jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jin Wang | Quan‐Wen Li | Si‐Guo Wu | Yan‐Cong Chen | Rui‐Chen Wan | Guo‐Zhang Huang | Yang Liu | Jun‐Liang Liu | Daniel Reta | Marcus J. Giansiracusa | Zhenxing Wang | Nicholas F. Chilton | Ming‐Liang Tong
Source: Angewandte Chemie International Edition (article, 2020-11-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Opening Magnetic Hysteresis by Axial Ferromagnetic Coupling: From Mono‐Decker to Double‐Decker Metallacrown Combining Ising-type magnetic anisotropy with collinear magnetic interactions in single-molecule magnets (SMMs) is a significant synthetic challenge. Herein we report a Dy[15-MC<sub>Cu</sub> -5] (1-Dy) SMM, where a Dy<sup>III</sup> ion is held in a central pseudo-D<sub>5h</sub> pocket of a rigid and planar Cu<sub>5</sub> metallacrown (MC). Linking two Dy[15-MC<sub>Cu</sub> -5] units with a single hydroxide bridge yields the double-decker {Dy[15-MC<sub>Cu</sub> -5]}<sub>2</sub> (2-Dy) SMM where the anisotropy axes of the two Dy<sup>III</sup> ions are nearly collinear, resulting in magnetic relaxation times for 2-Dy that are approximately 200 000 times slower at 2 K than for 1-Dy in zero external field. Whereas 1-Dy and the Y<sup>III</sup> -diluted Dy@2-Y analogue do not show remanence in magnetic hysteresis experiments, the hysteresis data for 2-Dy remain open up to 6 K without a sudden drop at zero field. In conjunction with theoretical calculations, these results demonstrate that the axial ferromagnetic Dy-Dy coupling suppresses fast quantum tunneling of magnetization (QTM). The relaxation profiles of both complexes curiously exhibit three distinct exponential regimes, and hold the largest effective energy barriers for any reported d-f SMMs up to 625 cm<sup>-1</sup> . Remanence ; Ferromagnetism ; Hysteresis ; Magnetic anisotropy ; Magnetization ; Ion ; Condensed matter physics ; Relaxation (psychology) ; Magnetic hysteresis ; Anisotropy ; Chemistry ; Single-molecule magnet ; Coupling (piping) ; Dysprosium ; Magnetic field ; Crystallography ; Materials science ; Physics ; Inorganic chemistry Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Advanced NMR Techniques and Applications
Authors: Haiying Liu | Jiucheng Cheng | Hongzhou Dong | Jianguang Feng | Beili Pang | Ziya Tian | Shuai Ma | Fengjin Xia | Chunkai Zhang | Lifeng Dong
Source: Computational Materials Science (article, 2020-02-27)
Field: Materials Science — Materials Chemistry

Screening stable and metastable ABO3 perovskites using machine learning and the materials project Perovskite (structure) ; Electronegativity ; Metastability ; Materials science ; Atom (system on chip) ; Octahedron ; Atomic radius ; Crystallography ; Crystal structure ; Chemistry ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Sehyun Chun | Sidhartha Roy | Yen Thi Nguyen | Joseph B. Choi | H. S. Udaykumar | Stephen S. Baek
Source: Scientific Reports (article, 2020-08-06)
Field: Materials Science — Materials Chemistry

Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials The sensitivity of heterogeneous energetic (HE) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. Initiation of chemical reactions occurs at hot spots due to energy localization at sites of porosities and other defects. Emerging multi-scale predictive models of HE response to loads account for the physics at the meso-scale, i.e. at the scale of statistically representative clusters of particles and other features in the microstructure. Meso-scale physics is infused in machine-learned closure models informed by resolved meso-scale simulations. Since microstructures are stochastic, ensembles of meso-scale simulations are required to quantify hot spot ignition and growth and to develop models for microstructure-dependent energy deposition rates. We propose utilizing generative adversarial networks (GAN) to spawn ensembles of synthetic heterogeneous energetic material microstructures. The method generates qualitatively and quantitatively realistic microstructures by learning from images of HE microstructures. We show that the proposed GAN method also permits the generation of new morphologies, where the porosity distribution can be controlled and spatially manipulated. Such control paves the way for the design of novel microstructures to engineer HE materials for targeted performance in a materials-by-design framework. Deep learning ; Microstructure ; Porosity ; Generative grammar ; Deposition (geology) ; Energy (signal processing) ; Multiscale modeling ; Uncertainty quantification Machine Learning in Materials Science ; Energetic Materials and Combustion ; High-Velocity Impact and Material Behavior
Authors: Arnab Kabiraj | Mayank Kumar | Santanu Mahapatra
Source: npj Computational Materials (article, 2020-04-08)
Field: Materials Science — Materials Chemistry

