jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Brandon T. Swartz | Hanyu Zheng | Gregory T. Forcherio | Jason Valentine
Source: Science Advances (article, 2024-02-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Broadband and large-aperture metasurface edge encoders for incoherent infrared radiation The prevalence of computer vision systems necessitates hardware-based approaches to relieve the high computational demand of deep neural networks in resource-limited applications. One solution would be to off-load low-level image feature extraction, such as edge detection, from the digital network to the analog imaging system. To that end, this work demonstrates incoherent, broadband, low-noise optical edge detection of real-world scenes by combining the wavefront shaping of a 24-mm aperture metasurface with a refractive lens. An inverse design approach is used to optimize the metasurface for Laplacian-based edge detection across the 7.5- to 13.5-μm LWIR imaging band, allowing for facile integration with uncooled microbolometer-based LWIR imagers to encode edge information. A polarization multiplexed approach leveraging a birefringent metasurface is also demonstrated as a single-aperture implementation. This work could be applied to improve computer vision capabilities of resource-constrained systems by leveraging optical preprocessing to alleviate the computational requirements for high-accuracy image segmentation and classification. Computer science ; Encoder ; Optics ; Broadband ; Artificial intelligence ; Aperture (computer memory) ; Edge detection ; Computer vision ; Image processing ; Telecommunications ; Physics Metamaterials and Metasurfaces Applications ; Random lasers and scattering media ; Orbital Angular Momentum in Optics
Authors: Yixiang Cao | Ty Balduf | Michael D. Beachy | M. Chandler Bennett | Arteum D. Bochevarov | Alan D. Chien | Pavel A. Dub | Kenneth G. Dyall | James W. Furness | Mathew D. Halls | Thomas F. Hughes | Leif D. Jacobson | H. Shaun Kwak | Daniel S. Levine | Daniel T. Mainz | Kevin B. Moore | Mats Svensson | Pablo E. Videla | Mark A. Watson | Richard A. Friesner
Source: The Journal of Chemical Physics (article, 2024-08-02)
Field: Materials Science — Materials Chemistry

Quantum chemical package Jaguar: A survey of recent developments and unique features This paper is dedicated to the quantum chemical package Jaguar, which is commercial software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific features that are relevant to chemical research as well as describe those aspects of the program that are pertinent to the user interface, the organization of the computer code, and its maintenance and testing. Among the scientific topics that feature prominently in this paper are the quantum chemical methods grounded in the pseudospectral approach. A number of multistep workflows dependent on Jaguar are covered: prediction of protonation equilibria in aqueous solutions (particularly calculations of tautomeric stability and pKa), reactivity predictions based on automated transition state search, assembly of Boltzmann-averaged spectra such as vibrational and electronic circular dichroism, as well as nuclear magnetic resonance. Discussed also are quantum chemical calculations that are oriented toward materials science applications, in particular, prediction of properties of optoelectronic materials and organic semiconductors, and molecular catalyst design. The topic of treatment of conformations inevitably comes up in real world research projects and is considered as part of all the workflows mentioned above. In addition, we examine the role of machine learning methods in quantum chemical calculations performed by Jaguar, from auxiliary functions that return the approximate calculation runtime in a user interface, to prediction of actual molecular properties. The current work is second in a series of reviews of Jaguar, the first having been published more than ten years ago. Thus, this paper serves as a rare milestone on the path that is being traversed by Jaguar's development in more than thirty years of its existence. Workflow ; Jaguar ; Computer science ; Interface (matter) ; Software ; Computational science ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Database ; Molecule ; Programming language Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Xin Li | Zhen Xu | Donglei Bu | Jinming Cai | Huamei Chen | Qi Chen | Ting Chen | Fang Cheng | Lifeng Chi | Wenjie Dong | Zhen‐Chao Dong | Shixuan Du | Qitang Fan | Xing Fan | Qiang Fu | Song Gao | Jing Guo | Weijun Guo | He Yang | Shimin Hou | Ying Jiang | Huihui Kong | Baojun Li | Deng‐Yuan Li | Jie Li | Qing Li | Ruoning Li | Shuying Li | Yuxuan Lin | Mengxi Liu | Pei Nian Liu | Yanyan Liu | Jing‐Tao Lü | Chuanxu Ma | Haoyang Pan | JinLiang Pan | Minghu Pan | Xiaohui Qiu | Ziyong Shen | Qiang Sun | Shijing Tan | Bing Wang | Dong Wang | Li Wang | Lili Wang | Tao Wang | Xiang Wang | Xingyue Wang | Xueyan Wang | Yansong Wang | Yu Wang | Kai Wu | Wei Xu | Na Xue | Linghao Yan | Fan Yang | Zhiyong Yang | Chi Zhang | Xue Zhang | Yang Zhang | Yao Zhang | Xiong Zhou | Junfa Zhu | Yajie Zhang | Feixue Gao | Yongfeng Wang
Source: Chinese Chemical Letters (article, 2024-06-08)
Field: Materials Science — Materials Chemistry

