jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuhao Wan | Zeyuan Li | Wei Yu | Anyang Wang | Ke Xue | Hailing Guo | Jinhao Su | Li Li | Qingzhong Gui | Songpeng Zhao | John Robertson | Zhaofu Zhang | Yuzheng Guo
Source: Advanced Materials (review, 2023-09-09)
Field: Materials Science — Materials Chemistry

Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic-level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks, can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross-validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of Artificial-intelligence-assisted material exploration. Materials science ; Nanotechnology ; Engineering physics ; Entropy (arrow of time) ; Systems engineering ; Engineering ; Thermodynamics ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Inorganic Chemistry and Materials
Authors: Benedikt Winter | Clemens Winter | Timm Esper | Johannes Schilling | André Bardow
Source: Fluid Phase Equilibria (article, 2023-01-20)
Field: Materials Science — Materials Chemistry

SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients The availability of property data is one of the major bottlenecks in the development of chemical processes and products, often requiring time-consuming and expensive experiments or limiting the chemical space to a small number of known molecules. This bottleneck has been the motivation behind the continuing development of predictive property models. For the property prediction of novel molecules, group contribution methods have been groundbreaking. In recent times, machine learning has joined the more established property prediction models. However, even with recent successes, the integration of physical constraints into machine learning models remains challenging. Physical constraints are vital to many thermodynamic properties, such as the Gibbs-Dunham relation, introducing an additional layer of complexity into the prediction. Here, we introduce SPT-NRTL, a natural language processing model to predict thermodynamically consistent activity coefficients and provide NRTL parameters for easy use in process simulations.SPT-NRTL can be classed as an advanced group contribution model that uses characters of the SMILES code as atom groups and then dynamically constructs higher-order groups. The results show that SPT-NRTL achieves higher accuracy than UNIFAC in the prediction of activity coefficients across all functional groups and is able to predict many vapor–liquid-equilibria with near experimental accuracy, as illustrated for multiple exemplary mixtures. To ease the application of SPT-NRTL, NRTL-parameters of 100 000 000 mixtures are calculated with SPT-NRTL and provided online. Non-random two-liquid model ; UNIFAC ; Chemistry ; Activity coefficient ; Physical property ; Property (philosophy) ; Thermodynamics ; Artificial intelligence ; Machine learning ; Statistical physics ; Computer science ; Physical chemistry ; Physics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Yu Xie | Jonathan Vandermause | Senja Ramakers | Nakib H. Protik | Anders Johansson | Boris Kozinsky
Source: npj Computational Materials (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC Abstract Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present a general Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide (SiC) polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured. The resulting model exhibits close agreement with both ab initio calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding. Gaussian process ; Computer science ; Force field (fiction) ; Kriging ; Machine learning ; Workflow ; Bayesian optimization ; Uncertainty quantification ; Active learning (machine learning) ; Molecular dynamics ; Artificial intelligence ; Bayesian probability ; Speedup ; Field (mathematics) ; Gaussian ; Statistical physics ; Physics ; Chemistry ; Computational chemistry ; Mathematics ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yihui Wang | Wei Sha | Mi Xiao | Cheng‐Wei Qiu | Liang Gao
Source: Advanced Materials (article, 2023-07-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep‐Learning‐Enabled Intelligent Design of Thermal Metamaterials Thermal metamaterials are mixture-based materials that are engineered to manipulate, control, and process the flow of heat, enabling numerous advanced thermal metadevices. Conventional thermal metamaterials are predominantly designed with tractable regular geometries owing to the delicate analytical solution and easy-to-implement effective structures. Nevertheless, it is challenging to achieve the design of thermal metamaterials with arbitrary geometry, letting alone intelligent (automatic, real-time, and customizable) design of thermal metamaterials. Here, an intelligent design framework of thermal metamaterials is presented via a pre-trained deep learning model, which gracefully achieves the desired functional structures of thermal metamaterials with exceptional speed and efficiency, regardless of arbitrary geometry. It possesses incomparable versatility and is of great flexibility to achieve the corresponding design of thermal metamaterials with different background materials, anisotropic geometries, and thermal functionalities. The transformation thermotics-induced, freeform, background-independent, and omnidirectional thermal cloaks, whose structural configurations are automatically designed in real-time according to shape and background, are numerically and experimentally demonstrated. This study sets up a novel paradigm for an automatic and real-time design of thermal metamaterials in a new design scenario. More generally, it may open a door to the realization of an intelligent design of metamaterials in also other physical domains. Metamaterial ; Thermal ; Flexibility (engineering) ; Realization (probability) ; Transformation optics ; Computer science ; Materials science ; Mechanical engineering ; Topology (electrical circuits) ; Physics ; Optoelectronics ; Engineering ; Electrical engineering ; Mathematics Metamaterials and Metasurfaces Applications ; Cellular and Composite Structures ; Advanced Materials and Mechanics
Authors: Yao Yang | Julian Feijóo | Valentín Briega‐Martos | Qihao Li | Mihail R. Krumov | Stefan Merkens | Giuseppe De Salvo | Andrey Chuvilin | Jianbo Jin | Haowei Huang | Christopher J. Pollock | Miquel Salmerón | Cheng Wang | David A. Muller | Héctor D. Abruña | Peidong Yang
Source: Current Opinion in Electrochemistry (article, 2023-09-28)
Field: Chemistry — Electrochemistry