High-throughput discovery of high Curie point two-dimensional ferromagnetic materials Abstract Databases for two-dimensional materials host numerous ferromagnetic materials without the vital information of Curie temperature since its calculation involves a manually intensive complex process. In this work, we develop a fully automated, hardware-accelerated, dynamic-translation based computer code, which performs first principles-based computations followed by Heisenberg model-based Monte Carlo simulations to estimate the Curie temperature from the crystal structure. We employ this code to conduct a high-throughput scan of 786 materials from a database to discover 26 materials with a Curie point beyond 400 K. For rapid data mining, we further use these results to develop an end-to-end machine learning model with generalized chemical features through an exhaustive search of the model space as well as the hyperparameters. We discover a few more high Curie point materials from different sources using this data-driven model. Such material informatics, which agrees well with recent experiments, is expected to foster practical applications of two-dimensional magnetism. Curie temperature ; Ferromagnetism ; Computer science ; Magnetism ; Curie ; Curie constant ; Point (geometry) ; Code (set theory) ; Hyperparameter ; Computational science ; Translation (biology) ; Algorithm ; Statistical physics ; Machine learning ; Condensed matter physics ; Physics ; Curie–Weiss law ; Mathematics ; Chemistry ; Programming language Machine Learning in Materials Science ; 2D Materials and Applications ; Theoretical and Computational Physics
Authors: Hong Wang | Yunchao Xie | Dawei Li | Heng Deng | Yunxin Zhao | Ming Xin | Jian Lin
Source: Journal of Chemical Information and Modeling (article, 2020-03-25)
Field: Materials Science — Materials Chemistry

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal-organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. This CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies. Convolutional neural network ; Identification (biology) ; Pattern recognition (psychology) ; Diffraction ; Computer science ; Artificial intelligence ; Physics ; Biology ; Optics ; Botany Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging ; Advanced X-ray and CT Imaging
Authors: Carlos Rı́os | Yifei Zhang | Mikhail Y. Shalaginov | Skylar Deckoff–Jones | Haozhe Wang | Sensong An | Hualiang Zhang | Myungkoo Kang | Kathleen A. Richardson | Christopher Roberts | Jeffrey B. Chou | Vladimir Liberman | Steven A. Vitale | Jing Kong | Tian Gu | Juejun Hu
Source: Advanced Photonics Research (article, 2020-10-07)
Field: Materials Science — Materials Chemistry

