jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yingjie Shi | Weixing Li | Xiaorong Zhang | Jiachao Jin | Jilin Wang | Yu Dong | Jingbo Mu | Guangsuo Wang | Xiaoliang Zhang | Zhixiao Zhang
Source: Journal of Advanced Ceramics (article, 2023-11-07)
Field: Materials Science — Ceramics and Composites

Preparation and toughening mechanism of Al <sub>2</sub>O <sub>3</sub> composite ceramics toughened by B <sub>4</sub>C@TiB <sub>2</sub> core–shell units In this paper, the concept of incorporating core–shell structured units as secondary phases to toughen Al<sub>2</sub>O<sub>3</sub> ceramics is proposed. An Al<sub>2</sub>O<sub>3</sub> composite ceramic toughened by B<sub>4</sub>C@TiB<sub>2</sub> core–shell units is successfully synthesized using a combination of molten salt methodology and spark plasma sintering. The synthesis of B<sub>4</sub>C@TiB<sub>2</sub> core–shell toughening units stems from the prior production of core–shell structural B<sub>4</sub>C@TiB<sub>2</sub> powders, and this core–shell structure is effectively preserved within the Al<sub>2</sub>O<sub>3</sub> matrix after sintering. The B<sub>4</sub>C@TiB<sub>2</sub> core–shell toughening unit consists of a micron-sized B<sub>4</sub>C core enclosed by a shell approximately 500 nm thick, composed of numerous nanosized TiB<sub>2</sub> grains. The regions surrounding these core–shell units exhibit distinct geometric structures and encompass multidimensional variations in phase composition, grain dimensions, and thermal expansion coefficients. Consequently, intricate stress distributions emerge, fostering the propagation of cracks in multiple dimensions. This behavior consumes a considerable amount of crack propagation energy, thereby enhancing the fracture toughness of the Al<sub>2</sub>O<sub>3</sub> matrix. The resulting Al<sub>2</sub>O<sub>3</sub> composite ceramics displays a relative density of 99.7±0.2%, a Vickers hardness of 21.5±0.8 GPa, and a fracture toughness 6.92±0.22 MPa·m<sup>1/2</sup>. Materials science ; Spark plasma sintering ; Ceramic ; Sintering ; Composite material ; Fracture toughness ; Shell (structure) ; Composite number ; Crystallography Advanced ceramic materials synthesis ; Advanced materials and composites ; MXene and MAX Phase Materials
Authors: Roger Monreal‐Corona | Anna Pla‐Quintana | Albert Poater
Source: Trends in Chemistry (article, 2023-11-18)
Field: Materials Science — Materials Chemistry

Predictive catalysis: a valuable step towards machine learning Biochemical engineering ; Catalysis ; Computer science ; Field (mathematics) ; Selectivity ; Chemistry ; Machine learning ; Artificial intelligence ; Organic chemistry ; Mathematics ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Andy S. Anker | Keith T. Butler | Raghavendra Selvan | Kirsten M. Ø. Jensen
Source: Chemical Science (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry The rapid growth of materials chemistry data, driven by advancements in large-scale radiation facilities as well as laboratory instruments, has outpaced conventional data analysis and modelling methods, which can require enormous manual effort. To address this bottleneck, we investigate the application of supervised and unsupervised machine learning (ML) techniques for scattering and spectroscopy data analysis in materials chemistry research. Our perspective focuses on ML applications in powder diffraction (PD), pair distribution function (PDF), small-angle scattering (SAS), inelastic neutron scattering (INS), and X-ray absorption spectroscopy (XAS) data, but the lessons that we learn are generally applicable across materials chemistry. We review the ability of ML to identify physical and structural models and extract information efficiently and accurately from experimental data. Furthermore, we discuss the challenges associated with supervised ML and highlight how unsupervised ML can mitigate these limitations, thus enhancing experimental materials chemistry data analysis. Our perspective emphasises the transformative potential of ML in materials chemistry characterisation and identifies promising directions for future applications. The perspective aims to guide newcomers to ML-based experimental data analysis. Spectroscopy ; Materials science ; Chemistry ; Computer science ; Analytical Chemistry (journal) ; Engineering physics ; Engineering ; Physics ; Environmental chemistry ; Astronomy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging
Authors: M. Boudalia | R.M. Fernández‐Domene | Lei Guo | S. Echihi | M.E. Belghiti | A. Zarrouk | A. Bellaouchou | A. Guenbour | J. García‐Antón
Source: Materials (article, 2023-01-10)
Field: Materials Science — Materials Chemistry

Experimental and Theoretical Tests on the Corrosion Protection of Mild Steel in Hydrochloric Acid Environment by the Use of Pyrazole Derivative In this study, 1,5-diallyl-1H-pyrazolo [3,4-d] pyrimidin-4 (5H)-one (PPD) was evaluated as an anticorrosion agent for mild steel (MS) in 1 M HCl. The analysis was performed by weight loss (WL), potentiodynamic polarization measurement, and electrochemical impedance spectroscopy (EIS). The Tafel polarization showed that PPD is a mixed-type inhibitor and reaches 94% of the protective efficiency at 10<sup>-3</sup> M. EIS results indicated that the resistance to charge transfer increases with increasing inhibitor concentration and the corrosion of MS is controlled by a charge transfer process. The inhibitor adsorption on the MS surface obeyed the Langmuir's adsorption isotherm. Thermodynamic parameters were calculated to elaborate the corrosion inhibition mechanism. The micrographic analysis revealed the existence of a barrier layer on the electrode surface with the presence of PPD. Theoretical examinations performed by electronic/atomic computer simulations confirmed that the obtained results were found to be consistent with experimental findings. Tafel equation ; Hydrochloric acid ; Dielectric spectroscopy ; Corrosion ; Adsorption ; Materials science ; Corrosion inhibitor ; Pyrazole ; Langmuir adsorption model ; Polarization (electrochemistry) ; Electrochemistry ; Electrode ; Nuclear chemistry ; Inorganic chemistry ; Analytical Chemistry (journal) ; Chemistry ; Metallurgy ; Physical chemistry ; Chromatography ; Organic chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Dan Chicea | Alexandra Nicolae-Maranciuc | A. S. Doroshkevich | Liana Maria Chicea | Osman Murat Özkendir
Source: Materials (article, 2023-07-26)
Field: Materials Science — Materials Chemistry

