jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Microsoft Research AI Science | Microsoft Azure Quantum
Source: arXiv (Cornell University) (preprint, 2023-11-13)
Field: Materials Science — Materials Chemistry

The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4 In recent years, groundbreaking advancements in natural language processing have culminated in the emergence of powerful large language models (LLMs), which have showcased remarkable capabilities across a vast array of domains, including the understanding, generation, and translation of natural language, and even tasks that extend beyond language processing. In this report, we delve into the performance of LLMs within the context of scientific discovery, focusing on GPT-4, the state-of-the-art language model. Our investigation spans a diverse range of scientific areas encompassing drug discovery, biology, computational chemistry (density functional theory (DFT) and molecular dynamics (MD)), materials design, and partial differential equations (PDE). Evaluating GPT-4 on scientific tasks is crucial for uncovering its potential across various research domains, validating its domain-specific expertise, accelerating scientific progress, optimizing resource allocation, guiding future model development, and fostering interdisciplinary research. Our exploration methodology primarily consists of expert-driven case assessments, which offer qualitative insights into the model's comprehension of intricate scientific concepts and relationships, and occasionally benchmark testing, which quantitatively evaluates the model's capacity to solve well-defined domain-specific problems. Our preliminary exploration indicates that GPT-4 exhibits promising potential for a variety of scientific applications, demonstrating its aptitude for handling complex problem-solving and knowledge integration tasks. Broadly speaking, we evaluate GPT-4's knowledge base, scientific understanding, scientific numerical calculation abilities, and various scientific prediction capabilities. Computer science ; Context (archaeology) ; Data science ; Domain (mathematical analysis) ; Resource (disambiguation) ; Comprehension ; Benchmark (surveying) ; Computational model ; Management science ; Scientific modelling ; Cognitive science ; Artificial intelligence ; Epistemology ; Programming language ; Psychology ; Engineering ; Biology Machine Learning in Materials Science
Authors: Waqia Tahir | Talat Zeeshan | Salma Waseem | Muhammad Danish Ali | Zohra Nazir Kayani | Zill-e-Huma Aftab | S Mehtab | Safa Ezzine
Source: Scientific Reports (article, 2023-09-21)
Field: Materials Science — Materials Chemistry

Impact of silver substitution on the structural, magnetic, optical, and antibacterial properties of cobalt ferrite Silver-doped Cobalt Ferrite nanoparticles Ag<sub>x</sub>Co<sub>1-x</sub>Fe<sub>2</sub>O<sub>4</sub> with concentrations (x = 0, 0.05, 0.1, 0.15) have been prepared using a hydrothermal technique. The XRD pattern confirms the formation of the spinel phase of CoFe<sub>2</sub>O<sub>4</sub> and the presence of Ag ions in the spinel structure. The spinel phase Ag<sub>x</sub>Co<sub>1-x</sub>Fe<sub>2</sub>O<sub>4</sub> nanoparticles are confirmed by FTIR analysis by the major bands formed at 874 and 651 cm<sup>-1</sup>, which represent the tetrahedral and octahedral sites. The analysis of optical properties reveals an increase in band gap energy with increasing concentration of the dopant. The energy band gap values depicted for prepared nanoparticles with concentrations x = 0, 0.05, 0.1, 0.15 are 3.58 eV, 3.08 eV, 2.93 eV, and 2.84 eV respectively. Replacement of the Co<sup>2+</sup> ion with the nonmagnetic Ag<sup>2+</sup> ion causes a change in saturation magnetization, with Ms values of 48.36, 29.06, 40.69, and 45.85 emu/g being recorded. The CoFe<sub>2</sub>O<sub>4</sub> and Ag<sup>2+</sup> CoFe<sub>2</sub>O<sub>4</sub> nanoparticles were found to be effective against the Acinetobacter Lwoffii and Moraxella species, with a high inhibition zone value of x = 0.15 and 8 × 8 cm against bacteria. It is suggested that, by the above results, the synthesized material is suitable for memory storage devices and antibacterial activity. Spinel ; Materials science ; Cobalt ; Superparamagnetism ; Dopant ; Band gap ; Ferrite (magnet) ; Octahedron ; Nanoparticle ; Ion ; Analytical Chemistry (journal) ; Doping ; Hydrothermal circulation ; Nuclear chemistry ; Magnetization ; Nanotechnology ; Chemical engineering ; Crystallography ; Crystal structure ; Chemistry ; Metallurgy ; Optoelectronics Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Nanoparticles: synthesis and applications
Authors: Shuya Dong | Yingying Wang | Jiayang Li | Yuanyuan Li | Li Wang | Jinglai Zhang
