jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ganeshraja Ayyakannu Sundaram | Govinda raj Muniyandi | Jayashree Ethiraj | P. Vairavel | A. Santhana Krishna Kumar
Source: ChemEngineering (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Introduction and Advancements in Room-Temperature Ferromagnetic Metal Oxide Semiconductors for Enhanced Photocatalytic Performance Recent advancements in the field of room-temperature ferromagnetic metal oxide semiconductors (RTFMOS) have revealed their promising potential for enhancing photocatalytic performance. This review delves into the combined investigation of the photocatalytic and ferromagnetic properties at room temperature, with a particular focus on metal oxides like TiO2, which have emerged as pivotal materials in the fields of magnetism and environmental remediation. Despite extensive research efforts, the precise mechanism governing the interplay between ferromagnetism and photocatalysis in these materials remains only partially understood. Several crucial factors contributing to magnetism, such as oxygen vacancies and various metal dopants, have been identified. Numerous studies have highlighted the significant role of these factors in driving room-temperature ferromagnetism and photocatalytic activity in wide-bandgap metal oxides. However, establishing a direct correlation between magnetism, oxygen vacancies, dopant concentration, and photocatalysis has posed significant challenges. These RTFMOS hold immense potential to significantly boost photocatalytic efficiency, offering promising solutions for diverse environmental- and energy-related applications, including water purification, air pollution control, and solar energy conversion. This review aims to offer a comprehensive overview of recent advancements in understanding the magnetism and photocatalytic behavior of metal oxides. By synthesizing the latest findings, this study sheds light on the considerable promise of RTFMOS as effective photocatalysts, thus contributing to advancements in environmental remediation and related fields. Photocatalysis ; Ferromagnetism ; Materials science ; Oxide ; Metal ; Semiconductor ; Nanotechnology ; Magnetic semiconductor ; Engineering physics ; Optoelectronics ; Metallurgy ; Condensed matter physics ; Chemistry ; Physics ZnO doping and properties ; Copper-based nanomaterials and applications ; Gas Sensing Nanomaterials and Sensors
Authors: Zehua Wang | Li Wang | Hao Zhang | Hong Xu | Xiangming He
Source: Nano Convergence (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Materials descriptors of machine learning to boost development of lithium-ion batteries Traditional methods for developing new materials are no longer sufficient to meet the needs of the human energy transition. Machine learning (ML) artificial intelligence (AI) and advancements have caused materials scientists to realize that using AI/ML to accelerate the development of new materials for batteries is a powerful potential tool. Although the use of certain fixed properties of materials as descriptors to act as a bridge between the two separate disciplines of AI and materials chemistry has been widely investigated, many of the descriptors lack universality and accuracy due to a lack of understanding of the mechanisms by which AI/ML operates. Therefore, understanding the underlying operational mechanisms and learning logic of AI/ML has become mandatory for materials scientists to develop more accurate descriptors. To address those challenges, this paper reviews previous work on AI, machine learning and materials descriptors and introduces the basic logic of AI and machine learning to help materials developers understand their operational mechanisms. Meanwhile, the paper also compares the accuracy of different descriptors and their advantages and disadvantages and highlights the great potential value of accurate descriptors in AI/machine learning applications for battery research, as well as the challenges of developing accurate material descriptors. Lithium (medication) ; Computer science ; Ion ; Artificial intelligence ; Machine learning ; Medicine ; Chemistry ; Internal medicine Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Luca Foppiano | G. Lambard | Toshiyuki Amagasa | Masashi Ishii
Source: Science and Technology of Advanced Materials Methods (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

Mining experimental data from materials science literature with large language models: an evaluation study This study is dedicated to assessing the capabilities of large language models (LLMs) such as GPT-3.5-Turbo, GPT-4, and GPT-4-Turbo in extracting structured information from scientific documents in materials science. To this end, we primarily focus on two critical tasks of information extraction: (i) a named entity recognition (NER) of studied materials and physical properties and (ii) a relation extraction (RE) between these entities. Due to the evident lack of datasets within Materials Informatics (MI), we evaluated using SuperMat, based on superconductor research, and MeasEval, a generic measurement evaluation corpus. The performance of LLMs in executing these tasks is benchmarked against traditional models based on the BERT architecture and rule-based approaches (baseline). We introduce a novel methodology for the comparative analysis of intricate material expressions, emphasising the standardisation of chemical formulas to tackle the complexities inherent in materials science information assessment. For NER, LLMs fail to outperform the baseline with zero-shot prompting and exhibit only limited improvement with few-shot prompting. However, a GPT-3.5-Turbo finetuned with the appropriate strategy for RE outperforms all models, including the baseline. Without any fine-tuning, GPT-4 and GPT-4-Turbo display remarkable reasoning and relationship extraction capabilities after being provided with merely a couple of examples, surpassing the baseline. Overall, the results suggest that although LLMs demonstrate relevant reasoning skills in connecting concepts, specialised models are currently a better choice for tasks requiring extracting complex domain-specific entities like materials. These insights provide initial guidance applicable to other materials science sub-domains in future work. Baseline (sea) ; Computer science ; Information extraction ; Informatics ; Data science ; Natural language processing ; Information retrieval ; Artificial intelligence ; Engineering Machine Learning in Materials Science ; Topic Modeling ; Data Quality and Management
Authors: R. El-Shater | S.T. Assar | Basem E. Keshta | Ali H. Gemeay | A. M. Abdelghany | E. K. Abdel-Khalek | F. Fakhry
Source: Journal of Sol-Gel Science and Technology (article, 2024-11-02)
Field: Materials Science — Materials Chemistry

