jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiachen Yu | Benjamin A. Foutty | Yves H. Kwan | Mark E. Barber | Kenji Watanabe | Takashi Taniguchi | Zhi‐Xun Shen | S. A. Parameswaran | Benjamin E. Feldman
Source: Nature Communications (article, 2023-10-21)
Field: Materials Science — Materials Chemistry

Spin skyrmion gaps as signatures of strong-coupling insulators in magic-angle twisted bilayer graphene The flat electronic bands in magic-angle twisted bilayer graphene (MATBG) host a variety of correlated insulating ground states, many of which are predicted to support charged excitations with topologically non-trivial spin and/or valley skyrmion textures. However, it has remained challenging to experimentally address their ground state order and excitations, both because some of the proposed states do not couple directly to experimental probes, and because they are highly sensitive to spatial inhomogeneities in real samples. Here, using a scanning single-electron transistor, we observe thermodynamic gaps at even integer moiré filling factors at low magnetic fields. We find evidence of a field-tuned crossover from charged spin skyrmions to bare particle-like excitations, suggesting that the underlying ground state belongs to the manifold of strong-coupling insulators. From the spatial dependence of these states and the chemical potential variation within the flat bands, we infer a link between the stability of the correlated ground states and local twist angle and strain. Our work advances the microscopic understanding of the correlated insulators in MATBG and their unconventional excitations. Skyrmion ; Bilayer graphene ; Condensed matter physics ; Physics ; Ground state ; Coupling (piping) ; Magic angle ; Spin (aerodynamics) ; Graphene ; Materials science ; Spectral line ; Quantum mechanics Graphene research and applications ; Quantum and electron transport phenomena ; Topological Materials and Phenomena
Authors: Bryn Davies | G. J. Chaplain | Timothy A. Starkey | Richard V. Craster
Source: Physical Review Letters (article, 2023-10-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graded Quasiperiodic Metamaterials Perform Fractal Rainbow Trapping The rainbow trapping phenomenon of graded metamaterials can be combined with the fractal spectra of quasiperiodic waveguides to give a metamaterial that performs fractal rainbow trapping. This is achieved through a graded cut-and-project algorithm that yields a geometry for which the effective projection angle is graded along its length. As a result, the fractal structure of local band gaps varies with position, leading to broadband "fractal" rainbow trapping. We demonstrate this principle by designing an acoustic waveguide, which is characterised using theory, simulation and experiments. Quasiperiodic function ; Metamaterial ; Fractal ; Rainbow ; Trapping ; Quasicrystal ; Physics ; Optics ; Materials science ; Condensed matter physics ; Mathematical analysis ; Mathematics Metamaterials and Metasurfaces Applications ; Cellular Automata and Applications ; Antenna Design and Analysis
Authors: Brenden G. Pelkie | Lilo D. Pozzo
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

The laboratory of Babel: highlighting community needs for integrated materials data management In this perspective, we highlight the need to integrate data management infrastructure across scales to best take advantage of advancements in automated and autonomous experimental methods in materials science. Data management ; Data science ; Engineering ; Computer science ; World Wide Web ; Database Machine Learning in Materials Science ; Scientific Computing and Data Management ; Electronic and Structural Properties of Oxides
Authors: Zhenru Chen | Yunchao Xie | Yuchao Wu | Yuyi Lin | Shigetaka Tomiya | Jian Lin
Source: Digital Discovery (article, 2023-12-29)
Field: Materials Science — Materials Chemistry

An interpretable and transferrable vision transformer model for rapid materials spectra classification An interpretable and transferrable Vision Transformer (ViT) model was developed for classifying individual materials from their XRD and FTIR spectra. Transformer ; Artificial intelligence ; Computer science ; Spectral line ; Pattern recognition (psychology) ; Machine learning ; Engineering ; Electrical engineering ; Physics ; Voltage Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Jaroslav Hornak | Jakub Černohous | Pavel Prosr | Pavel Rous | Pavel Trnka | A. Baran | Štefan Hardoň
Source: Polymers (article, 2023-03-20)
Field: Materials Science — Polymers and Plastics

