemical, physical as well as the accessibility of FLW precursors. Processing techniques are analyzed within the fields of polymer chemistry and physics. The prospects of FLW streams and biomass surplus, considering their availability, interactions with water and thermal stability, are critically discussed in a near-future scenario that is expected to lead to next-generation bioplastics and advanced materials. Bioplastic ; Materials science ; Nexus (standard) ; Food packaging ; Nanotechnology ; Polymer science ; Waste management ; Engineering ; Mechanical engineering biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution
Authors: Zheqin Dong | Maja Vuckovac | Wenjuan Cui | Quan Zhou | Robin H. A. Ras | Pavel A. Levkin
Source: Advanced Materials (article, 2021-09-28)
Field: Materials Science — Surfaces, Coatings and Films

3D Printing of Superhydrophobic Objects with Bulk Nanostructure The rapid development of 3D printing (or additive manufacturing) technologies demands new materials with novel properties and functionalities. Superhydrophobic materials, owing to their ultralow water adhesion, self-cleaning, anti-biofouling, or superoleophilic properties are useful for myriad applications involving liquids. However, the majority of the methods for making superhydrophobic surfaces have been based on surface functionalization and coatings, which are challenging to apply to 3D objects. Additionally, these coatings are vulnerable to abrasion due to low mechanical stability and limited thickness. Here, a new materials concept and methodology for 3D printing of superhydrophobic macroscopic objects with bulk nanostructure and almost unlimited geometrical freedom is presented. The method is based on a specific ink composed of hydrophobic (meth)acrylate monomers and porogen solvents, which undergoes phase separation upon photopolymerization to generate inherently nanoporous and superhydrophobic structures. Using a desktop Digital Light Processing printer, superhydrophobic 3D objects with complex shapes are demonstrated, with ultralow and uniform water adhesion measured with scanning droplet adhesion microscopy. It is shown that the 3D-printed objects, owing to their nanoporous structure throughout the entire volume, preserve their superhydrophobicity upon wear damage. Finally, a superhydrophobic 3D-printed gas-permeable and water-repellent microfluidic device and a hierarchically structured 3D-printed super-oil-absorbent are demonstrated. Materials science ; Nanoporous ; 3D printing ; Nanostructure ; Nanotechnology ; Adhesion ; Photopolymer ; Microfluidics ; Biofouling ; 3d printed ; Acrylate ; Abrasion (mechanical) ; Superhydrophobic coating ; Inkwell ; Composite material ; Coating ; Copolymer ; Monomer ; Polymer ; Membrane Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Advanced Sensor and Energy Harvesting Materials
Authors: Pengju Wang | Yazhou Sun | Xiaoquan Shi | Huixing Shen | Haohao Ning | Haitao Liu
Source: Bio-Design and Manufacturing (review, 2021-01-04)
Field: Materials Science — Biomaterials

3D printing of tissue engineering scaffolds: a focus on vascular regeneration Tissue engineering is an emerging means for resolving the problems of tissue repair and organ replacement in regenerative medicine. Insufficient supply of nutrients and oxygen to cells in large-scale tissues has led to the demand to prepare blood vessels. Scaffold-based tissue engineering approaches are effective methods to form new blood vessel tissues. The demand for blood vessels prompts systematic research on fabrication strategies of vascular scaffolds for tissue engineering. Recent advances in 3D printing have facilitated fabrication of vascular scaffolds, contributing to broad prospects for tissue vascularization. This review presents state of the art on modeling methods, print materials and preparation processes for fabrication of vascular scaffolds, and discusses the advantages and application fields of each method. Specially, significance and importance of scaffold-based tissue engineering for vascular regeneration are emphasized. Print materials and preparation processes are discussed in detail. And a focus is placed on preparation processes based on 3D printing technologies and traditional manufacturing technologies including casting, electrospinning, and Lego-like construction. And related studies are exemplified. Transformation of vascular scaffolds to clinical application is discussed. Also, four trends of 3D printing of tissue engineering vascular scaffolds are presented, including machine learning, near-infrared photopolymerization, 4D printing, and combination of self-assembly and 3D printing-based methods. Tissue engineering ; Scaffold ; Regenerative medicine ; Biomedical engineering ; 3D printing ; Regeneration (biology) ; Nanotechnology ; Materials science ; Computer science ; Engineering ; Mechanical engineering ; Stem cell ; Biology Electrospun Nanofibers in Biomedical Applications ; 3D Printing in Biomedical Research ; Additive Manufacturing and 3D Printing Technologies
Authors: Olga Kononova | Tanjin He | Haoyan Huo | Amalie Trewartha | Elsa Olivetti | Gerbrand Ceder
Source: iScience (review, 2021-02-07)
Field: Materials Science — Materials Chemistry

Opportunities and challenges of text mining in materials research Research publications are the major repository of scientific knowledge. However, their unstructured and highly heterogenous format creates a significant obstacle to large-scale analysis of the information contained within. Recent progress in natural language processing (NLP) has provided a variety of tools for high-quality information extraction from unstructured text. These tools are primarily trained on non-technical text and struggle to produce accurate results when applied to scientific text, involving specific technical terminology. During the last years, significant efforts in information retrieval have been made for biomedical and biochemical publications. For materials science, text mining (TM) methodology is still at the dawn of its development. In this review, we survey the recent progress in creating and applying TM and NLP approaches to materials science field. This review is directed at the broad class of researchers aiming to learn the fundamentals of TM as applied to the materials science publications. Data science ; Nanotechnology ; Chemistry ; Engineering ethics ; Computer science ; Engineering ; Materials science Machine Learning in Materials Science ; Topic Modeling ; Natural Language Processing Techniques
Authors: Yongtae Kim | Youngsoo Kim | Charles Yang | Kundo Park | Grace X. Gu | Seunghwa Ryu
Source: npj Computational Materials (article, 2021-09-02)
Field: Materials Science — Materials Chemistry

Deep learning framework for material design space exploration using active transfer learning and data augmentation Abstract Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome the limitation also suffer from the weak predictive power on the unseen domain. In this study, we propose a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods. We demonstrate the potential of our framework with a grid composite optimization problem that has an astronomical number of possible design configurations. Results show that our proposed framework can provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training dataset size. Computer science ; Artificial neural network ; Artificial intelligence ; Transfer of learning ; Machine learning ; Generative grammar ; Domain (mathematical analysis) ; Deep learning ; Set (abstract data type) ; Range (aeronautics) ; Generative Design ; Grid ; Hyperparameter optimization ; Engineering ; Mathematics Machine Learning in Materials Science ; Topology Optimization in Engineering ; Advanced Multi-Objective Optimization Algorithms
Authors: Conor H. Sharp | Brandon C. Bukowski | Hongyu Li | Eric M. Johnson | Stefan Ilić | Amanda J. Morris | Dilip Gersappe | Randall Q. Snurr | John R. Morris
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Nanoconfinement and mass transport in metal–organic frameworks The ubiquity of metal-organic frameworks in recent scientific literature underscores their highly versatile nature. MOFs have been developed for use in a wide array of applications, including: sensors, catalysis, separations, drug delivery, and electrochemical processes. Often overlooked in the discussion of MOF-based materials is the mass transport of guest molecules within the pores and channels. Given the wide distribution of pore sizes, linker functionalization, and crystal sizes, molecular diffusion within MOFs can be highly dependent on the MOF-guest system. In this review, we discuss the major factors that govern the mass transport of molecules through MOFs at both the intracrystalline and intercrystalline scale; provide an overview of the experimental and computational methods used to measure guest diffusivity within MOFs; and highlight the relevance of mass transfer in the applications of MOFs in electrochemical systems, separations, and heterogeneous catalysis. Metal-organic framework ; Mass transport ; Nanotechnology ; Linker ; Mass transfer ; Thermal diffusivity ; Electrochemistry ; Molecule ; Materials science ; Chemistry ; Physical chemistry ; Computer science ; Organic chemistry ; Electrode ; Physics ; Engineering physics ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Ellen Roels | Seppe Terryn | Fumiya Iida | Anton W. Bosman | Sophie Norvez | Frank Clemens | Guy Van Assche | Bram Vanderborght | Joost Brancart
Source: Advanced Materials (review, 2021-10-07)
Field: Materials Science — Polymers and Plastics