Synthesis and study on structural, magnetic, dielectric properties and impedance spectroscopy of nanosized Mn-doped γ-Fe2O3 for multifunctional applications Abstract Nano structured samples of γ-Mn x Fe 2- x O 3 (0 ≤ x ≤ 1.0, step 0.2) were prepared by a wet-chemical method. The formation of the pure-phase FCC spinel structure of the samples has been confirmed by x-ray diffraction and Fourier Transform Infrared spectroscopy, where their crystallite size increases from 12 to 28 nm by adding Mn 3+ ions. The high-resolution transmission electron microscopy images show spherical-shaped agglomerated nanoparticles with uniform size, confirming the nanostructure of the samples. The X-ray photoelectron spectroscopy and Mössbauer results confirm the existence of Fe 3+ and Mn 3+ ions and the disappearance of the Fe 2+ and Mn 2+ ions in the cubic structure of the γ-Mn x Fe 2- x O 3 . The Brunauer–Emmett–Teller specific surface area of the samples ranges from 36 to 79 m 2 /g, where the sample of x = 0.2 has the largest value. The sigmoid-shaped hysteresis loops reflect the soft magnetic nature of the samples. As the Mn 3+ ion addition increases, the M s and M r increase up to the sample of x = 0.4, then decrease again. The H c , which has lower values, increases as the crystallite size increases, manifesting the single-domain nature of the particles and approaching the superparamagnetic limits. The composition dependence of σ dc , σʹ ac , εʹ , εʺ , and tanδ is dependent on grain size, specific surface area, and cation distribution. Graphical Abstract Dielectric spectroscopy ; Materials science ; Dielectric ; Doping ; Electrical impedance ; Spectroscopy ; Chemical engineering ; Nanotechnology ; Optoelectronics ; Chemistry ; Electrical engineering ; Physical chemistry ; Electrode ; Engineering Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Iron oxide chemistry and applications
Authors: Rahul Suresh | Hardik Bishnoi | Artem V. Kuklin | Atharva Parikh | Мaxim S. Моlokeev | Rajendran Harinarayanan | Sarvesh Gharat | P Hiba
Source: Frontiers in Physics (article, 2024-02-16)
Field: Materials Science — Materials Chemistry

Revolutionizing physics: a comprehensive survey of machine learning applications In the context of the 21st century and the fourth industrial revolution, the substantial proliferation of data has established it as a valuable resource, fostering enhanced computational capabilities across scientific disciplines, including physics. The integration of Machine Learning stands as a prominent solution to unravel the intricacies inherent to scientific data. While diverse machine learning algorithms find utility in various branches of physics, there exists a need for a systematic framework for the application of Machine Learning to the field. This review offers a comprehensive exploration of the fundamental principles and algorithms of Machine Learning, with a focus on their implementation within distinct domains of physics. The review delves into the contemporary trends of Machine Learning application in condensed matter physics, biophysics, astrophysics, material science, and addresses emerging challenges. The potential for Machine Learning to revolutionize the comprehension of intricate physical phenomena is underscored. Nevertheless, persisting challenges in the form of more efficient and precise algorithm development are acknowledged within this review. Data science ; Computer science Machine Learning in Materials Science ; Scientific Computing and Data Management ; Reservoir Engineering and Simulation Methods
Authors: Asmaa A. H. El-Bassuony | R. S. Hafez | Nermen M. S. Matter | H. K. Abdelsalam
Source: JOM (article, 2024-04-24)
Field: Materials Science — Materials Chemistry

