jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stephanie G. E. Amos | Marion Garreau | Luca Buzzetti | Jérôme Waser
Source: Beilstein Journal of Organic Chemistry (review, 2020-05-29)
Field: Chemistry — Organic Chemistry

Photocatalysis with organic dyes: facile access to reactive intermediates for synthesis Organic dyes have emerged as a reliable class of photoredox catalysts. Their great structural variety combined with the easy fine-tuning of their electronic properties has unlocked new possibilities for the generation of reactive intermediates. In this review, we provide an overview of the available approaches to access reactive intermediates that employ organophotocatalysis. Our contribution is not a comprehensive description of the work in the area but rather focuses on key concepts, accompanied by a few selected illustrative examples. The review is organized along the type of reactive intermediates formed in the reaction, including C(sp<sup>3</sup>) and C(sp <i><sup>2</sup></i> ) carbon-, nitrogen-, oxygen-, and sulfur-centered radicals, open-shell charged species, and sensitized organic compounds. Chemistry ; Reactive intermediate ; Radical ; Photocatalysis ; Organic synthesis ; Sulfur ; Primary (astronomy) ; Combinatorial chemistry ; Photoredox catalysis ; Catalysis ; Nanotechnology ; Photochemistry ; Organic chemistry Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques ; Catalytic C–H Functionalization Methods
Authors: Yang Zhang | Noah F. Q. Yuan | Liang Fu
Source: Physical review. B./Physical review. B (article, 2020-11-30)
Field: Materials Science — Materials Chemistry

Moiré quantum chemistry: Charge transfer in transition metal dichalcogenide superlattices Transition metal dichalcogenide (TMD) bilayers have recently emerged as a robust and tunable moir\'e system for studying and designing correlated electron physics. In this Rapid Communication, by combining a large-scale first-principles calculation and continuum model approach, we provide an electronic structure theory that maps long-period TMD heterobilayer superlattices onto diatomic crystals with cations and anions. We find that the interplay between the moir\'e potential and Coulomb interaction leads to filling-dependent charge transfer between different moir\'e superlattice regions. We show that the insulating state at half filling found in recent experiments on $\mathrm{W}{\mathrm{Se}}_{2}/\mathrm{W}{\mathrm{S}}_{2}$ is a charge-transfer insulator rather than a Mott-Hubbard insulator. Our work reveals the richness of simplicity in moir\'e quantum chemistry. Superlattice ; Transition metal ; Charge (physics) ; Quantum ; Chemistry ; Chemical physics ; Condensed matter physics ; Nanotechnology ; Materials science ; Physics ; Quantum mechanics ; Catalysis ; Organic chemistry 2D Materials and Applications ; Crystal Structures and Properties ; Quantum Dots Synthesis And Properties
Authors: Jing Zhang | Fan Wang | Vivek B. Shenoy | Ming Tang | Jun Lou
Source: Materials Today (article, 2020-07-21)
Field: Materials Science — Materials Chemistry

Towards controlled synthesis of 2D crystals by chemical vapor deposition (CVD) Chemical vapor deposition ; Materials science ; Combustion chemical vapor deposition ; Nanotechnology ; Chemical engineering ; Hybrid physical-chemical vapor deposition ; Carbon film ; Thin film ; Engineering 2D Materials and Applications ; Graphene research and applications ; Nanowire Synthesis and Applications
Authors: Yifan Wang | Yanheng Xu | Menglei Hu | Han Ling | Xi Zhu
Source: Nanophotonics (article, 2020-02-01)
Field: Materials Science — Materials Chemistry

MXenes: focus on optical and electronic properties and corresponding applications Abstract The discovery of graphene, the first two‐dimensional (2D) material, has caused an upsurge, as this kind of material revealed a tremendous potential of application in areas such as energy storage, electronics, and gas separation. MXenes are referred to as a family of 2D transition metal carbides, carbonitrides, and nitrides. After the synthesis of Ti 3 C 2 from Ti 3 AlC 2 in 2011, about 30 new compositions have been reported. These materials have been widely discussed, synthesized, and investigated by many research groups, as they have many advantages over traditional 2D materials. This review covers the structures of MXenes, discusses various synthesis routines, analyzes the properties, especially optical and electronic properties, and summarizes their applications and potential, which may give readers an overview of these popular materials. MXenes ; Carbide ; Materials science ; Nanotechnology ; Graphene ; Nanomaterials ; Electronics ; Nitride ; Characterization (materials science) ; Engineering physics ; Chemistry ; Layer (electronics) ; Physics ; Metallurgy ; Physical chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: James E. Saal | Anton O. Oliynyk | Bryce Meredig
Source: Annual Review of Materials Research (article, 2020-05-18)
Field: Materials Science — Materials Chemistry

Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches The rapidly growing interest in machine learning (ML) for materials discovery has resulted in a large body of published work. However, only a small fraction of these publications includes confirmation of ML predictions, either via experiment or via physics-based simulations. In this review, we first identify the core components common to materials informatics discovery pipelines, such as training data, choice of ML algorithm, and measurement of model performance. Then we discuss some prominent examples of validated ML-driven materials discovery across a wide variety of materials classes, with special attention to methodological considerations and advances. Across these case studies, we identify several common themes, such as the use of domain knowledge to inform ML models. Variety (cybernetics) ; Computer science ; Data science ; Materials informatics ; Informatics ; Machine learning ; Common core ; Core (optical fiber) ; Artificial intelligence ; Data mining ; Health informatics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Sensong An | Bowen Zheng | Mikhail Y. Shalaginov | Hong Tang | Hang Li | L. P. Zhou | Jun Ding | Anu Agarwal | Clara Rivero‐Baleine | Myungkoo Kang | Kathleen Richardson | Tian Gu | Juejun Hu | Clayton Fowler | Hualiang Zhang
Source: Optics Express (article, 2020-08-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning modeling approach for metasurfaces with high degrees of freedom Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. The design of meta-atoms, the fundamental building blocks of metasurfaces, typically relies on trial and error to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of meta-atom designs with varying physical and geometric parameters, which demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with nearly freeform 2D patterns and different lattice sizes, material refractive indices and thicknesses. Moreover, the presented approach features the capability of predicting a meta-atom's wide spectrum response in the timescale of milliseconds, attractive for applications necessitating fast on-demand design and optimization of a meta-atom/metasurface. Optics ; Degrees of freedom (physics and chemistry) ; Computer science ; Physics Metamaterials and Metasurfaces Applications ; Advanced Materials and Mechanics ; Micro and Nano Robotics
Authors: Joshua L. Lansford | Dionisios G. Vlachos
Source: Nature Communications (article, 2020-03-23)
Field: Materials Science — Materials Chemistry

Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap. Infrared spectroscopy ; Infrared ; Spectral line ; Statistical physics ; Chemical physics ; Spectroscopy ; Materials science ; Computer science ; Biological system ; Computational physics ; Physics ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Electrochemical Analysis and Applications
Authors: Nathan C. Frey | Deji Akinwande | Deep Jariwala | Vivek B. Shenoy
Source: ACS Nano (article, 2020-09-08)
Field: Materials Science — Materials Chemistry

Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing Engineered point defects in two-dimensional (2D) materials offer an attractive platform for solid-state devices that exploit tailored optoelectronic, quantum emission, and resistive properties. Naturally occurring defects are also unavoidably important contributors to material properties and performance. The immense variety and complexity of possible defects make it challenging to experimentally control, probe, or understand atomic-scale defect-property relationships. Here, we develop an approach based on deep transfer learning, machine learning, and first-principles calculations to rapidly predict key properties of point defects in 2D materials. We use physics-informed featurization to generate a minimal description of defect structures and present a general picture of defects across materials systems. We identify over one hundred promising, unexplored dopant defect structures in layered metal chalcogenides, hexagonal nitrides, and metal halides. These defects are prime candidates for quantum emission, resistive switching, and neuromorphic computing. Neuromorphic engineering ; Materials science ; Nanotechnology ; Quantum ; Resistive touchscreen ; Quantum computer ; Computer science ; Quantum dot ; Atomic units ; Dopant ; Optoelectronics ; Artificial intelligence ; Physics ; Artificial neural network ; Doping 2D Materials and Applications ; Advanced Memory and Neural Computing ; Machine Learning in Materials Science
Authors: Ajay Vikram Singh | Romi-Singh Maharjan | Anurag Kanase | Katherina Siewert | Daniel Rosenkranz | Rishabh Singh | Peter Laux | Andreas Luch
Source: ACS Applied Materials & Interfaces (article, 2020-12-29)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Approach to Decode the Influence of Nanomaterial Properties on Their Interaction with Cells In an <i>in vitro</i> nanotoxicity system, cell-nanoparticle (NP) interaction leads to the surface adsorption, uptake, and changes into nuclei/cell phenotype and chemistry, as an indicator of oxidative stress, genotoxicity, and carcinogenicity. Different types of nanomaterials and their chemical composition or "corona" have been widely studied in context with nanotoxicology. However, rare reports are available, which delineate the details of the cell shape index (CSI) and nuclear area factors (NAFs) as a descriptor of the type of nanomaterials. In this paper, we propose a machine-learning-based graph modeling and correlation-establishing approach using tight junction protein ZO-1-mediated alteration in the cell/nuclei phenotype to quantify and propose it as indices of cell-NP interactions. We believe that the phenotypic variation (CSI and NAF) in the epithelial cell is governed by the physicochemical descriptors (<i>e.g.</i>, shape, size, zeta potential, concentration, diffusion coefficients, polydispersity, and so on) of the different classes of nanomaterials, which critically determines the intracellular uptake or cell membrane interactions when exposed to the epithelial cells at sub-lethal concentrations. The intrinsic and extrinsic physicochemical properties of the representative nanomaterials (NMs) were measured using optical (dynamic light scattering, NP tracking analysis) methods to create a set of nanodescriptors contributing to cell-NM interactions <i>via</i> phenotype adjustments. We used correlation function as a machine-learning algorithm to successfully predict cell and nuclei shapes and polarity functions as phenotypic markers for five different classes of nanomaterials studied herein this report. The CSI and NAF as nanodescriptors can be used as intuitive cell phenotypic parameters to define the safety of nanomaterials extensively used in consumer products and nanomedicine. Nanomaterials ; Nanotoxicology ; Biophysics ; Materials science ; Context (archaeology) ; Nanotechnology ; Cell ; Phenotype ; Biological system ; Nanoparticle ; Zeta potential ; Chemistry ; Biochemistry ; Biology ; Gene Nanoparticles: synthesis and applications
Authors: Lina Papadimitriou | Phanee Manganas | Anthi Ranella | Emmanuel Stratakis
Source: Materials Today Bio (review, 2020-01-30)
Field: Materials Science — Biomaterials