Multi‐Level Electro‐Thermal Switching of Optical Phase‐Change Materials Using Graphene Reconfigurable photonic systems featuring minimal power consumption are crucial for integrated optical devices in real‐world technology. Current active devices available in foundries, however, use volatile methods to modulate light, requiring a constant supply of power and significant form factors. Essential aspects to overcome these issues are the development of nonvolatile optical reconfiguration techniques which are compatible with on‐chip integration with different photonic platforms and do not disrupt their optical performances. Herein, a solution is demonstrated using an optoelectronic framework for nonvolatile tunable photonics that uses undoped‐graphene microheaters to thermally and reversibly switch the optical phase‐change material Ge 2 Sb 2 Se 4 Te 1 (GSST). An in situ Raman spectroscopy method is utilized to demonstrate, in real‐time, reversible switching between four different levels of crystallinity. Moreover, a 3D computational model is developed to precisely interpret the switching characteristics, and to quantify the impact of current saturation on power dissipation, thermal diffusion, and switching speed. This model is used to inform the design of nonvolatile active photonic devices; namely, broadband Si 3 N 4 integrated photonic circuits with small form‐factor modulators and reconfigurable metasurfaces displaying 2π phase coverage through neural‐network‐designed GSST meta‐atoms. This framework will enable scalable, low‐loss nonvolatile applications across a diverse range of photonics platforms. Photonics ; Materials science ; Optical switch ; Optoelectronics ; Switching time ; Control reconfiguration ; Photonic integrated circuit ; Computer science ; Nanotechnology Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Neural Networks and Reservoir Computing
Authors: Alexandros Marios Sofias | Yohana C. Toner | Anu E. Meerwaldt | Mandy M. T. van Leent | Georgios Soultanidis | Mattijs Elschot | Haruki Gonai | Kristin Grendstad | Åsmund Flobak | Ulrike Neckmann | Camilla Wolowczyk | Elizabeth L. Fisher | Thomas Reiner | Catharina de Lange Davies | Geir Bjørkøy | Abraham J. P. Teunissen | Jordi Ochando | Carlos Pérez‐Medina | Willem J. M. Mulder | Sjoerd Hak
Source: ACS Nano (article, 2020-05-15)
Field: Materials Science — Biomaterials

Tumor Targeting by α<sub>v</sub>β<sub>3</sub>-Integrin-Specific Lipid Nanoparticles Occurs <i>via</i> Phagocyte Hitchhiking Although the first nanomedicine was clinically approved more than two decades ago, nanoparticles' (NP) <i>in vivo</i> behavior is complex and the immune system's role in their application remains elusive. At present, only passive-targeting nanoformulations have been clinically approved, while more complicated active-targeting strategies typically fail to advance from the early clinical phase stage. This absence of clinical translation is, among others, due to the very limited understanding for <i>in vivo</i> targeting mechanisms. Dynamic <i>in vivo</i> phenomena such as NPs' real-time targeting kinetics and phagocytes' contribution to active NP targeting remain largely unexplored. To better understand <i>in vivo</i> targeting, monitoring NP accumulation and distribution at complementary levels of spatial and temporal resolution is imperative. Here, we integrate <i>in vivo</i> positron emission tomography/computed tomography imaging with intravital microscopy and flow cytometric analyses to study α<sub>v</sub>β<sub>3</sub>-integrin-targeted cyclic arginine-glycine-aspartate decorated liposomes and oil-in-water nanoemulsions in tumor mouse models. We observed that ligand-mediated accumulation in cancerous lesions is multifaceted and identified "NP hitchhiking" with phagocytes to contribute considerably to this intricate process. We anticipate that this understanding can facilitate rational improvement of nanomedicine applications and that immune cell-NP interactions can be harnessed to develop clinically viable nanomedicine-based immunotherapies. Phagocyte ; Nanoparticle ; Phagocytosis ; Nanotechnology ; Materials science ; Integrin ; Chemistry ; Biophysics ; Biochemistry ; Immunology ; Biology ; Receptor Nanoparticle-Based Drug Delivery ; Cell Adhesion Molecules Research ; RNA Interference and Gene Delivery
Authors: Dan-Liang Wen | Haitao Deng | Xin Liu | Guo-Ke Li | Xin-Ran Zhang | Xiaosheng Zhang
Source: Microsystems & Nanoengineering (article, 2020-09-06)
Field: Materials Science — Materials Chemistry