Enhancement of Cobalt Bismuth Nano-Ferrite via Heat Treatment to be Applied in High-Frequency and Antimicrobial Applications Abstract Cobalt bismuth nano-ferrite (Co/Bi) with the chemical formula CoBi 0.02 Fe 1.98 O 4 was produced using a simple flash auto-combustion method at three different temperatures: as-prepared, 600°C, and 800°C. A single-phase spinel structure was confirmed using X-ray diffraction, and the nano-scale morphology was examined using AFM (atomic force microscopy). Magnetic measurements demonstrated that increasing the annealing temperature increased the saturation magnetization M s by 1.3 times. However, the coercivity H c changed from semi-hard ferrite (as-prepared sample) to soft ferrite (Co/Bi nano-ferrite at 800°C) and reduced 10.7 times that of as-prepared nanoparticles. Therefore, the 800°C Co/Bi nano-ferrite with a low coercive field is recommended for transformers, recording heads, inductor cores, magnetic shielding, and microwave devices. The as-prepared sample and that at 600°C displayed super-high microwave frequency (SHF) in the X band in high-frequency applications calculated from magnetic measurement. The 800°C sample also has an extremely high microwave frequency in the Ku band, which is utilized in radar and satellite communications. Antimicrobial characterization showed that raising the annealing temperature increased the effectiveness of the samples against tested microorganisms. Thus, the samples under investigation are highly suggested for ultra-high microwave frequency applications and biological antibacterial nanomaterials. Coercivity ; Materials science ; Microwave ; Ferrite (magnet) ; Annealing (glass) ; Analytical Chemistry (journal) ; Nanoparticle ; Nanomaterials ; Nuclear magnetic resonance ; Metallurgy ; Nanotechnology ; Composite material ; Chemistry ; Condensed matter physics Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Advancements in Battery Materials
Authors: Lei Li | Qunfeng Cheng
Source: Interdisciplinary materials (article, 2024-07-21)
Field: Materials Science — Materials Chemistry

Anisotropic thermally conductive films based on two‐dimensional nanomaterials Abstract The significant advancement of high‐power densification and miniaturization in modern electronic devices has attracted increasing attention to effective thermal management. The primary objective of thermal management is to transfer excess heat from electronics to the outside environment through the use of thermal conductive materials. The anisotropic thermally conductive films (TCFs) based on two‐dimensional (2D) nanomaterials exhibit outstanding controlled heat transfer capability, which effectively removes hotspots along the in‐plane direction and provides thermal insulation along the cross‐plane direction. However, a comprehensive review of anisotropic TCFs is rarely reported. Herein, we first discuss the intrinsic anisotropic thermal conductivity of 2D nanomaterials for preparing TCFs. Then, the preparation methods and anisotropic thermal conductivity of TCFs have been summarized and discussed. Furthermore, we conclude with the practical applications of TCFs for anisotropy thermal management. Finally, a conclusion of the challenges and outlook of TCFs is provided to promote their development in future scientific research. Anisotropy ; Thermal conductivity ; Electrical conductor ; Nanomaterials ; Materials science ; Miniaturization ; Thermal ; Nanotechnology ; Optics ; Composite material ; Physics ; Thermodynamics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Graphene research and applications
Authors: Stijn De Smedt | B. Attaianese | Ruth Cardinaels
Source: Current Opinion in Colloid & Interface Science (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Direct ink writing of particle-based multiphase materials: From rheology to functionality Direct ink writing (DIW) allows to produce complicated geometries by extruding material from a nozzle. The ink has to meet certain material requirements during and after printing for the object to be successfully produced. Meanwhile, the functionality requirements of the end-use application should be met. This paper attempts to provide the rheological basis and critical view to understand the material requirements for DIW inks and to help making the bridge between the rheology and printability of particle-based multiphase DIW inks while meeting the functional demands of the end product. Colloidal suspensions and Pickering emulsions are often used material classes for DIW. Some of the most important and noteworthy applications are described for both material classes. Thereafter, a more novel particle based multiphase system for DIW, namely capillary suspensions, is briefly discussed. Rheology ; Inkwell ; Materials science ; Particle (ecology) ; Chemical engineering ; Polymer science ; Composite material ; Geology ; Engineering Pickering emulsions and particle stabilization ; Additive Manufacturing and 3D Printing Technologies ; Polymer composites and self-healing
Authors: Ruidong Xue | Helia Hooshmand | Mohammed Isa | Samanta Piano | Richard Leach
Source: Measurement Science and Technology (review, 2024-09-09)
Field: Chemistry — Analytical Chemistry

Applying machine learning to optical metrology: a review Abstract This literature review investigates the integration of machine learning (ML) into optical metrology, unveiling enhancements in both efficiency and effectiveness of measurement processes. With a focus on phase demodulation, unwrapping, and phase-to-height conversion, the review highlights how ML algorithms have transformed traditional optical metrology techniques, offering improved speed, accuracy, and data processing capabilities. Efficiency improvements are underscored by advancements in data generation, intelligent sampling, and processing strategies, where ML algorithms have accelerated the metrological evaluations. Effectiveness is enhanced in measurement precision, with ML providing robust solutions to complex pattern recognition and noise reduction challenges. Additionally, the role of parallel computing using graphics processing units and field programmable gate arrays is emphasised, showcasing their importance in supporting the computationally intensive ML algorithms for real-time processing. This review culminates in identifying future research directions, emphasising the potential of advanced ML models and broader applications within optical metrology. Through this investigation, the review articulates a future where optical metrology, empowered by ML, achieves improved levels of operational efficiency and effectiveness. Metrology ; Computer science ; Artificial intelligence ; Machine learning ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Surface Roughness and Optical Measurements
Authors: Paulina Baczewska | Michał Kulczykowski | Bartosz K. Zambroń | Joanna A. Jaszczewska‐Adamczak | Zbigniew Pakulski | Rafał Roszak | Bartosz A. Grzybowski | Jacek Młynarski
Source: Angewandte Chemie International Edition (article, 2024-06-15)
Field: Chemistry — Inorganic Chemistry