Source: International Journal of Hydrogen Energy (article, 2023-06-29)
Field: Materials Science — Materials Chemistry

Exploration and design of Mg alloys for hydrogen storage with supervised machine learning Hydrogen storage ; Ternary operation ; Hydrogen ; Magnesium ; Metal ; Materials science ; Thermodynamics ; Hydrogen fuel ; Computer science ; Chemistry ; Algorithm ; Metallurgy ; Physics Hydrogen Storage and Materials ; Magnesium Alloys: Properties and Applications ; MXene and MAX Phase Materials
Authors: Ahmed Badreldin | O. Bouhali | Ahmed Abdel‐Wahab
Source: Advanced Functional Materials (article, 2023-12-12)
Field: Materials Science — Materials Chemistry

Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective Abstract Heterogenous electrocatalysis continues to witness propagating interest in a plethora of non‐limiting electrochemical fields. Of which, water electrolysis has moved from lab‐scale systems to commercial electrolyzers albeit high dependence on historic benchmark noble‐metal based catalysts is still the status quo. Notwithstanding, advances in material groups such as single‐atom catalysts, perovskites, high‐entropy alloys, among others continue to see an increased interest toward utilization in next‐generation electrolyzers. To that end, progress in electrocatalyst discovery techniques is revolutionized through synergistically combining density functional theory (DFT) and machine learning (ML) techniques. The success of ML herein depends on numerous interlinked factors such as the algorithm employed, data availability and accuracy, with descriptors being critical to encapsulate physicochemical perspectives. Historic utilization of ML frameworks in areas other than materials discovery has left a lack of standardization toward appropriating suitable methods of high‐throughput DFT, ML approaches, and feature engineering that bridge the gap between activity‐structure‐electronic relationships. This review outlines needed considerations toward DFT calculations, important criteria during filtering out screened surfaces, and synergistic approaches toward utilizing theoretical and/or experimental datasets for formulating effective ML frameworks. Persisting challenges, perspectives, and recommendations thereof are highlighted to expedite and generalize future work pertaining to high‐volume water electrocatalysis discovery. Electrocatalyst ; Nanotechnology ; Scalability ; Computer science ; Electrolysis of water ; Materials science ; Biochemical engineering ; Electrolysis ; Electrochemistry ; Chemistry ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Yuanbin Liu | Huili Liang | Lei Yang | Guang Yang | Hong-Ao Yang | Shuang Song | Zengxia Mei | Gábor Cśanyi | Bing Cao
Source: Advanced Materials (article, 2023-02-21)
Field: Materials Science — Materials Chemistry

Unraveling Thermal Transport Correlated with Atomistic Structures in Amorphous Gallium Oxide via Machine Learning Combined with Experiments Thermal transport properties of amorphous materials are crucial for their emerging applications in energy and electronic devices. However, understanding and controlling thermal transport in disordered materials remains an outstanding challenge, owing to the intrinsic limitations of computational techniques and the lack of physically intuitive descriptors for complex atomistic structures. Here, it is shown how combining machine-learning-based models and experimental observations can help to accurately describe realistic structures, thermal transport properties, and structure-property maps for disordered materials, which is illustrated by a practical application on gallium oxide. First, the experimental evidence is reported to demonstrate that machine-learning interatomic potentials, generated in a self-guided fashion with minimum quantum-mechanical computations, enable the accurate modeling of amorphous gallium oxide and its thermal transport properties. The atomistic simulations then reveal the microscopic changes in the short-range and medium-range order with density and elucidate how these changes can reduce localization modes and enhance coherences' contribution to heat transport. Finally, a physics-inspired structural descriptor for disordered phases is proposed, with which the underlying relationship between structures and thermal conductivities is predicted in a linear form. This work may shed light on the future accelerated exploration of thermal transport properties and mechanisms in disordered functional materials. Materials science ; Amorphous solid ; Thermal ; Gallium ; Work (physics) ; Molecular dynamics ; Oxide ; Nanotechnology ; Chemical physics ; Statistical physics ; Physics ; Thermodynamics ; Computational chemistry ; Crystallography ; Chemistry Machine Learning in Materials Science ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: L. Natrayan | A. Bhaskar | Pravin P. Patil | S. Kaliappan | M. Dineshkumar | E. S. Esakkiraj