A Comprehensive Study of Polyurethane Potting Compounds Doped with Magnesium Oxide Nanoparticles Recently, polyurethanes (PURs) have become a very promising group of materials with considerable utilization and innovation potential. This work presents a comprehensive analysis of the changes in material properties important for PUR applications in the electrical industry due to the incorporation of magnesium oxide (MgO) nanoparticles at different weight ratios. From the results of the investigations carried out, it is evident that the incorporation of MgO improves the volume (by up to +0.5 order of magnitude) and surface (+1 order of magnitude) resistivities, reduces the dielectric losses at higher temperatures (-62%), improves the thermal stability of the material, and slows the decomposition reaction of polyurethane at specific temperatures (+30 °C). In contrast, the incorporation of MgO results in a slight decrease in the dielectric strength (-15%) and a significant decrease in the mechanical strength (-37%). Potting ; Polyurethane ; Materials science ; Magnesium ; Thermal stability ; Doping ; Nanoparticle ; Dielectric ; Oxide ; Thermal decomposition ; Chemical engineering ; Composite material ; Nanotechnology ; Metallurgy ; Organic chemistry ; Chemistry ; Optoelectronics Polymer composites and self-healing ; High voltage insulation and dielectric phenomena ; Dielectric materials and actuators
Authors: Muhammad Mohsin Yousufi | Iskandar Dzulkarnain | Mysara Eissa Mohyaldinn | Shehzad Ahmed
Source: Processes (article, 2023-09-06)
Field: Materials Science — Materials Chemistry

A Perspective on the Prospect of Pickering Emulsion in Reservoir Conformance Control with Insight into the Influential Parameters and Characterization Techniques In reservoir conformance control, polymer gels and foams are majorly used; however, they have drawbacks such as inducing formation damage, having weaker shear resistance, requiring a higher pumping rate, and limited penetration depth. Emulsions are a potential alternative that can address these issues, but they are not widely used. Current surfactant-based emulsions require high emulsifier concentrations for stability and often rely on multiple additives to address various factors, which makes the surfactant synthesis and utilization of emulsions quite challenging. However, Pickering emulsions, which utilize solid particles for emulsion stabilization, have emerged as a promising solution for reservoir conformance control. Compared to conventional polymer gels and foams, Pickering emulsions offer superior shear resistance, deeper penetration, and reduced formation damage. This review provides an overview of recent developments in the utilization of Pickering emulsions for conformance control, highlighting important parameters and characteristics that must be considered during the design and deployment of a Pickering emulsion for water shut-off operation. This review also sheds light on current challenges and provides recommendations for future development of the particle-stabilized colloid system. Pickering emulsion ; Emulsion ; Pulmonary surfactant ; Materials science ; Penetration (warfare) ; Software deployment ; Nanotechnology ; Polymer ; Chemical engineering ; Petroleum engineering ; Engineering ; Composite material ; Operations research Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Enhanced Oil Recovery Techniques
Authors: Paramita Paul | Leena Chacko | Tarun Kumar Dua | Pratik Chakraborty | Udita Paul | Vishwakarma Vishal Phulchand | Niraj Kumar Jha | Saurabh Kumar Jha | Ramesh Kandimalla | Saikat Dewanjee
Source: Frontiers in Endocrinology (review, 2023-11-03)
Field: Materials Science — Biomaterials