Operando methods: A new era of electrochemistry Nanotechnology ; Electrochemistry ; Synchrotron ; Reaction dynamics ; Electrochemical energy conversion ; Materials science ; Computer science ; Biochemical engineering ; Chemistry ; Physics ; Electrode ; Organic chemistry ; Optics ; Engineering ; Physical chemistry Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Advanced Electron Microscopy Techniques and Applications
Authors: Cheng Chang | Saeed Rad | Lei Gan | Zitao Li | Junfeng Dai | Asfandyar Shahab
Source: Nanotechnology Reviews (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Review of the sol–gel method in preparing nano TiO <sub>2</sub> for advanced oxidation process Abstract Application of nano titanium dioxide (TiO 2 ) in various fields such as advanced oxidation process (AOP) has led to the development of its preparation technologies. The sol–gel process is a widely used chemical wet method for preparing nanoscale TiO 2 gels. This technique offers numerous advantages, such as the potential to produce large quantities of homogeneous materials with high purity, surface area, porosity, and reactivity, as well as being cost-effective, simple to implement, and capable of controlling the size and shape of the resulting particles. This review provides a comprehensive overview of the chemicals, reaction conditions, and procedures required for preparing nano TiO 2 using the sol–gel method. It covers the selection of necessary compounds, such as TiO 2 precursors, solvents, hydrolysis agents, and additives, along with their composition and sequences of adding, reaction order, and impact on the final product. Additionally, it provides detailed information on the routes of gel formation and ambient conditions, including temperature, humidity, stirring speed, injection rates of compounds, aging process, and storage conditions. This information serves as a basic reference for understanding the sol–gel process and the relative contribution rates of the influencing factors, which is essential for controlling the size, morphology, crystallinity, and other physicochemical properties of the resulting TiO 2 gel/powder for targeted applications. Sol-gel ; Materials science ; Titanium dioxide ; Crystallinity ; Porosity ; Nanotechnology ; Chemical engineering ; Process (computing) ; Nano- ; Specific surface area ; Process engineering ; Catalysis ; Computer science ; Organic chemistry ; Chemistry ; Composite material Catalytic Processes in Materials Science ; TiO2 Photocatalysis and Solar Cells ; Advanced Photocatalysis Techniques
Authors: Abeer S. Elsherbiny | Ahmed Rady | Reda M. Abdelhameed | Ali H. Gemeay
Source: Environmental Science and Pollution Research (article, 2023-02-27)
Field: Chemistry — Inorganic Chemistry