Wearable multi-sensing double-chain thermoelectric generator Wearable electronics play a crucial role in advancing the rapid development of artificial intelligence, and as an attractive future vision, all-in-one wearable microsystems integrating powering, sensing, actuating and other functional components on a single chip have become an appealing tendency. Herein, we propose a wearable thermoelectric generator (ThEG) with a novel double-chain configuration to simultaneously realize sustainable energy harvesting and multi-functional sensing. In contrast to traditional single-chain ThEGs with the sole function of thermal energy harvesting, each individual chain of the developed double-chain thermoelectric generator (DC-ThEG) can be utilized to scavenge heat energy, and moreover, the combination of the two chains can be employed as functional sensing electrodes at the same time. The mature mass-fabrication technology of screen printing was successfully introduced to print n-type and p-type thermoelectric inks atop a polymeric substrate to form thermocouples to construct two independent chains, which makes this DC-ThEG flexible, high-performance and cost-efficient. The emerging material of silk fibroin was employed to cover the gap of the fabricated two chains to serve as a functional layer for sensing the existence of liquid water molecules in the air and the temperature. The powering and sensing functions of the developed DC-ThEG and their interactions were systematically studied via experimental measurements, which proved the DC-ThEG to be a robust multi-functional power source with a 151 mV open-circuit voltage. In addition, it was successfully demonstrated that this DC-ThEG can convert heat energy to achieve a 3.3 V output, matching common power demands of wearable electronics, and harvest biothermal energy to drive commercial electronics (i.e., a calculator). The integration approach of powering and multi-functional sensing based on this new double-chain configuration might open a new chapter in advanced thermoelectric generators, especially in the applications of all-in-one self-powered microsystems. Energy harvesting ; Thermoelectric generator ; Materials science ; Wearable computer ; Thermoelectric effect ; Nanotechnology ; Generator (circuit theory) ; Optoelectronics ; Thermocouple ; Electrical engineering ; Computer science ; Power (physics) ; Embedded system ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Innovative Energy Harvesting Technologies
Authors: Hayden A. Evans | Yue Wu | Ram Seshadri | Anthony K. Cheetham
Source: Nature Reviews Materials (review, 2020-01-22)
Field: Materials Science — Materials Chemistry

Perovskite-related ReO3-type structures Polyatomic ion ; Crystallography ; Chemistry ; Halogen ; Materials science ; Chemical physics ; Nanotechnology ; Ion ; Organic chemistry Thermal Expansion and Ionic Conductivity ; Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies
Authors: Rodrigo C. Silva | Luely Oliveira da Silva | Aloisio de Andrade Bartolomeu | Timothy J. Brocksom | Kléber T. de Oliveira
Source: Beilstein Journal of Organic Chemistry (review, 2020-05-06)
Field: Materials Science — Materials Chemistry

Recent applications of porphyrins as photocatalysts in organic synthesis: batch and continuous flow approaches In this review we present relevant and recent applications of porphyrin derivatives as photocatalysts in organic synthesis, involving both single electron transfer (SET) and energy transfer (ET) mechanistic approaches. We demonstrate that these highly conjugated photosensitizers show increasing potential in photocatalysis since they combine both photo- and electrochemical properties which can substitute available metalloorganic photocatalysts. Batch and continuous-flow approaches are presented highlighting the relevance of enabling technologies for the renewal of porphyrin applications in photocatalysis. Finally, the reaction scale in which the methodologies were developed are highlighted since this is an important parameter in the authors' opinion. Porphyrin ; Photocatalysis ; Chemistry ; Continuous flow ; Nanotechnology ; Biochemical engineering ; Organic synthesis ; Combinatorial chemistry ; Electron transfer ; Flow chemistry ; Electrochemistry ; Energy transfer ; Photochemistry ; Catalysis ; Organic chemistry ; Materials science ; Chemical physics Porphyrin and Phthalocyanine Chemistry ; CO2 Reduction Techniques and Catalysts ; Radical Photochemical Reactions
Authors: Xiang Sun | Jingnan Zheng | Yijing Gao | Chenglong Qiu | Yi‐Long Yan | Zihao Yao | Shengwei Deng | Jianguo Wang
Source: Applied Surface Science (article, 2020-05-06)
Field: Materials Science — Materials Chemistry

Machine-learning-accelerated screening of hydrogen evolution catalysts in MBenes materials Catalysis ; Gibbs free energy ; Density functional theory ; Hydrogen ; Hydrogen atom ; Adsorption ; Metal ; Atom (system on chip) ; MXenes ; Materials science ; Physical chemistry ; Chemistry ; Computational chemistry ; Thermodynamics ; Nanotechnology ; Computer science ; Physics ; Metallurgy ; Group (periodic table) MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Ibrahim Dubdub | Mohammed Al‐Yaari
Source: Polymers (article, 2020-04-12)
Field: Materials Science — Materials Chemistry