Nanomedicines for the management of diabetic nephropathy: present progress and prospects Diabetic nephropathy (DN) is a serious microvascular consequence of diabetes mellitus (DM), posing an encumbrance to public health worldwide. Control over the onset and progress of DN depend heavily on early detection and effective treatment. DN is a major contributor to end-stage renal disease, and a complete cure is yet to be achieved with currently available options. Though some therapeutic molecules have exhibited promise in treating DN complications, their poor solubility profile, low bioavailability, poor permeation, high therapeutic dose and associated toxicity, and low patient compliance apprehend their clinical usefulness. Recent research has indicated nano-systems as potential theranostic platforms displaying futuristic promise in the diagnosis and treatment of DN. Early and accurate diagnosis, site-specific delivery and retention by virtue of ligand conjugation, and improved pharmacokinetic profile are amongst the major advantages of nano-platforms, defining their superiority. Thus, the emergence of nanoparticles has offered fresh approaches to the possible diagnostic and therapeutic strategies regarding DN. The present review corroborates an updated overview of different types of nanocarriers regarding potential approaches for the diagnosis and therapy of DN. Nanocarriers ; Medicine ; Intensive care medicine ; Diabetic nephropathy ; Disease ; Bioavailability ; Diabetes mellitus ; Pharmacology ; Internal medicine ; Drug ; Endocrinology Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Pregnancy and Medication Impact
Authors: Ján Kruželák | Andrea Kvasničáková | Michaela Džuganová | Lenka Hašková | Rastislav Dosoudil | Ivan Hudec
Source: Polymers (article, 2023-02-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Curing, Properties and EMI Absorption Shielding of Rubber Composites Based on Ferrites and Carbon Fibres In this work, magnetic soft ferrites, namely manganese-zinc ferrite, nickel-zinc ferrite and combinations of both fillers, were incorporated into acrylonitrile-butadiene rubber to fabricate composite materials. The total content of ferrites was kept constant-300 phr. The second series of composites was fabricated with a similar composition. Moreover, carbon fibres were incorporated into rubber compounds in constant amount-25 phr. The work was focused on investigation of the fillers on absorption shieling performance of the composites, which was investigated within the frequency range 1-6 GHz. Then, the physical-mechanical properties of the composites were evaluated. The achieved results demonstrated that the absorption shielding efficiency of both composite types increased with increasing proportion of nickel-zinc ferrite, which suggests that nickel-zinc ferrite demonstrated better absorption shielding potential. Higher electrical conductivity and higher permittivity of composites filled with carbon fibres and ferrites resulted in their lower absorption shielding performance. Simultaneously, they absorbed electromagnetic radiation at lower frequencies. On the other hand, carbon fibres reinforced the rubber matrix, and subsequent improvement in physical-mechanical properties was recorded. Composite material ; Materials science ; Curing (chemistry) ; Electromagnetic shielding ; EMI ; Natural rubber ; Electromagnetic interference ; Electronic engineering Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: Dibyendu Roy | Sumit Roy | Andrew Smallbone | Anthony Paul Roskilly
Source: Applied Energy (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Assessing the techno-economic viability of a trigeneration system integrating ammonia-fuelled solid oxide fuel cell In recent years, ammonia has gained traction as a clean fuel alternative and a promising energy carrier. In this study, a trigeneration system fuelled by ammonia has been conceptualised, integrating a solid oxide fuel cell stack for power generation, a hot water unit for heating, and an NH3-H2O absorption chiller for cooling. The main objective of this study is to conduct a comprehensive techno-economic feasibility assessment of the proposed trigeneration system. The system's performance was analysed for a UK supermarket requiring electricity, heating, and cooling. A detailed sensitivity analysis was performed to investigate the influence of significant operating parameters, including current density, fuel utilisation factor, and cell temperature, on the system's performance. The system can deliver maximum power, heating, and cooling outputs of 357.6 kW, 257.9 kW, and 46.99 kW, respectively. The trigeneration system is projected to achieve its highest exergy efficiency at 60.94%, with a maximum fuel energy saving ratio of 47.67%. The lowest levelised cost of energy (LCOE) is estimated to be £0.1232 per kWh. This study's objective is also aligned with United Nations Sustainable Development Goal (SDG) No. 7, which aims to achieve “Affordable and Clean Energy”. Absorption refrigerator ; Solid oxide fuel cell ; Waste management ; Exergy efficiency ; Environmental science ; Cost of electricity by source ; Exergy ; Renewable energy ; Process engineering ; Waste heat ; Chiller ; Electricity generation ; Engineering ; Automotive engineering ; Environmental engineering ; Heat exchanger ; Refrigeration ; Power (physics) ; Mechanical engineering ; Electrical engineering ; Chemistry ; Anode Advancements in Solid Oxide Fuel Cells ; Carbon Dioxide Capture Technologies ; Chemical Looping and Thermochemical Processes
Authors: Huaijun Sun | Chao Zhang | Ling Tang | Renhai Wang | Weiyi Xia | Cai‐Zhuang Wang
Source: Physical review. B./Physical review. B (article, 2023-06-02)
Field: Materials Science — Materials Chemistry