Biofabrication for neural tissue engineering applications Unlike other tissue types, the nervous tissue extends to a wide and complex environment that provides a plurality of different biochemical and topological stimuli, which in turn defines the advanced functions of that tissue. As a consequence of such complexity, the traditional transplantation therapeutic methods are quite ineffective; therefore, the restoration of peripheral and central nervous system injuries has been a continuous scientific challenge. Tissue engineering and regenerative medicine in the nervous system have provided new alternative medical approaches. These methods use external biomaterial supports, known as scaffolds, to create platforms for the cells to migrate to the injury site and repair the tissue. The challenge in neural tissue engineering (NTE) remains the fabrication of scaffolds with precisely controlled, tunable topography, biochemical cues, and surface energy, capable of directing and controlling the function of neuronal cells toward the recovery from neurological disorders and injuries. At the same time, it has been shown that NTE provides the potential to model neurological diseases <i>in vitro</i>, mainly via lab-on-a-chip systems, especially in cases for which it is difficult to obtain suitable animal models. As a consequence of the intense research activity in the field, a variety of synthetic approaches and 3D fabrication methods have been developed for the fabrication of NTE scaffolds, including soft lithography and self-assembly, as well as subtractive (top-down) and additive (bottom-up) manufacturing. This article aims at reviewing the existing research effort in the rapidly growing field related to the development of biomaterial scaffolds and lab-on-a-chip systems for NTE applications. Besides presenting recent advances achieved by NTE strategies, this work also delineates existing limitations and highlights emerging possibilities and future prospects in this field. Biofabrication ; Tissue engineering ; Neural tissue engineering ; Computer science ; Biochemical engineering ; Engineering ; Neuroscience ; Nanotechnology ; Biomedical engineering ; Materials science ; Biology Electrospun Nanofibers in Biomedical Applications ; 3D Printing in Biomedical Research ; Bone Tissue Engineering Materials
Authors: Yuxin Yan | Fathima Zahra Nashath | Sharon Chen | Sivakumar Manickam | Siew Shee Lim | Haitao Zhao | Edward Lester | Tao Wu | Cheng Heng Pang
Source: Nanotechnology Reviews (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Synthesis of graphene: Potential carbon precursors and approaches Abstract Graphene is an advanced carbon functional material with inherent unique properties that make it suitable for a wide range of applications. It can be synthesized through either the top–down approach involving delamination of graphitic materials or the bottom–up approach involving graphene assembly from smaller building units. Common top–down approaches are exfoliation and reduction while bottom–up approaches include chemical vapour deposition, epitaxial growth, and pyrolysis. A range of materials have been successfully used as precursors in various synthesis methods to derive graphene. This review analyses and discusses the suitability of conventional, plant- and animal-derived, chemical, and fossil precursors for graphene synthesis. Together with its associated technical feasibility and economic and environmental impacts, the quality of resultant graphene is critically assessed and discussed. After evaluating the parameters mentioned above, the most appropriate synthesis method for each precursor is identified. While graphite is currently the most common precursor for graphene synthesis, several other precursors have the potential to synthesize graphene of comparable, if not better, quality and yield. Thus, this review provides an overview and insights into identifying the potential of various carbon precursors for large-scale and commercial production of fit-for-purpose graphene for specific applications. Graphene ; Materials science ; Nanotechnology ; Chemical vapor deposition ; Graphite ; Exfoliation joint ; Carbon fibers ; Yield (engineering) ; Composite number ; Composite material Graphene research and applications ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Bohayra Mortazavi | Fazel Shojaei | Masoud Shahrokhi | Maryam Azizi | Timon Rabczuk | Alexander V. Shapeev | Xiaoying Zhuang
Source: Carbon (article, 2020-06-05)
Field: Materials Science — Materials Chemistry