Processing of Self‐Healing Polymers for Soft Robotics Soft robots are, due to their softness, inherently safe and adapt well to unstructured environments. However, they are prone to various damage types. Self-healing polymers address this vulnerability. Self-healing soft robots can recover completely from macroscopic damage, extending their lifetime. For developing healable soft robots, various formative and additive manufacturing methods have been exploited to shape self-healing polymers into complex structures. Additionally, several novel manufacturing techniques, noted as (re)assembly binding techniques that are specific to self-healing polymers, have been created. Herein, the wide variety of processing techniques of self-healing polymers for robotics available in the literature is reviewed, and limitations and opportunities discussed thoroughly. Based on defined requirements for soft robots, these techniques are critically compared and validated. A strong focus is drawn to the reversible covalent and (physico)chemical cross-links present in the self-healing polymers that do not only endow healability to the resulting soft robotic components, but are also beneficial in many manufacturing techniques. They solve current obstacles in soft robots, including the formation of robust multi-material parts, recyclability, and stress relaxation. This review bridges two promising research fields, and guides the reader toward selecting a suitable processing method based on a self-healing polymer and the intended soft robotics application. Soft robotics ; Robot ; Robotics ; Materials science ; Soft materials ; Self-healing ; Artificial intelligence ; Polymer ; Computer science ; Nanotechnology ; Composite material Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Parinaz Naseri | Sean V. Hum
Source: IEEE Transactions on Antennas and Propagation (article, 2021-02-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces The synthesis of a metasurface exhibiting a specific set of desired scattering properties is a time-consuming and resource-demanding process, which conventionally relies on many cycles of full-wave simulations. It requires an experienced designer to choose the number of metallic layers, the scatterer shapes and dimensions, and the type and the thickness of the separating substrates. In this article, we propose a generative machine learning (ML)-based approach to solve this one-to-many mapping and automate the inverse design of dual- and triple-layer metasurfaces. Using this approach, it is possible to solve optimization problems with single or more constraints by synthesizing thin structures composed of potentially brand-new scatterer designs, in cases where the interlayer coupling between the layers is nonnegligible and synthesis by traditional methods becomes cumbersome. Various examples to provide specific magnitude and phase responses of x- and y-polarized scattering coefficients across a frequency range as well as bounded responses for different metasurface applications are presented to verify the practicality of the proposed method. Inverse ; Bounded function ; Set (abstract data type) ; Inverse problem ; Scattering ; Inverse scattering problem ; Range (aeronautics) ; Coupling (piping) Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Advanced Wireless Communication Technologies
Authors: Yan Chen | Xudong Wang | Le Huang | Xiaoting Wang | Wei Jiang | Zhen Wang | Peng Wang | Binmin Wu | Tie Lin | Hong Shen | Zhongming Wei | Weida Hu | Xiangjian Meng | Junhao Chu | Jianlu Wang
Source: Nature Communications (article, 2021-06-29)
Field: Materials Science — Materials Chemistry

Ferroelectric-tuned van der Waals heterojunction with band alignment evolution Van der Waals integration with abundant two-dimensional materials provides a broad basis for assembling functional devices. In a specific van der Waals heterojunction, the band alignment engineering is crucial and feasible to realize high performance and multifunctionality. Here, we design a ferroelectric-tuned van der Waals heterojunction device structure by integrating a GeSe/MoS<sub>2</sub> VHJ and poly (vinylidene fluoride-trifluoroethylene)-based ferroelectric polymer. An ultrahigh electric field derived from the ferroelectric polarization can effectively modulate the band alignment of the GeSe/MoS<sub>2</sub> heterojunction. Band alignment transition of the heterojunction from type II to type I is demonstrated. The combination of anisotropic GeSe with MoS<sub>2</sub> realizes a high-performance polarization-sensitive photodetector exhibiting low dark current of approximately 1.5 pA, quick response of 14 μs, and high detectivity of 4.7 × 10<sup>12</sup> Jones. Dichroism ratios are also enhanced by ferroelectric polarization in a broad spectrum from visible to near-infrared. The ferroelectric-tuned GeSe/MoS<sub>2</sub> van der Waals heterojunction has great potential for multifunctional detection applications in sophisticated light information sensing. More profoundly, the ferroelectric-tuned van der Waals heterojunction structure provides a valid band-engineering approach to creating versatile devices. Ferroelectricity ; van der Waals force ; Heterojunction ; Materials science ; Optoelectronics ; Ferroelectric polymers ; Polarization (electrochemistry) ; Electronic band structure ; Photodetector ; Specific detectivity ; Nanotechnology ; Condensed matter physics ; Dielectric ; Dark current ; Physics ; Chemistry ; Molecule ; Physical chemistry 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials ; Perovskite Materials and Applications
Authors: Tetiana Zubatiuk | Olexandr Isayev
Source: Accounts of Chemical Research (article, 2021-03-13)
Field: Materials Science — Materials Chemistry

Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence Machine learning interatomic potentials (MLIPs) are widely used for describing molecular energy and continue bridging the speed and accuracy gap between quantum mechanical (QM) and classical approaches like force fields. In this Account, we focus on the out-of-the-box approaches to developing transferable MLIPs for diverse chemical tasks. First, we introduce the "Accurate Neural Network engine for Molecular Energies," ANAKIN-ME, method (or ANI for short). The ANI model utilizes Justin Smith Symmetry Functions (JSSFs) and realizes training for vast data sets. The training data set of several orders of magnitude larger than before has become the key factor of the knowledge transferability and flexibility of MLIPs. As the quantity, quality, and types of interactions included in the training data set will dictate the accuracy of MLIPs, the task of proper data selection and model training could be assisted with advanced methods like active learning (AL), transfer learning (TL), and multitask learning (MTL).Next, we describe the AIMNet "Atoms-in-Molecules Network" that was inspired by the quantum theory of atoms in molecules. The AIMNet architecture lifts multiple limitations in MLIPs. It encodes long-range interactions and learnable representations of chemical elements. We also discuss the AIMNet-ME model that expands the applicability domain of AIMNet from neutral molecules toward open-shell systems. The AIMNet-ME encompasses a dependence of the potential on molecular charge and spin. It brings ML and physical models one step closer, ensuring the correct molecular energy behavior over the total molecular charge.We finally describe perhaps the simplest possible physics-aware model, which combines ML and the extended Hückel method. In ML-EHM, "Hierarchically Interacting Particle Neural Network," HIP-NN generates the set of a molecule- and environment-dependent Hamiltonian elements α<sub>μμ</sub> and <i>K</i><sup>‡</sup>. As a test example, we show how in contrast to traditional Hückel theory, ML-EHM correctly describes orbital crossing with bond rotations. Hence it learns the underlying physics, highlighting that the inclusion of proper physical constraints and symmetries could significantly improve ML model generalization. Computer science ; Flexibility (engineering) ; Artificial intelligence ; Artificial neural network ; Bridging (networking) ; Quantum ; Set (abstract data type) ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Aditya Sood | Andrey D. Poletayev | Daniel A. Cogswell | Peter M. Csernica | J. Tyler Mefford | Dimitrios Fraggedakis | Michael F. Toney | Aaron M. Lindenberg | Martin Z. Bazant | William C. Chueh
Source: Nature Reviews Materials (article, 2021-05-24)
Field: Materials Science — Materials Chemistry

Electrochemical ion insertion from the atomic to the device scale Ranging ; Context (archaeology) ; Electrochemistry ; Characterization (materials science) ; Thermal conduction ; Ion ; Isotropy ; Thermal ; Ultrashort pulse 2D Materials and Applications ; Electrocatalysts for Energy Conversion ; Graphene research and applications
Authors: Yukihiro Ozaki
Source: Analytical Sciences (article, 2021-02-18)
Field: Chemistry — Spectroscopy

Infrared Spectroscopy—Mid-infrared, Near-infrared, and Far-infrared/Terahertz Spectroscopy Spectroscopy ; Infrared spectroscopy ; Infrared ; Chemistry ; Terahertz spectroscopy and technology ; Two-dimensional infrared spectroscopy ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Optics ; Physics Molecular spectroscopy and chirality ; Advanced Chemical Sensor Technologies ; Molecular Spectroscopy and Structure