Pyrolysis of Low Density Polyethylene: Kinetic Study Using TGA Data and ANN Prediction Pyrolysis of waste low-density polyethylene (LDPE) is considered to be a highly efficient, promising treatment method. This work aims to investigate the kinetics of LDPE pyrolysis using three model-free methods (Friedman, Flynn-Wall-Qzawa (FWO), and Kissinger-Akahira-Sunose (KAS)), two model-fitting methods (Arrhenius and Coats-Redfern), as well as to develop, for the first time, a highly efficient artificial neural network (ANN) model to predict the kinetic parameters of LDPE pyrolysis. Thermogravimetric (TG) and derivative thermogravimetric (DTG) thermograms at 5, 10, 20 and 40 K min<sup>-1</sup> showed only a single pyrolysis zone, implying a single reaction. The values of the kinetic parameters (<i>E</i> and <i>A</i>) of LDPE pyrolysis have been calculated at different conversions by three model-free methods and the average values of the obtained activation energies are in good agreement and ranging between 193 and 195 kJ mol<sup>-1</sup>. In addition, these kinetic parameters at different heating rates have been calculated using Arrhenius and Coats-Redfern methods. Moreover, a feed-forward ANN with backpropagation model, with 10 neurons in two hidden layers and logsig-logsig transfer functions, has been employed to predict the thermogravimetric analysis (TGA) kinetic data. Results showed good agreement between the ANN-predicted and experimental data (R > 0.9999). Then, the selected network topology was tested for extra new input data with a highly efficient performance. Low-density polyethylene ; Thermogravimetric analysis ; Arrhenius equation ; Materials science ; Kinetic energy ; Pyrolysis ; Polyethylene ; Activation energy ; Thermodynamics ; Composite material ; Physical chemistry ; Chemistry ; Organic chemistry ; Physics Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Polymer crystallization and properties
Authors: Yong Zhang | Hyun-Seung Kim | Qing Wang | Wook Jo | Angus I. Kingon | Seung‐Hyun Kim | Chang Kyu Jeong
Source: Nanoscale Advances (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Progress in lead-free piezoelectric nanofiller materials and related composite nanogenerator devices Current piezoelectric device systems need a significant reduction in size and weight so that electronic modules of increasing capacity and functionality can be incorporated into a great range of applications, particularly in energy device platforms. The key question for most applications is whether they can compete in the race of down-scaling and an easy integration with highly adaptable properties into various system technologies such as nano-electro-mechanical systems (NEMS). Piezoelectric NEMS have potential to offer access to a parameter space for sensing, actuating, and powering, which is inflential and intriguing. Fortunately, recent advances in modelling, synthesis, and characterization techniques are spurring unprecedented developments in a new field of piezoelectric nano-materials and devices. While the need for looking more closely at the piezoelectric nano-materials is driven by the relentless drive of miniaturization, there is an additional motivation: the piezoelectric materials, which are showing the largest electromechanical responses, are currently toxic lead (Pb)-based perovskite materials (such as the ubiquitous Pb(Zr,Ti)O<sub>3</sub>, PZT). This is important, as there is strong legislative and moral push to remove toxic lead compounds from commercial products. By far, the lack of viable alternatives has led to continuing exemptions to allow their temporary use in piezoelectric applications. However, the present exemption will expire soon, and the concurrent improvement of lead-free piezoelectric materials has led to the possibility that no new exemption will be granted. In this paper, the universal approaches and recent progresses in the field of lead-free piezoelectric nano-materials, initially focusing on hybrid composite materials as well as individual nanoparticles, and related energy harvesting devices are systematically elaborated. The paper begins with a short introduction to the properties of interest in various piezoelectric nanomaterials and a brief description of the current state-of-the-art for lead-free piezoelectric nanostructured materials. We then describe several key methodologies for the synthesis of nanostructure materials including nanoparticles, followed by the discussion on the critical current and emerging applications in detail. Miniaturization ; Piezoelectricity ; Nanoelectromechanical systems ; Nanotechnology ; Nanogenerator ; Materials science ; Energy harvesting ; Characterization (materials science) ; Engineering physics ; Engineering ; Energy (signal processing) ; Physics ; Composite material Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; Acoustic Wave Resonator Technologies