Source: Journal of Nanomaterials (article, 2023-02-22)
Field: Materials Science — Polymers and Plastics

Optimization of Filler Content and Size on Mechanical Performance of Graphene/Hemp/Epoxy-Based Hybrid Composites using Taguchi with ANN Technique The usage of nanofillers in composite materials has grown over time due to various benefits, including superior properties, better adhesion, and high stiffness. To accomplish this, 150, 200, 250, and 300 gsm of hemp fiber mat with various thicknesses and weight proportions of graphene powder, including 0%, 3%, 6%, and 9%, as well as 3, 6, 18, and 25 µm-sized particles, were used. High-speed mechanical stirring was used to evenly mix the nanofiller (nanographene) with the epoxy-based nanocomposites at various loadings. We looked at the bending and interlaminar shear strength (ILSS) properties of hybrid nanomaterials. According to the study, adding 300 gsm of hemp epoxy composites filled with 6 wt% nanographene has significantly improved mechanical properties. The development of a forecasting model to determine the mechanical properties using artificial neural networks (ANN). The constructed model has a significant connection with the test findings. A correlation of 0.9724 for the Levenberg–Marquardt training procedure indicates a significant connection between the predicted and experimental artificial neural models. The observational and projected results for bending and ILSS have &lt;3% and 4% errors, corresponding to the ANN prediction and Taguchi L16 matrix. The potential of ANN for forecasting the bending and ILSS of composite materials is expanded by the close relationship between ANN and experimental findings. The following parameters were used in the current study to determine the flexural strength: graphene content (40.79%), graphene size (34.19%), the number of hemp layers (12.57%), and hemp fiber thickness (11.65%). Similar to ILSS, graphene content accounts for 47.82% of the total, with graphene size (27.87%), hemp fiber thickness (11.80%), and the number of hemp layers (also 11.80%) all contributing (11.78%). Materials science ; Composite material ; Epoxy ; Flexural strength ; Taguchi methods ; Composite number ; Graphene ; Bending ; Orthogonal array Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Carbon Nanotubes in Composites
Authors: Yubing Liu | Hao Wu | Qian Wang
Source: Journal of Materials Chemistry A (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Strategies to improve the photocatalytic performance of covalent triazine frameworks Various strategies for improving the photocatalytic performance of covalent triazine frameworks, including molecular design, structural regulation and the creation of heterostructures, are summarized. Triazine ; Photocatalysis ; Covalent bond ; Nanotechnology ; Computer science ; Materials science ; Chemistry ; Polymer chemistry ; Organic chemistry ; Catalysis Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Caching and Content Delivery
Authors: Zezhou Zhang | Chuanchuan Yang | Yifeng Qin | Hao Feng | Jiqiang Feng | Hongbin Li
Source: Nanophotonics (article, 2023-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Diffusion probabilistic model based accurate and high‐degree‐of‐freedom metasurface inverse design Conventional meta-atom designs rely heavily on researchers' prior knowledge and trial-and-error searches using full-wave simulations, resulting in time-consuming and inefficient processes. Inverse design methods based on optimization algorithms, such as evolutionary algorithms, and topological optimizations, have been introduced to design metamaterials. However, none of these algorithms are general enough to fulfill multi-objective tasks. Recently, deep learning methods represented by generative adversarial networks (GANs) have been applied to inverse design of metamaterials, which can directly generate high-degree-of-freedom meta-atoms based on S-parameters requirements. However, the adversarial training process of GANs makes the network unstable and results in high modeling costs. This paper proposes a novel metamaterial inverse design method based on the diffusion probability theory. By learning the Markov process that transforms the original structure into a Gaussian distribution, the proposed method can gradually remove the noise starting from the Gaussian distribution and generate new high-degree-of-freedom meta-atoms that meet S-parameters conditions, which avoids the model instability introduced by the adversarial training process of GANs and ensures more accurate and high-quality generation results. Experiments have proven that our method is superior to representative methods of GANs in terms of model convergence speed, generation accuracy, and quality. Computer science ; Metamaterial ; Degrees of freedom (physics and chemistry) ; Inverse ; Engineering design process ; Probabilistic logic ; Algorithm ; Convergence (economics) ; Mathematical optimization ; Process (computing) ; Gaussian process ; Gaussian ; Theoretical computer science ; Artificial intelligence ; Mathematics Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Animal Vocal Communication and Behavior
Authors: Meng Wang | Bingbing Wang | Ruihua Zhang | Zihao Wu | Xinqing Xiao
Source: Food Quality and Safety (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Flexible Vis/NIR wireless sensing system for banana monitoring Abstract Objectives The quality of the fruit seriously affects the economic value of the fruit. Fruit quality is related to many ripening parameters, such as soluble solid content (SSC), pH, and firmness (FM), and is a complex process. Traditional methods are inefficient, do not guarantee quality, and do not adapt to the current rhythm of the fruit market. In this paper, a was designed and implemented for quality prediction and maturity level classification of Philippine Cavendish bananas. Materials and Methods The quality changes of bananas in different stages were analyzed. Twelve light intensity reflectance values for each maturity stage were compared to conventionally measured SSC, FM, PH, and color space. Results Our device can be compared with traditional forms of quality measurement. The experimental results show that the established predictive model with specific preprocessing and modeling algorithms can effectively determine various banana quality parameters (SSC, pH, FM, L*, a*, and b*). The RPD values of SSC and a* were greater than 3.0, the RPD values of L* and b* were between 2.5 and 3.0, and the pH and FM were between 2.0 and 2.5. In addition, a new banana maturity level classification method (FSC) was proposed, and the results showed that the method could effectively classify the maturity level classes (i.e. four maturity levels) with an accuracy rate of up to 97.5%. Finally, the MLR and FSC models are imported into the MCU to realize the near-range and long-range real-time display of data. Conclusions These methods can also be applied more broadly to fruit quality detection, providing a basic framework for future research. Maturity (psychological) ; Preprocessor ; Ripening ; Mathematics ; Quality (philosophy) ; Computer science ; Biological system ; Environmental science ; Horticulture ; Statistics ; Artificial intelligence ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Sheta M. Sheta | Mohamed A. Hamouda | Omnia I. Ali | A. T. Kandil | Reda R. Sheha | Said M. El‐Sheikh
Source: RSC Advances (review, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Recent progress in high-performance environmental impacts of the removal of radionuclides from wastewater based on metal–organic frameworks: a review The nuclear industry is rapidly developing and the effective management of nuclear waste and monitoring the nuclear fuel cycle are crucial. The presence of various radionuclides such as uranium (U), europium (Eu), technetium (Tc), iodine (I), thorium (Th), cesium (Cs), and strontium (Sr) in the environment is a major concern, and the development of materials with high adsorption capacity and selectivity is essential for their effective removal. Metal-organic frameworks (MOFs) have recently emerged as promising materials for removing radioactive elements from water resources due to their unique properties such as tunable pore size, high surface area, and chemical structure. This review provides an extensive analysis of the potential of MOFs as adsorbents for purifying various radionuclides rather than using different techniques such as precipitation, filtration, ion exchange, electrolysis, solvent extraction, and flotation. This review discusses various MOF fabrication methods, focusing on minimizing environmental impacts when using organic solvents and solvent-free methods, and covers the mechanism of MOF adsorption towards radionuclides, including macroscopic and microscopic views. It also examines the effectiveness of MOFs in removing radionuclides from wastewater, their behavior on exposure to high radiation, and their renewability and reusability. We conclude by emphasizing the need for further research to optimize the performance of MOFs and expand their use in real-world applications. Overall, this review provides valuable insights into the potential of MOFs as efficient and durable materials for removing radioactive elements from water resources, addressing a critical issue in the nuclear industry. Adsorption ; Radioactive waste ; Nuclear fuel cycle ; Radionuclide ; Metal-organic framework ; Environmental science ; Wastewater ; Uranium ; Waste management ; Nuclear industry ; Materials science ; Chemistry ; Environmental engineering ; Engineering Radioactive element chemistry and processing ; Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Characterization