Machine Learning Algorithm Guides Catalyst Choices for Magnesium‐Catalyzed Asymmetric Reactions Organic-chemical literature encompasses large numbers of catalysts and reactions they can effect. Many of these examples are published merely to document the catalysts' scope but do not necessarily guarantee that a given catalyst is "optimal"-in terms of yield or enantiomeric excess-for a particular reaction. This paper describes a Machine Learning model that aims to improve such catalyst-reaction assignments based on the carefully curated literature data. As we show here for the case of asymmetric magnesium catalysis, this model achieves relatively high accuracy and offers out of-the-box predictions successfully validated by experiment, e.g., in synthetically demanding asymmetric reductions or Michael additions. Catalysis ; Scope (computer science) ; Magnesium ; Yield (engineering) ; Computer science ; Chemistry ; Enantioselective synthesis ; Combinatorial chemistry ; Biochemical engineering ; Organic chemistry ; Materials science ; Engineering Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Chemical Synthesis and Analysis
Authors: Xinyan Liu | Hong‐Jie Peng
Source: Engineering (article, 2024-01-05)
Field: Materials Science — Materials Chemistry

Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine Learning Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes, and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility. Computational high-throughput screening presents a viable solution to this challenge, as machine learning (ML) has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information. This review focuses on recent progress in applying ML in adsorption energy prediction, which predominantly quantifies the catalytic potential of a solid catalyst. ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed. At the end of the review, an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied. We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and, ultimately, reshape the chemical industry and energy landscape. Leverage (statistics) ; Catalysis ; Biochemical engineering ; Computer science ; Reactivity (psychology) ; Throughput ; Nanotechnology ; Process engineering ; Artificial intelligence ; Materials science ; Engineering ; Chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Lanting Xu | Jiazhou Zhu | Xiaodong Shen | Jiashuang Chai | Lei Shi | Bin Wu | Wei Li | Dawei Ma
Source: Angewandte Chemie International Edition (article, 2024-08-27)
Field: Chemistry — Organic Chemistry

6‐Hydroxy Picolinohydrazides Promoted Cu(I)‐Catalyzed Hydroxylation Reaction in Water: Machine‐Learning Accelerated Ligands Design and Reaction Optimization Hydroxylated (hetero)arenes are privileged motifs in natural products, materials, small-molecule pharmaceuticals and serve as versatile intermediates in synthetic organic chemistry. Herein, we report an efficient Cu(I)/6-hydroxy picolinohydrazide-catalyzed hydroxylation reaction of (hetero)aryl halides (Br, Cl) in water. By establishing machine learning (ML) models, the design of ligands and optimization of reaction conditions were effectively accelerated. The N-(1,3-dimethyl-9H- carbazol-9-yl)-6-hydroxypicolinamide (L32, 6-HPA-DMCA) demonstrated high efficiency for (hetero)aryl bromides, promoting hydroxylation reactions with a minimal catalyst loading of 0.01 mol % (100 ppm) at 80 °C to reach 10000 TON; for substrates containing sensitive functional groups, the catalyst loading needs to be increased to 3.0 mol % under near-room temperature conditions. N-(2,7-Di-tert-butyl-9H-carbazol-9-yl)-6-hydroxypicolinamide (L42, 6-HPA-DTBCA) displayed superior reaction activity for chloride substrates, enabling hydroxylation reactions at 100 °C with 2-3 mol % catalyst loading. These represent the state of art for both lowest catalyst loading and temperature in the copper-catalyzed hydroxylation reactions. Furthermore, this method features a sustainable and environmentally friendly solvent system, accommodates a wide range of substrates, and shows potential for developing robust and scalable synthesis processes for key pharmaceutical intermediates. Hydroxylation ; Catalysis ; Chemistry ; Aryl ; Halide ; Solvent ; Reactive intermediate ; Green chemistry ; Combinatorial chemistry ; Reaction intermediate ; Organic chemistry ; Reaction mechanism ; Enzyme Catalytic C–H Functionalization Methods ; Oxidative Organic Chemistry Reactions ; Chemical Synthesis and Reactions