Recent progress on surface chemistry II: Property and characterization Characterization (materials science) ; Property (philosophy) ; Surface (topology) ; Nanotechnology ; Chemistry ; Materials science ; Polymer science ; Mathematics ; Philosophy ; Epistemology ; Geometry Graphene research and applications ; Surface and Thin Film Phenomena ; Surface Chemistry and Catalysis
Authors: Huanhuan Li | Sheng Wei | Selorm Yao‐Say Solomon Adade | Xorlali Nunekpeku | Quansheng Chen
Source: Food Chemistry (article, 2024-08-08)
Field: Chemistry — Analytical Chemistry

Investigation of heat-induced pork batter quality detection and change mechanisms using Raman spectroscopy coupled with deep learning algorithms Raman spectroscopy ; Algorithm ; Quality (philosophy) ; Artificial intelligence ; Spectroscopy ; Computer science ; Chemistry ; Machine learning ; Materials science ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Yaohui Xu | Yang Zhou | Chaoqun Li | Shuai Dong | Hao Liu | Weijie Yang | Yuting Li | Han Jiang | Zhao Ding | Hao Li | Leon L. Shaw
Source: Fuel (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Unraveling the Potential of Solid-State Hydrogen Storage Materials: Insights from First Principle Calculations Hydrogen storage ; Solid-state ; Hydrogen ; Materials science ; State (computer science) ; Chemical physics ; Nanotechnology ; Chemistry ; Computer science ; Physical chemistry ; Algorithm Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Nuclear Materials and Properties
Authors: Lei Jin | Gurpreet Singh Selopal | Xin Tong | Dmitrii F. Perepichka | Zhiming M. Wang | Federico Rosei
Source: Advanced Materials (review, 2024-06-23)
Field: Materials Science — Materials Chemistry

Heavy‐Metal‐Free Colloidal Quantum Dots: Progress and Opportunities in Solar Technologies Colloidal quantum dots (QDs) hold great promise as building blocks in solar technologies owing to their remarkable photostability and adjustable properties through the rationale involving size, atomic composition of core and shell, shapes, and surface states. However, most high-performing QDs in solar conversion contain hazardous metal elements, including Cd and Pb, posing significant environmental risks. Here, a comprehensive review of heavy-metal-free colloidal QDs for solar technologies, including photovoltaic (PV) devices, solar-to-chemical fuel conversion, and luminescent solar concentrators (LSCs), is presented. Emerging synthetic strategies to optimize the optical properties by tuning the energy band structure and manipulating charge dynamics within the QDs and at the QDs/charge acceptors interfaces, are analyzed. A comparative analysis of different synthetic methods is provided, structure-property relationships in these materials are discussed, and they are correlated with the performance of solar devices. This work is concluded with an outlook on challenges and opportunities for future work, including machine learning-based design, sustainable synthesis, and new surface/interface engineering. Quantum dot ; Materials science ; Photovoltaic system ; Nanotechnology ; Solar energy ; Solar cell ; Engineering physics ; Optoelectronics Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Advanced Photocatalysis Techniques
Authors: Somboon Sukpancharoen | Thossaporn Wijakmatee | Tossapon Katongtung | Kowit Ponhan | Nopporn Rattanachoung | Sujira Khojitmate
Source: Results in Engineering (article, 2024-09-03)
Field: Materials Science — Biomaterials