Molecular dynamics simulation of Fe-Si alloys using a neural network machine learning potential Interatomic potential development using machine learning (ML) approaches has attracted a lot of attention in recent years because these potentials can effectively describe the structural and dynamical properties of complex materials at the atomistic level. In this work, we present the development of a neural network (NN) deep ML interatomic potential for Fe-Si alloys, and we demonstrate the effectiveness of the NN-ML potential in predicting the structures and energies of liquid and crystalline phases of Fe-Si alloys in comparison with the results from ab initio molecular dynamics simulations or experimental data. The developed NN-ML potential is also used to perform molecular dynamics simulations to study the structures of Fe-Si alloys with various compositions under rapid solidification conditions. The short-ranged orders in the rapidly solidified Fe-Si alloys are also analyzed by a cluster alignment method. Interatomic potential ; Molecular dynamics ; Materials science ; Cluster (spacecraft) ; Work (physics) ; Artificial neural network ; Ab initio ; Chemical physics ; Statistical physics ; Thermodynamics ; Computer science ; Machine learning ; Computational chemistry ; Physics ; Chemistry Machine Learning in Materials Science ; Microstructure and mechanical properties ; Magnetic Properties and Applications
Authors: Jong Seung Kim | Dong Hyeon Mok | Heejin Kim | Seoin Back
Source: ACS Applied Materials & Interfaces (article, 2023-11-04)
Field: Materials Science — Materials Chemistry

Accelerating the Search for New Solid Electrolytes: Exploring Vast Chemical Space with Machine Learning-Enabled Computational Calculations Discovering new solid electrolytes (SEs) is essential to achieving higher safety and better energy density for all-solid-state lithium batteries. In this work, we report machine learning (ML)-assisted high-throughput virtual screening (HTVS) results to identify new SE materials. This approach expands the chemical space to explore by substituting elements of prototype structures and accelerates an evaluation of properties by applying various ML models. The screening results in a few candidate materials, which are validated by density functional theory calculations and ab initio molecular dynamics simulations. The shortlisted oxysulfide materials satisfy key properties to be successful SEs. The advanced screening method presented in this work will accelerate the discovery of energy materials for related applications. Materials science ; Density functional theory ; Chemical space ; Virtual screening ; Fast ion conductor ; Work (physics) ; Space (punctuation) ; Electrolyte ; Solid-state ; Computer science ; Lithium (medication) ; Nanotechnology ; Molecular dynamics ; Engineering physics ; Computational chemistry ; Drug discovery ; Thermodynamics ; Physical chemistry ; Physics ; Electrode Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Jianan Zhang | Aditya Koneru | Subramanian K. R. S. Sankaranarayanan | Carmen M. Lilley
Source: ACS Applied Materials & Interfaces (article, 2023-04-11)
Field: Materials Science — Materials Chemistry