Nanoporous C3N4, C3N5 and C3N6 nanosheets; novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties Nanoporous ; Semiconductor ; Materials science ; Thermal ; Electronic materials ; Optoelectronics ; Nanotechnology ; Physics MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Graphene research and applications
Authors: Pedro Baptista de Castro | Kensei Terashima | Takafumi D Yamamoto | Zhufeng Hou | Suguru Iwasaki | Ryo Matsumoto | Shintaro Adachi | Yoshito Saito | Peng Song | Hiroyuki Takeya | Yoshihiko Takano
Source: NPG Asia Materials (article, 2020-05-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-learning-guided discovery of the gigantic magnetocaloric effect in HoB2 near the hydrogen liquefaction temperature Abstract Magnetic refrigeration exploits the magnetocaloric effect, which is the entropy change upon the application and removal of magnetic fields in materials, providing an alternate path for refrigeration other than conventional gas cycles. While intensive research has uncovered a vast number of magnetic materials that exhibit a large magnetocaloric effect, these properties remain unknown for a substantial number of compounds. To explore new functional materials in this unknown space, machine learning is used as a guide for selecting materials that could exhibit a large magnetocaloric effect. By this approach, HoB 2 is singled out and synthesized, and its magnetocaloric properties are evaluated, leading to the experimental discovery of a gigantic magnetic entropy change of 40.1 J kg −1 K −1 (0.35 J cm −3 K −1 ) for a field change of 5 T in the vicinity of a ferromagnetic second-order phase transition with a Curie temperature of 15 K. This is the highest value reported so far, to the best of our knowledge, near the hydrogen liquefaction temperature; thus, HoB 2 is a highly suitable material for hydrogen liquefaction and low-temperature magnetic cooling applications. Magnetic refrigeration ; Refrigerant ; Curie temperature ; Ferromagnetism ; Liquefaction ; Refrigeration ; Magnetic field Magnetic and transport properties of perovskites and related materials ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Mohammadreza Karamad | Rishikesh Magar | Yuting Shi | Samira Siahrostami | Ian D. Gates | Amir Barati Farimani
Source: Physical Review Materials (article, 2020-09-08)
Field: Materials Science — Materials Chemistry

Orbital graph convolutional neural network for material property prediction Material representations that are compatible with machine learning models play a key role in developing models that exhibit high accuracy for property prediction. Atomic orbital interactions are one of the important factors that govern the properties of crystalline materials from which the local chemical environments of atoms is inferred. Therefore, to develop robust machine learning models for material properties prediction, it is imperative to include features representing such chemical attributes. Here, we propose the orbital graph convolutional neural network (OGCNN), a crystal graph convolutional neural network framework that includes atomic orbital interaction features that learns material properties in a robust way. In addition, we embedded an encoder-decoder network into the OGCNN enabling it to learn important features among basic atomic (elemental features), orbital-orbital interactions, and topological features. We examined the performance of this model on a broad range of crystalline materials data to predict different properties. We benchmarked the performance of the OGCNN model with that of: (1) the crystal graph convolutional neural network, (2) other state-of-the-art descriptors for material representations including many-body tensor representation and the smooth overlap of atomic positions, and (3) other conventional regression machine learning algorithms where different crystal featurization methods have been used. We find that the OGCNN significantly outperforms them. The OGCNN model with high predictive accuracy can be used to discover new materials among the immense phase and compound spaces of materials. Convolutional neural network ; Computer science ; Graph ; Artificial neural network ; Encoder ; Atomic orbital ; Materials science ; Property (philosophy) ; Artificial intelligence ; Crystal (programming language) ; Representation (politics) ; Machine learning ; Algorithm ; Theoretical computer science ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography