jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhilong Wang | An Chen | Kehao Tao | Junfei Cai | Yanqiang Han | Jing Gao | Simin Ye | Shiwei Wang | Imran Ali | Jinjin Li
Source: npj Computational Materials (article, 2023-07-26)
Field: Materials Science — Materials Chemistry

AlphaMat: a material informatics hub connecting data, features, models and applications Abstract The development of modern civil industry, energy and information technology is inseparable from the rapid explorations of new materials. However, only a small fraction of materials being experimentally/computationally studied in a vast chemical space. Artificial intelligence (AI) is promising to address this gap, but faces many challenges, such as data scarcity and inaccurate material descriptors. Here, we develop an AI platform, AlphaMat, that can complete data preprocessing and downstream AI models. With high efficiency and accuracy, AlphaMat exhibits strong powers to model typical 12 material attributes (formation energy, band gap, ionic conductivity, magnetism, bulk modulus, etc.). AlphaMat’s capabilities are further demonstrated to discover thousands of new materials for use in specific domains. AlphaMat does not require users to have strong programming experience, and its effective use will facilitate the development of materials informatics, which is of great significance for the implementation of AI for Science (AI4S). Computer science ; Materials informatics ; Data science ; Big data ; Informatics ; Space (punctuation) ; Preprocessor ; Artificial intelligence ; Data mining ; Health informatics ; Engineering ; Engineering informatics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Jiyun Park | Boyuan Xu | Jie Pan | Dawei Zhang | Stephan Lany | Xingbo Liu | Jian Luo | Yue Qi
Source: npj Computational Materials (article, 2023-02-28)
Field: Materials Science — Materials Chemistry

Accurate prediction of oxygen vacancy concentration with disordered A-site cations in high-entropy perovskite oxides Abstract Entropic stabilized ABO 3 perovskite oxides promise many applications, including the two-step solar thermochemical hydrogen (STCH) production. Using binary and quaternary A-site mixed {A}FeO 3 as a model system, we reveal that as more cation types, especially above four, are mixed on the A-site, the cell lattice becomes more cubic-like but the local Fe–O octahedrons are more distorted. By comparing four different Density Functional Theory-informed statistical models with experiments, we show that the oxygen vacancy formation energies ( $${E}_{V}^{f}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>E</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>V</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> ) distribution and the vacancy interactions must be considered to predict the oxygen non-stoichiometry ( δ ) accurately. For STCH applications, the $${E}_{V}^{f}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mrow> <mml:mi>E</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>V</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> </mml:msubsup> </mml:math> distribution, including both the average and the spread, can be optimized jointly to improve Δ δ (difference of δ between the two-step conditions) in some hydrogen production levels. This model can be used to predict the range of water splitting that can be thermodynamically improved by mixing cations in {A}FeO 3 perovskites. Algorithm ; Materials science ; Thermodynamics ; Computer science ; Physics Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Electrocatalysts for Energy Conversion
Authors: Umit Volkan Ucak | Islambek Ashyrmamatov | Juyong Lee
Source: Journal of Cheminformatics (article, 2023-05-29)
Field: Materials Science — Materials Chemistry

Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization Tokenization is an important preprocessing step in natural language processing that may have a significant influence on prediction quality. This research showed that the traditional SMILES tokenization has a certain limitation that results in tokens failing to reflect the true nature of molecules. To address this issue, we developed the atom-in-SMILES tokenization scheme that eliminates ambiguities in the generic nature of SMILES tokens. Our results in multiple chemical translation and molecular property prediction tasks demonstrate that proper tokenization has a significant impact on prediction quality. In terms of prediction accuracy and token degeneration, atom-in-SMILES is more effective method in generating higher-quality SMILES sequences from AI-based chemical models compared to other tokenization and representation schemes. We investigated the degrees of token degeneration of various schemes and analyzed their adverse effects on prediction quality. Additionally, token-level repetitions were quantified, and generated examples were incorporated for qualitative examination. We believe that the atom-in-SMILES tokenization has a great potential to be adopted by broad related scientific communities, as it provides chemically accurate, tailor-made tokens for molecular property prediction, chemical translation, and molecular generative models. Computer science ; Lexical analysis ; Quality (philosophy) ; Atom (system on chip) ; Natural language processing ; Information retrieval ; Physics ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Gauhar Rehman | Muhammad Umar | Nasrullah Shah | Muhammad Hamayun | Abid Ali | Waliullah Khan | Arif Khan | Sajjad Ahmad | Abdulwahed Fahad Alrefaei | Mikhlid H. Almutairi | Yong‐Sun Moon | Sajid Ali
Source: Pharmaceuticals (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Green Synthesis and Characterization of Silver Nanoparticles Using Azadirachta indica Seeds Extract: In Vitro and In Vivo Evaluation of Anti-Diabetic Activity The results showed a considerable drop in blood sugar levels, including pancreatic and liver cell regeneration, demonstrating that AI-AgNPs have strong anti-diabetic potential. Azadirachta ; Silver nanoparticle ; Chemistry ; In vivo ; Nuclear chemistry ; Streptozotocin ; Fourier transform infrared spectroscopy ; Traditional medicine ; Diabetes mellitus ; Nanoparticle ; Medicine ; Biology ; Materials science ; Biotechnology ; Nanotechnology ; Endocrinology Nanoparticles: synthesis and applications ; Medicinal Plants and Neuroprotection ; Diverse Scientific Research Studies
Authors: Georgios P. Gakis | Ioannis G. Aviziotis | Costas A. Charitidis
Source: Environmental Science Nano (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Metal and metal oxide nanoparticle toxicity: moving towards a more holistic structure–activity approach A classification SAR approach for the toxicity of metal and metal oxide nanoparticles, towards various cells and organisms. The analysis revealed key nanoparticle properties that induce toxicity. Metal ; Toxicity ; Nanoparticle ; Metal toxicity ; Oxide ; Nanotechnology ; Materials science ; Chemistry ; Metallurgy ; Organic chemistry Nanoparticles: synthesis and applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: Minjia Yuan | Qian Li | Yang Gao | Chao He | Mohsen Adli | Changzhu Wu | Hongju Zhou | Xianglin Luo | Lang Ma | Chong Cheng
Source: Advanced Functional Materials (article, 2023-06-03)
Field: Materials Science — Materials Chemistry

Tunable Structured Metal Oxides for Biocatalytic Therapeutics Abstract The past few decades have witnessed the flourish of creating metal oxides for biocatalytic therapeutics due to their structural diversities, feasible modifications, tunable catalytic sites, and low cost when compared to their natural enzyme counterparts. Here, in this timely review, the most recent progress and future trends in engineering tunable structured metal oxides and decoding their structure‐reactivity relationships for biocatalytic therapeutics is comprehensively summarized. At first, the fundamental activities, evaluations, and mechanisms of metal oxide‐based biocatalysts are carefully disclosed. Subsequently, the merits, design methods, and state‐of‐art achievements of different types of nanostructured and biofunctionalized metal oxides are thoroughly discussed. Thereafter, it provides detailed comments on the catalytic center modulation strategies to engineer metal oxides for efficient reactive oxygen species (ROS)‐catalysis, including atomic catalytic site engineering, heterostructures, and support effects. Furthermore, the representative applications of these ROS‐catalytic metal oxides have been systematically summarized, such as catalytic disinfections, cancer therapies, ROS scavenging and anti‐inflammations, biocatalytic sensors, as well as corresponding toxicities. Finally, current challenges and future perspectives are also highlighted. It is believed that this review can provide cutting‐edge and multidisciplinary instruction for the future design of ROS‐catalytic metal oxides and stimulate their widespread utilization in broad therapeutic applications. Materials science ; Nanotechnology ; Metal ; Engineering ethics ; Metallurgy Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: N. Sivagangi Reddy | Chang‐Sik Ha
Source: Nanomaterials (review, 2023-07-17)
Field: Materials Science — Polymers and Plastics

Structural Designs of Transparent Polyimide Films with Low Dielectric Properties and Low Water Absorption: A Review The rapid development of communication networks (5G and 6G) that rely on high-speed devices requiring fast and high-quality intra- and inter-terminal signal transmission media has led to a steady increase in the need for high-performance, low-dielectric-constant (D<sub>k</sub>) (<2.5) materials. Consequently, low-dielectric polymeric materials, particularly polyimides (PIs), are very attractive materials that are capable of meeting the requirements of high-performance terminal devices that transmit broadband high-frequency signals. However, such a PI needs to be properly designed with appropriate properties, including a low D<sub>k</sub>, low dielectric loss (D<sub>f</sub>), and low water absorptivity. PI materials are broadly used in various fields owing to their superior property/processibility combinations. This review summarizes the structural designs of PIs with low D<sub>k</sub> and D<sub>f</sub> values, low water-absorbing capacity, and high optical transparency intended for communication applications. Furthermore, we characterize structure-property relationships for various PI types and finally propose structural modifications required to obtain useful values of the abovementioned parameters. Dielectric ; Materials science ; Broadband ; Polyimide ; Absorption (acoustics) ; Molar absorptivity ; Optoelectronics ; Optics ; Computer science ; Nanotechnology ; Telecommunications ; Composite material ; Layer (electronics) ; Physics Synthesis and properties of polymers ; Dielectric materials and actuators ; Silicone and Siloxane Chemistry
Authors: Jong Hyun Jung | Prashanth Srinivasan | Axel Forslund | Blazej Grabowski
Source: npj Computational Materials (article, 2023-01-10)
Field: Materials Science — Materials Chemistry

High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials Abstract Accurate prediction of thermodynamic properties requires an extremely accurate representation of the free-energy surface. Requirements are twofold—first, the inclusion of the relevant finite-temperature mechanisms, and second, a dense volume–temperature grid on which the calculations are performed. A systematic workflow for such calculations requires computational efficiency and reliability, and has not been available within an ab initio framework so far. Here, we elucidate such a framework involving direct upsampling , thermodynamic integration and machine-learning potentials, allowing us to incorporate, in particular, the full effect of anharmonic vibrations. The improved methodology has a five-times speed-up compared to state-of-the-art methods. We calculate equilibrium thermodynamic properties up to the melting point for bcc Nb, magnetic fcc Ni, fcc Al, and hcp Mg, and find remarkable agreement with experimental data. A strong impact of anharmonicity is observed specifically for Nb. The introduced procedure paves the way for the development of ab initio thermodynamic databases. Thermodynamics ; Ab initio ; Melting point ; Point (geometry) ; Statistical physics ; Materials science ; Physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Carlos Romero-Mu ntilde iz | Jia Yan Law | Jorge Revuelta-Losada | Luis M. Moreno-Ram iacute rez | V. Franco
Source: The Innovation Materials (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetocaloric materials for hydrogen liquefaction &lt;p&gt;The expected energy transition to hydrogen gas as a greener energy vector has revived the interest in magnetic refrigeration at the cryogenic range, specifically between 20 and 80 K, with the vision to develop a new generation of hydrogen gas liquefiers. From the materials science point of view, the search for magnetocaloric materials containing mainly non-critical elements with a significant response in that temperature range, together with good cyclability and stability, is a challenging task. Given the increasing interest of the research community on this topic, we aim to establish a comprehensive catalog of the magnetocaloric compounds characterized so far, to be used as a starting point for further research. For this purpose, a systematic outlook of the state of the art is presented here, with the analysis and classification of more than 400 cryogenic magnetocaloric materials, divided into five large families according to their physicochemical properties. Moreover, we provide detailed information about their magnetocaloric properties, magnetic behavior, and transition characteristics together with criticality, which will facilitate the future search for optimal compounds.&lt;/p&gt; Magnetic refrigeration ; Liquefaction ; Materials science ; Hydrogen ; Thermodynamics ; Range (aeronautics) ; Refrigeration ; Computer science ; Chemistry ; Physics ; Magnetic field ; Organic chemistry ; Magnetization Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Multiferroics and related materials
Authors: Yazad Jamshed Davur | Wiebke Kämper | Kourosh Khoshelham | Stephen J. Trueman | Shahla Hosseini Bai
Source: Horticulturae (article, 2023-05-19)
Field: Chemistry — Analytical Chemistry

Estimating the Ripeness of Hass Avocado Fruit Using Deep Learning with Hyperspectral Imaging Rapid ripeness assessment of fruit after harvest is important to reduce post-harvest losses by sorting fruit according to the duration until they become ready to eat. However, there has been little research on non-destructive estimation of the ripeness and ripening speed of avocado fruit. Unlike previous methods, which classify the ripeness of fruit into a few categories (e.g., unripe and ripe) or indirectly estimate ripeness from its firmness, we developed a method using hyperspectral imaging coupled with deep learning regression to directly estimate the duration until ripeness of Hass avocado fruit. A set of 44,096 sub-images of 551 Hass avocado fruit images was used to train, validate, and test a convolutional neural network (CNN) to predict the number of days until ripeness. Training, validation, and test samples were generated as sub-images of Hass fruit images and were used to train a spectral–spatial residual network to estimate the duration to ripeness. We achieved predictions of duration to ripeness with an average error of 1.17 days per fruit on the test set. A series of experiments demonstrated that our deep learning regression approach outperformed classification approaches that rely on dimensionality reduction techniques such as principal component analysis. Our results show the potential for combining hyperspectral imaging with deep learning to estimate the ripeness stage of fruit, which could help to fine-tune avocado fruit sorting and processing. Ripeness ; Ripening ; Hyperspectral imaging ; Artificial intelligence ; Mathematics ; Horticulture ; Convolutional neural network ; Pattern recognition (psychology) ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Yuhao Liu | Xiaoxiao Huang | Xu Yan | Long Xia | Tao Zhang | Jiahao Sun | Yanan Liu | Yu Zhou
Source: Journal of Advanced Ceramics (article, 2023-01-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Pushing the limits of microwave absorption capability of carbon fiber in fabric design based on genetic algorithm The field of electromagnetic wave absorption (EWA) requires the adaptability, tenability, and multifunction of high-performance materials in the future. The design and preparation of EWA materials aiming at performance requirements is the latest research hotspot. Here, a performance-driven strategy for simultaneously coordinating different target performances was proposed to optimize the structure of the periodical long continuous carbon/glass fiber fabric (PCGF) materials through algorithm and simulation. The optimized structure of the PCGF not only improves the impedance matching, but also introduces the induced orientation effect for a high cooperative loss of conductivity, resonance, and periodic structure. The flexible PCGF shows a broad effective absorption bandwidth (EAB) of 32.7 GHz covering a part of the C-band and the whole X-, Ku-, K-, and Ka-bands with a thickness (<i>d</i>) of only 0.92 mm and a density of 5.6×10<sup>−4</sup> kg·cm<sup>−3</sup>. This highly designable fabric is promising for the EWA practical application owing to integrating the characteristics of good flexibility, acid and alkali resistance, bending resistance, excellent mechanical properties, and easy large-scale preparation. Materials science ; Microwave ; Bandwidth (computing) ; Absorption (acoustics) ; Composite material ; Optoelectronics ; Nanotechnology ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Flame retardant materials and properties
Authors: Misaki Ozawa | Giulio Biroli
Source: Physical Review Letters (article, 2023-03-27)
Field: Materials Science — Materials Chemistry

Elasticity, Facilitation, and Dynamic Heterogeneity in Glass-Forming Liquids We study the role of elasticity-induced facilitation on the dynamics of glass-forming liquids by a coarse-grained two-dimensional model in which local relaxation events, taking place by thermal activation, can trigger new relaxations by long-range elastically mediated interactions. By simulations and an analytical theory, we show that the model reproduces the main salient facts associated with dynamic heterogeneity and offers a mechanism to explain the emergence of dynamical correlations at the glass transition. We also discuss how it can be generalized and combined with current theories. Salient ; Elasticity (physics) ; Glass transition ; Dynamical heterogeneity ; Statistical physics ; Facilitation ; Relaxation (psychology) ; Thermal ; Thermal fluctuations ; Materials science ; Mechanics ; Physics ; Classical mechanics ; Chemical physics ; Thermodynamics ; Computer science ; Polymer ; Economics ; Composite material ; Psychology Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Theoretical and Computational Physics
Authors: Franziska Kistner | Justus L. Großmann | Ludwig Sinn | Vadim Demichev
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-06-24)
Field: Chemistry — Spectroscopy

QuantUMS: uncertainty minimisation enables confident quantification in proteomics Abstract Mass spectrometry-based proteomics has been rapidly gaining traction as a powerful analytical method both in basic research and translation. While the problem of error control in peptide and protein identification has been addressed extensively, the quality of the resulting quantities remains challenging to evaluate. Here we introduce QuantUMS ( Quant ification using an U ncertainty M inimising S olution), a machine learning-based method which minimises errors and eliminates bias in peptide and protein quantification by integrating multiple sources of quantitative information. In combination with data-independent acquisition proteomics, QuantUMS boosts accuracy and precision of quantities, as well as reports an uncertainty metric, enabling effective filtering of data for downstream analysis. The algorithm has linear complexity with respect to the number of mass spectrometry acquisitions in the experiment and is thus scalable to infinitely large proteomic experiments. For an easy implementation in a proteomics laboratory, we integrate QuantUMS in our automated DIA-NN software suite. Computer science ; Proteomics ; Suite ; Data mining ; Scalability ; Chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: M. Girolami | Matteo Bosi | Valerio Serpente | Matteo Mastellone | L. Seravalli | Sara Pettinato | S. Salvatori | D.M. Trucchi | R. Fornari
Source: Journal of Materials Chemistry C (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Orthorhombic undoped κ-Ga<sub>2</sub>O<sub>3</sub> epitaxial thin films for sensitive, fast, and stable direct X-ray detectors X-ray photoresponse of κ-Ga 2 O 3 is investigated for the first time. Results show that κ-Ga 2 O 3 is a versatile material for the development of high-performance direct X-ray detectors. Materials science ; Orthorhombic crystal system ; X-ray detector ; Epitaxy ; X-ray ; Detector ; Thin film ; Crystallography ; Optoelectronics ; Optics ; Nanotechnology ; Crystal structure ; Physics ; Chemistry Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; ZnO doping and properties
Authors: Shuang Sun | Qiangliang Yu | Bo Yu | Feng Zhou
Source: Coatings (article, 2023-09-22)
Field: Materials Science — Polymers and Plastics

New Progress in the Application of Flame-Retardant Modified Epoxy Resins and Fire-Retardant Coatings Due to the enhancement of people’s environmental awareness, flame-retardant epoxy resin (EP) tends to be non-toxic, efficient, and multi-functional, and its development is systematic. At present, many new flame retardants or intrinsic modification methods reported in studies can effectively improve the flame retardability and thermal stability of EP. However, many aspects still need to be further improved. In this review, the flame-retardant mechanism and method of flame-retardant epoxy resins are briefly analyzed. The research progress of the flame-retardant modification of epoxy resin by physical addition and chemical reaction is summarized and discussed. Furthermore, the research trend of flame-retardant epoxy resin in the field of fire-protective coatings is discussed, and future problems in this field are put forward. This work may provide some new insights for the design of multi-functional integrated epoxy resin fireproof coatings. Fire retardant ; Epoxy ; Materials science ; Thermal stability ; Composite material ; Chemistry ; Organic chemistry Flame retardant materials and properties ; Polymer composites and self-healing ; Fire dynamics and safety research