Graph Neural Network Guided Evolutionary Search of Grain Boundaries in 2D Materials Grain boundaries (GBs) in two-dimensional (2D) materials are known to dramatically impact material properties ranging from the physical, chemical, mechanical, electronic, and optical, to name a few. Predicting a range of physically realistic GB structures for 2D materials is critical to exercising control over their properties. This, however, is nontrivial given the vast structural and configurational (defect) search space between lateral 2D sheets with varying misfits. Here, in a departure from traditional evolutionary search methods, we introduce a workflow that combines the Graph Neural Network (GNN) and an evolutionary algorithm for the discovery and design of novel 2D lateral interfaces. We use a representative 2D material, blue phosphorene (BP), and identify 2D GB structures to test the efficacy of our GNN model. The GNN was trained with a computationally inexpensive machine learning bond order potential (Tersoff formalism) and density functional theory (DFT). Systematic downsampling of the training data sets indicates that our model can predict structural energy under 0.5% mean absolute error with sparse (<2000) DFT generated energy labels for training. We further couple the GNN model with a multiobjective genetic algorithm (MOGA) and demonstrate strong accuracy in the ability of the GNN to predict GBs. Our method is generalizable, is material agnostic, and is anticipated to accelerate the discovery of 2D GB structures. Artificial neural network ; Materials science ; Computer science ; Workflow ; Evolutionary algorithm ; Algorithm ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; 2D Materials and Applications ; Graphene research and applications
Authors: Rishikesh Magar | Amir Barati Farimani
Source: Computational Materials Science (article, 2023-04-10)
Field: Materials Science — Materials Chemistry

Learning from mistakes: Sampling strategies to efficiently train machine learning models for material property prediction Recent advances in machine learning (ML) based methodologies have accelerated the prediction of the physical properties of materials. These ML models, however, rely on large amounts of simulated or experimental data to make a reliable prediction. This dependence on large amounts of data can be a roadblock to building ML models since collecting the data is prohibitively expensive and time-consuming. In this work, we propose two sampling strategies to reliably train machine learning models in the lowest amounts of data. Our algorithms alleviate the need to generate large datasets to train machine learning models. We demonstrate the effectiveness of these sampling strategies by improving the performance of Crystal Graph Convolutional Neural Network (CGCNN) on four different datasets. Using the proposed strategies, we can reach the benchmark performance of CGCNN models in fewer data samples. Computer science ; Machine learning ; Benchmark (surveying) ; Artificial intelligence ; Convolutional neural network ; Sampling (signal processing) ; Artificial neural network ; Graph ; Property (philosophy) ; Predictive modelling ; Deep learning ; Data mining ; Data sampling ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Haoyang Ni | Zhenyao Wu | Xinyi Wu | Jacob Smith | Michael J. Zachman | Jian‐Min Zuo | Lili Ju | Guannan Zhang | Miaofang Chi
Source: Nano Letters (article, 2023-08-11)
Field: Materials Science — Surfaces, Coatings and Films

Quantifying Atomically Dispersed Catalysts Using Deep Learning Assisted Microscopy The catalytic performance of atomically dispersed catalysts (ADCs) is greatly influenced by their atomic configurations, such as atom-atom distances, clustering of atoms into dimers and trimers, and their distributions. Scanning transmission electron microscopy (STEM) is a powerful technique for imaging ADCs at the atomic scale; however, most STEM analyses of ADCs thus far have relied on human labeling, making it difficult to analyze large data sets. Here, we introduce a convolutional neural network (CNN)-based algorithm capable of quantifying the spatial arrangement of different adatom configurations. The algorithm was tested on different ADCs with varying support crystallinity and homogeneity. Results show that our algorithm can accurately identify atom positions and effectively analyze large data sets. This work provides a robust method to overcome a major bottleneck in STEM analysis for ADC catalyst research. We highlight the potential of this method to serve as an on-the-fly analysis tool for catalysts in future in situ microscopy experiments. Scanning transmission electron microscopy ; Bottleneck ; Convolutional neural network ; Catalysis ; Materials science ; Atomic units ; Crystallinity ; Cluster analysis ; Artificial neural network ; Transmission electron microscopy ; Nanotechnology ; Microscopy ; Atom (system on chip) ; Computer science ; Homogeneity (statistics) ; Biological system ; Artificial intelligence ; Chemistry ; Physics ; Machine learning ; Optics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science