Efficiency and selectivity of cost-effective Zn-MOF for dye removal, kinetic and thermodynamic approach Green synthesis of metal-organic frameworks (MOFs) has attracted a lot of attention as a crucial step for practical industrial applications. In this work, green synthesis of zinc(II) metal-organic framework (Zn-MOF) has been carried out at room temperature. The Zn metal (node) was extracted from spent domestic batteries, and the linker was benzene di-carboxylic acid (BDC). The characterization of the as-prepared Zn-MOF was accomplished by PXRD, FT-IR spectroscopy, SEM, TEM, TGA, and nitrogen adsorption at 77 K. All the characterization techniques strongly supported that as-synthesized Zn-MOF using metallic solid waste Zn is similar to that was reported in the literature. The as-prepared Zn-MOF was stable in water for 24 h without any changes in its functional groups and framework. The prepared Zn-MOF was tested for the adsorption of three dyes, two anionic dyes, aniline blue (AB), and orange II (O(II)) as well as methylene blue (MB), an example of cationic dye from aqueous solution. AB has the highest equilibrium adsorbed amount, q<sub>e</sub>, of value 55.34 mg g<sup>-1</sup> at pH = 7 and 25 °C within 40 min. Investigation of the adsorption kinetics indicated that these adsorption processes could be described as a pseudo-second-order kinetic model. Furthermore, the adsorption process of the three dyes was described well by the Freundlich isotherm model. According to the thermodynamic parameters, the adsorption of AB on the prepared Zn-MOF was an endothermic and spontaneous process. In contrast, it was non-spontaneous and exothermic for the uptake of O(II) and MB. This study complements the business case development model of "solid waste to value-added MOFs." Selectivity ; Kinetic energy ; Chemistry ; Ecotoxicology ; Environmental chemistry ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Mercury impact and mitigation studies
Authors: Fangfei Zhang | Weigang Ge | Lingling Huang | Dan Li | Lijuan Liu | Zhen Dong | Luang Xu | Xuan Ding | Cheng Zhang | Yingying Sun | Jun Ai | Jinlong Gao | Tiannan Guo
Source: Molecular & Cellular Proteomics (article, 2023-07-20)
Field: Chemistry — Spectroscopy

A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology. Orbitrap ; Computer science ; Skyline ; Mass spectrometry ; Identification (biology) ; Proteomics ; Information retrieval ; Proteome ; Data mining ; Data science ; Bioinformatics ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Megha Prajapati | Vinamrita Singh | Mohan V. Jacob | Chhaya Ravi Kant
Source: Renewable and Sustainable Energy Reviews (article, 2023-06-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent advancement in metal-organic frameworks and composites for high-performance supercapatteries Supercapacitor ; Materials science ; Nanotechnology ; Energy storage ; Porosity ; Blueprint ; Metal-organic framework ; Electrochemistry ; Electrochemical energy storage ; Power density ; Electrode ; Energy density ; Engineering physics ; Mechanical engineering ; Composite material ; Power (physics) ; Engineering ; Chemistry Supercapacitor Materials and Fabrication ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced battery technologies research
Authors: Kalina Detka | Krzysztof Górecki
Source: Energies (article, 2023-06-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Selected Technologies of Electrochemical Energy Storage—A Review The paper presents modern technologies of electrochemical energy storage. The classification of these technologies and detailed solutions for batteries, fuel cells, and supercapacitors are presented. For each of the considered electrochemical energy storage technologies, the structure and principle of operation are described, and the basic constructions are characterized. Values of the parameters characterizing individual technologies are compared and typical applications of each of them are indicated. Selected characteristics illustrating properties of the presented electrochemical energy storage devices are also shown. The advantages and disadvantages of the considered electrochemical energy storage devices and typical areas of their application are indicated. In addition, new, constantly developing technologies, not yet commercially available, are mentioned. Examples of existing energy storage solutions using the discussed technologies on the example of electric cars or storage systems in the world are given. Energy storage ; Supercapacitor ; Electrochemical energy storage ; Electrochemical energy conversion ; Process engineering ; Computer science ; Computer data storage ; Emerging technologies ; Electrochemistry ; Electrical engineering ; Nanotechnology ; Engineering ; Materials science ; Chemistry ; Power (physics) ; Computer hardware ; Physics Supercapacitor Materials and Fabrication ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: E. Salim | A.E. Tarabiah
Source: Journal of Inorganic and Organometallic Polymers and Materials (article, 2023-04-07)
Field: Materials Science — Polymers and Plastics