Data-driven prediction of electrospun nanofiber diameter using machine learning: A comprehensive study and web-based tool development Electrospinning has emerged as a versatile technique for fabricating polymer nanofibers with diameters ranging from tens to hundreds of nanometers. Precisely controlling and predicting the nanofiber diameter is crucial for various applications. The complex interplay of multiple electrospinning parameters presents a significant challenge in this endeavor. This study introduces a novel data-driven approach using machine learning (ML) to predict and optimize the diameter of electrospun nanofibers. A comprehensive dataset of approximately 430 data points was compiled from literature sources, including polymer properties and electrospinning process parameters. Six ML algorithms were evaluated: Decision Tree (DT), Extra Trees Regression (ETR), Kernel Ridge Regression (KRR), Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGB). A 75/25 train-test split was selected for optimal model evaluation, with 10-fold cross-validation employed for hyperparameter tuning and estimating the variability of performance metrics. RF and ETR demonstrated the best predictive accuracy, with R 2 values of 0.9468 and 0.9421, and RMSE values of 92.3 nm and 96.1 nm, respectively, on the test set. Feature importance analysis and SHapley Additive exPlanations (SHAP) values revealed that polymer concentration, applied voltage, and feed rate significantly influence the nanofiber diameter, which was further elucidated by partial dependence plots. To facilitate accessibility and collaboration, an interactive web server called ENDP was developed, allowing users to input polymer properties and process parameters to predict the nanofiber diameter. This study showcases the potential of ML in guiding the design and optimization of electrospun nanofibers, providing valuable insights for tailoring their properties into specific applications. • ML models predict electrospun nanofiber diameter with high accuracy (R 2 > 0.94). • RF algorithm shows superior performance in diameter prediction. • Polymer concentration, voltage, and feed rate are key influencing factors. • Interactive ENDP web tool aids in precise electrospinning design. Nanofiber ; Electrospinning ; Support vector machine ; Hyperparameter ; Materials science ; Computer science ; Machine learning ; Algorithm ; Artificial intelligence ; Polymer ; Nanotechnology ; Composite material Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Nadia Mouats | Souad Djellali | Hana Ferkous | Amel Sedik | Amel Delimi | Abir Boublia | Khadidja Otmane Rachedi | Malika Berredjem | Alaaddin Çukurovalı | Manawwer Alam | Barbara Ernsti | Yacine Benguerba
Source: ACS Omega (article, 2024-06-15)
Field: Materials Science — Materials Chemistry

Comprehensive Investigation of the Adsorption, Corrosion Inhibitory Properties, and Quantum Calculations for 2-(2,4,5-Trimethoxybenzylidene) Hydrazine Carbothioamide in Mitigating Corrosion of XC38 Carbon Steel under HCl Environment This study investigates the inhibitory effects of 2-(2,4,5-trimethoxy benzylidene) hydrazine carbothioamide (TMBHCA) on the corrosion of carbon steel in a 1 M HCl solution across various concentrations. The assessment employs a comprehensive approach, combining gravimetric analysis, potentiodynamic polarization tests, and electrochemical impedance spectroscopy (EIS). Additionally, scanning electron microscopy (SEM) and quantum chemical calculations are employed to provide a thorough understanding of the corrosion inhibition mechanism. The influence of exposure time on mild steel corrosion is systematically examined. Results reveal a remarkable reduction in the corrosion rate of steel, with TMBHCA demonstrating its highest inhibition efficiency of 97.8% at 200 ppm. Potentiodynamic polarization studies characterize TMBHCA as a mixed-type inhibitor, while Nyquist plots illustrate increased charge transfer resistance and decreased double-layer capacitance with escalating TMBHCA concentrations. Consistency between weight loss measurements and electrochemical findings further validates the efficacy of TMBHCA as a corrosion inhibitor. SEM images substantiate and visually support the obtained results. An immersion test conducted at 25 °C over 28 days showcases a notable enhancement in TMBHCA efficiency (IE%) from 45.16% to 92.43% at 200 ppm as the immersion period progresses from 1 day to 28 days. This improvement is attributed to the augmented adsorption of inhibitor molecules on the steel surface over time. These comprehensive findings significantly contribute to our understanding of TMBHCA's corrosion inhibition behavior, emphasizing its potential as a highly efficient corrosion inhibitor for diverse industrial applications. Corrosion ; Dielectric spectroscopy ; Gravimetric analysis ; Carbon steel ; Adsorption ; Double-layer capacitance ; Corrosion inhibitor ; Nyquist plot ; Polarization (electrochemistry) ; Electrochemistry ; Materials science ; Scanning electron microscope ; Chemistry ; Nuclear chemistry ; Metallurgy ; Chemical engineering ; Composite material ; Organic chemistry ; Electrode ; Physical chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: M. Manikandan | E. Manikandan | V Swetha | S. Kurpaa | S. N. S. Maruthy Vijay | V. Kiruthika
Source: Scientific Reports (article, 2024-05-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nickel-copper-cobalt mixed oxide electrode material for high performance asymmetric supercapacitor Nickel copper cobalt oxide (NiCuCoO) ternary metal oxide nanoparticles were synthesized by employing the hydrothermal method. NiCuCoO electrode demonstrates a specified capacity of 596 C g<sup>-1</sup> at 1 A g<sup>-1</sup>, high capacitance retaining of 99% even if 1000 sequences at the density of current 10 A g<sup>-1</sup>, and significant extended cyclic strength over 1000 sequences. The gathered asymmetric supercapacitor (ASC) tool via NiCuCoO as the cathode and activated carbon as anode materials achieve a specified capacity of 168 C g<sup>-1</sup> at a current density of 1 Ag<sup>-1</sup>, an excellent capacity retaining of 95% even later than 5000 sequences at a density of current 10 A g<sup>-1</sup>. The fabricated device exhibits a high density of energy and power is 96 Wh kg<sup>-1</sup> and 841 W kg<sup>-1</sup>. The prepared material confirms an excellent capacitance routine, so this work represents for a next-generation energy storage device. Supercapacitor ; Capacitance ; Anode ; Materials science ; Current density ; Nickel ; Cobalt oxide ; Electrode ; Ternary operation ; Cathode ; Oxide ; Cobalt ; Power density ; Chemical engineering ; Copper ; Energy storage ; Copper oxide ; Metallurgy ; Chemistry ; Power (physics) ; Computer science Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Advancements in Battery Materials
Authors: Shimelis Tamene Gobena | Abraham Debebe Woldeyonnes
Source: Discover Materials (review, 2024-10-03)
Field: Materials Science — Polymers and Plastics

A review of synthesis methods, and characterization techniques of polymer nanocomposites for diverse applications Polymer nanocomposites have become a quickly growing area of study, fueling the advancement of sophisticated materials in recent years. Adding nanoparticles to a polymer matrix greatly improves the characteristics of the resulting nanocomposites. These improvements play a crucial role in a wide range of applications, including dental restorations, biosensors, food packaging, electronic devices, and various biomedical uses. This review offers a thorough examination of various techniques for synthesizing polymer-based nanocomposites, such as melt intercalation, sol–gel processes, in situ polymerization, and emulsion techniques. Moreover, it is essential to characterize these nanocomposites to comprehend their mechanical properties, structure, and morphology. Methods like atomic force microscopy (AFM), X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) are widely utilized for the examination of these attributes. The thermal properties of polymer nanocomposites are highly interesting, along with their mechanical properties. Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TGA), and Fourier Transform Infrared Spectroscopy (FTIR) are used to assess these thermal and chemical characteristics. In general, the review seeks to summarize the methods of combining and analyzing polymer nanocomposites, giving a thorough insight into how these processes enhance the improvement and refinement of materials for various industrial and medical uses. Characterization (materials science) ; Nanocomposite ; Materials science ; Polymer ; Polymer nanocomposite ; Polymer science ; Nanotechnology ; Composite material Polymer Nanocomposite Synthesis and Irradiation ; Nanotechnology research and applications ; Polymer Nanocomposites and Properties
Authors: Wei Li | Zhonghui Shen | Run‐Lin Liu | Xiaoxiao Chen | Mengfan Guo | Jin-Ming Guo | Hua Hao | Yang Shen | Hanxing Liu | Long‐Qing Chen | Ce‐Wen Nan
Source: Nature Communications (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

Generative learning facilitated discovery of high-entropy ceramic dielectrics for capacitive energy storage Dielectric capacitors offer great potential for advanced electronics due to their high power densities, but their energy density still needs to be further improved. High-entropy strategy has emerged as an effective method for improving energy storage performance, however, discovering new high-entropy systems within a high-dimensional composition space is a daunting challenge for traditional trial-and-error experiments. Here, based on phase-field simulations and limited experimental data, we propose a generative learning approach to accelerate the discovery of high-entropy dielectrics in a practically infinite exploration space of over 10<sup>11</sup> combinations. By encoding-decoding latent space regularities to facilitate data sampling and forward inference, we employ inverse design to screen out the most promising combinations via a ranking strategy. Through only 5 sets of targeted experiments, we successfully obtain a Bi(Mg<sub>0.5</sub>Ti<sub>0.5</sub>)O<sub>3</sub>-based high-entropy dielectric film with a significantly improved energy density of 156 J cm<sup>-3</sup> at an electric field of 5104 kV cm<sup>-1</sup>, surpassing the pristine film by more than eight-fold. This work introduces an effective and innovative avenue for designing high-entropy dielectrics with drastically reduced experimental cycles, which could be also extended to expedite the design of other multicomponent material systems with desired properties. Generative grammar ; Dielectric ; Energy storage ; Capacitive sensing ; Generative model ; Ceramic ; Computer science ; Materials science ; Nanotechnology ; Artificial intelligence ; Optoelectronics ; Physics ; Composite material Ferroelectric and Piezoelectric Materials ; Dielectric properties of ceramics ; Dielectric materials and actuators
Authors: Jin Yang | Michelle C. Neary | Tianning Diao
Source: Journal of the American Chemical Society (article, 2024-02-23)
Field: Chemistry — Organic Chemistry

ProPhos: A Ligand for Promoting Nickel-Catalyzed Suzuki-Miyaura Coupling Inspired by Mechanistic Insights into Transmetalation Nickel-catalyzed Suzuki-Miyaura coupling (Ni-SMC) offers the potential to reduce the cost of pharmaceutical process synthesis. However, its application has been restricted by challenges such as slow reaction rates, high catalyst loading, and a limited scope of heterocycles. Despite recent investigations, the mechanism of transmetalation in Ni-SMC, often viewed as the turnover-limiting step, remains insufficiently understood. We elucidate the "Ni-oxo" transmetalation pathway, applying PPh<sub>2</sub>Me as the ligand, and identify the formation of a nickel-oxo intermediate as the turnover-limiting step. Building on this insight, we develop a scaffolding ligand, ProPhos, featuring a pendant hydroxyl group connected to the phosphine via a linker. The design preorganizes both the nucleophile and the nickel catalyst, thereby facilitating transmetalation. This catalyst exhibits fast kinetics and robust activity across a wide range of heteroarenes, with a catalyst loading of 0.5-3 mol %. For arene substrates, the catalyst loading can be further reduced to 0.1 mol %. Transmetalation ; Chemistry ; Nickel ; Ligand (biochemistry) ; Catalysis ; Coupling (piping) ; Combinatorial chemistry ; Organic chemistry ; Biochemistry ; Metallurgy ; Receptor Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Organoboron and organosilicon chemistry