Authors: Mao‐Yin Ran | Shenghua Zhou | Wenbo Wei | Bing‐Xuan Li | Xin‐Tao Wu | Hua Lin | Qi‐Long Zhu
Source: Small (article, 2023-02-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rational Design of a Rare‐Earth Oxychalcogenide Nd<sub>3</sub>[Ga<sub>3</sub>O<sub>3</sub>S<sub>3</sub>][Ge<sub>2</sub>O<sub>7</sub>] with Superior Infrared Nonlinear Optical Performance Inorganic chalcogenides have been studied as the most promising infrared (IR) nonlinear optical (NLO) candidates for the past decades. However, it is proven difficult to discover high-performance materials that combine the often-incompatible properties of large energy gap (E<sub>g</sub> ) and strong second harmonic generation (SHG) response (d<sub>eff</sub> ), especially for rare-earth chalcogenides. Herein, centrosymmetric Cs<sub>3</sub> [Sb<sub>3</sub> O<sub>6</sub> ][Ge<sub>2</sub> O<sub>7</sub> ] is selected as a maternal structure and a new noncentrosymmetric rare-earth oxychalcogenide, namely, Nd<sub>3</sub> [Ga<sub>3</sub> O<sub>3</sub> S<sub>3</sub> ][Ge<sub>2</sub> O<sub>7</sub> ], is successfully designed and obtained by the module substitution strategy for the first time. Especially, Nd<sub>3</sub> [Ga<sub>3</sub> O<sub>3</sub> S<sub>3</sub> ][Ge<sub>2</sub> O<sub>7</sub> ] is the first case of breaking the trade-off relationship between wide E<sub>g</sub> (>3.5 eV) and large d<sub>eff</sub> (>0.5 × AgGaS<sub>2</sub> ) in rare-earth chalcogenide system, and thus displays an outstanding IR-NLO comprehensive performance. Detailed structure analyses and theoretical studies reveal that the NLO effect originates mainly from the cooperation of heteroanionic [GaO<sub>2</sub> S<sub>2</sub> ] and [NdO<sub>2</sub> S<sub>6</sub> ] asymmetric building blocks. This work not only presents an excellent rare-earth IR-NLO candidate, but also plays a crucial role in the rational structure design of other NLO materials in which both large E<sub>g</sub> and strong d<sub>eff</sub> are pursued. Rare earth ; Chalcogenide ; Materials science ; Nonlinear optical ; Germanium compounds ; Earth (classical element) ; Optoelectronics ; Crystallography ; Nanotechnology ; Germanium ; Chemistry ; Physics ; Nonlinear system ; Metallurgy Crystal Structures and Properties ; High-pressure geophysics and materials ; Solid-state spectroscopy and crystallography
Authors: K. Saito | Masatomo Yashima
Source: Nature Communications (article, 2023-11-17)
Field: Materials Science — Materials Chemistry

High proton conductivity within the ‘Norby gap’ by stabilizing a perovskite with disordered intrinsic oxygen vacancies Proton conductors are attractive materials with a wide range of potential applications such as proton-conducting fuel cells (PCFCs). The conventional strategy to enhance the proton conductivity is acceptor doping into oxides without oxygen vacancies. However, the acceptor doping results in proton trapping near dopants, leading to the high apparent activation energy and low proton conductivity at intermediate and low temperatures. The hypothetical cubic perovskite BaScO<sub>2.5</sub> may have intrinsic oxygen vacancies without the acceptor doping. Herein, we report that the cubic perovskite-type BaSc<sub>0.8</sub>Mo<sub>0.2</sub>O<sub>2.8</sub> stabilized by Mo donor-doing into BaScO<sub>2.5</sub> exhibits high proton conductivity within the 'Norby gap' (e.g., 0.01 S cm<sup>-1</sup> at 320 °C) and high chemical stability under oxidizing, reducing and CO<sub>2</sub> atmospheres. The high proton conductivity of BaSc<sub>0.8</sub>Mo<sub>0.2</sub>O<sub>2.8</sub> at intermediate and low temperatures is attributable to high proton concentration, high proton mobility due to reduced proton trapping, and three-dimensional proton diffusion in the cubic perovskite stabilized by the Mo-doping into BaScO<sub>2.5</sub>. The donor doping into the perovskite with disordered intrinsic oxygen vacancies would be a viable strategy towards high proton conductivity at intermediate and low temperatures. Perovskite (structure) ; Conductivity ; Proton ; Materials science ; Doping ; Acceptor ; Oxygen ; Dopant ; Proton transport ; Chemical physics ; Chemistry ; Crystallography ; Physical chemistry ; Condensed matter physics ; Optoelectronics ; Physics Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Fuel Cells and Related Materials