The Influence of NiO Nanoparticles on Structural, Optical and Dielectric Properties of CMC/PVA/PEDOT:PSS Nanocomposites Abstract The solution casting process was used to fabricate nanocomposite samples composed of carboxymethylcellulose (CMC), polyvinyl alcohol (PVA), poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) and filled with varied concentrations of nickel oxide nanoparticles (NiO NPs). The effect of NiO nanoparticles on the structural, optical, and electrical properties of the pure CMC/PVA/PEDOT:PSS mixture was studied and discussed. X-ray diffraction (XRD) and transmission electron microscopy (TEM) analysis revealed that the NiO NPs are cubic in phase and range in size from 10 to 55 nm. The XRD analysis of the incorporated films indicated that the NiO NPs crystallinity increased at expense of the CMC/PVA/PEDOT:PSS composites. Fourier Transform Infrared (FTIR) examination revealed the main absorption vibrational peaks of CMC, PVA, PEDOT:PSS, and Ni-O, whose intensities changed randomly after filling, revealing the intermolecular interaction between the nanocomposite components. The UV and visible range absorption spectra showed a sharp peak around 228 for the pure blend, which can be assigned to the π→π* transition. After being filled with NiO NPs, the nanocomposites produced displayed new peaks at 292 and 422 nm that steadily increased with increasing NiO NPs concentration. The optical energy gap (Eg) was computed, and it was discovered that when the NiO NPs content increased, the Eg decreased (from 4.88 to 4.06 eV). At room temperature and over a wide frequency range, between 10 − 1 and 10 7 Hz, the samples’ impedance, AC conductivity, and dielectric qualities were examined. Increased NiO NPs content was seen to gradually enhance dielectric loss (up to 2255), and dielectric constant (up to 311). The AC conductivity of the filled samples is also enhanced and corresponds to Jonscher power law. By analysing impedance components of the Z * , the equivalent electrical circuit for each sample was determined. Because of the considerable improvement in optical and electrical properties, these composite films could compete for usage in optoelectronic applications. Materials science ; Non-blocking I/O ; Nanocomposite ; PEDOT:PSS ; Crystallinity ; Fourier transform infrared spectroscopy ; Dielectric ; Chemical engineering ; Nanoparticle ; Polyvinyl alcohol ; Composite material ; Nanotechnology ; Polymer ; Organic chemistry ; Chemistry Polymer Nanocomposite Synthesis and Irradiation ; Dielectric materials and actuators ; Conducting polymers and applications
Authors: Philipp Ritzinger | Karel Výborný
Source: Royal Society Open Science (review, 2023-10-01)
Field: Materials Science — Materials Chemistry

Anisotropic magnetoresistance: materials, models and applications Resistance of certain (conductive and otherwise isotropic) ferromagnets turns out to exhibit anisotropy with respect to the direction of magnetization: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mo>∥</mml:mo></mml:mrow></mml:msub></mml:math> for magnetization parallel to the electric current direction is different from <i>R</i><sub>⊥</sub> for magnetization perpendicular to the electric current direction. In this review, this century-old phenomenon is reviewed both from the perspective of materials and physical mechanisms involved. More recently, this effect has also been identified and studied in antiferromagnets. To date, sensors based on the anisotropic magnetoresistance (AMR) effect are widely used in different fields, such as the automotive industry, aerospace or in biomedical imaging. Anisotropy ; Magnetoresistance ; Isotropy ; Condensed matter physics ; Ferromagnetism ; Magnetization ; Materials science ; Electrical conductor ; Physics ; Magnetic field ; Optics ; Composite material ; Quantum mechanics Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials ; Multiferroics and related materials