Comparative Synthesis of Silver Nanoparticles: Evaluation of Chemical Reduction Procedures, AFM and DLS Size Analysis The size of silver nanoparticles plays a crucial role in their ultimate application in the medical and industrial fields, as their efficacy is enhanced by decreasing dimensions. This study presents two chemical synthesis procedures for obtaining silver particles and compares the results to a commercially available Ag-based product. The first procedure involves laboratory-based chemical reduction using D-glucose (C<sub>6</sub>H<sub>12</sub>O<sub>6</sub>) and NaOH as reducing agents, while the second approach utilizes trisodium citrate dehydrate (C<sub>6</sub>H<sub>5</sub>Na<sub>3</sub>O<sub>7</sub>·2H<sub>2</sub>O, TSC). The Ag nanoparticle suspensions were examined using FT-IR and UV-VIS spectroscopy, which indicated the formation of Ag particles. The dimensional properties were investigated using Atomic Force Microscopy (AFM) and confirmed by Dynamic Light Scattering (DLS). The results showed particle size from microparticles to nanoparticles, with a particle size of approximately 60 nm observed for the laboratory-based TSC synthesis approach. Dynamic light scattering ; Trisodium citrate ; Particle size ; Nanoparticle ; Materials science ; Atomic force microscopy ; Reducing agent ; Chemical reduction ; Silver nanoparticle ; Nanotechnology ; Chemical engineering ; Particle (ecology) ; Nuclear chemistry ; Chemistry ; Physical chemistry ; Electrochemistry Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticle-Based Drug Delivery
Authors: Alejandro G. Roca | José Francisco López-Barberá | Aritz Lafuente | Faruk Özel | Elvira Fantechi | Javier Muro-Cruces | Miryana Hémadi | Borja Sepúlveda | J. Nogués
Source: Physics Reports (article, 2023-11-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Iron oxide nanoparticles (Fe3O4, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e889" altimg="si63.svg"><mml:mi>γ</mml:mi></mml:math>-Fe2O3 and FeO) as photothermal heat mediators in the first, second and third biological windows Nanotherapies are gaining increased interest for the treatment diverse diseases, particularly cancer, since they target the affected area directly, presenting higher efficacy and reduced side effects than traditional therapies. A promising nanotherapy approach is hyperthermia, where the nanoparticle can induce a local temperature increase by an external stimulus in the sick tissue to selectively kill the malignant cells. Among the diverse hyperthermia methods, photothermia is based on the absorption of light by the nanoparticles and further conversion into heat. Within the very wide range of nanostructured photothermal agents, iron oxides offer remarkable features since they are already approved by the FDA/EMA for various biomedical applications, they are biodegradable, easily manipulated using magnetic fields and can be imaged by diverse techniques. Here, we summarize the advantages of using the second biological window, both from the perspective of the skin and the optical properties of iron oxides. Further, we review the photothermal performance of iron oxide nanoparticles in the first, second and third biological windows. Overall, the results show that, for different types of iron oxide nanoparticles (Fe3O4, γ-Fe2O3, wüstite-FeO), both the heating capacity (i.e., induced temperature increase) and the photothermal conversion efficiency, η, vary in a complex way with the light wavelength, depending critically on the measurement conditions and physiochemical properties of the materials. Despite the spread in the reported photothermal properties of iron oxides, Fe3O4 particles tend to perform better than their γ-Fe2O3 counterparts, particularly in the second biological window. Interestingly, FeO, which has not been exploited so far from a photothermal perspective, shows very appealing absorption properties. Our preliminary studies using FeO/Fe3O4 core/shell nanoparticles evidence that they have excellent photothermal properties, outperforming Fe3O4 in both first and second biological windows. Finally, some applications beyond cancer treatment of iron oxide nanoparticles, exploiting the enhanced properties in the second spectral window, are discussed. Photothermal therapy ; Iron oxide ; Nanoparticle ; Iron oxide nanoparticles ; Wüstite ; Nanotechnology ; Hyperthermia ; Oxide ; Materials science ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics
Authors: Tien-Sinh Vu | Minh-Quyet Ha | Duong‐Nguyen Nguyen | Viet-Cuong Nguyen | Yukihiro Abe | Truyen Tran | Tran Doan Huan | Hiori Kino | Takashi Miyake | Koji Tsuda | Hieu‐Chi Dam
Source: npj Computational Materials (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

Towards understanding structure–property relations in materials with interpretable deep learning Abstract Deep learning (DL) models currently employed in materials research exhibit certain limitations in delivering meaningful information for interpreting predictions and comprehending the relationships between structure and material properties. To address these limitations, we propose an interpretable DL architecture that incorporates the attention mechanism to predict material properties and gain insights into their structure–property relationships. The proposed architecture is evaluated using two well-known datasets (the QM9 and the Materials Project datasets), and three in-house-developed computational materials datasets. Train–test–split validations confirm that the models derived using the proposed DL architecture exhibit strong predictive capabilities, which are comparable to those of current state-of-the-art models. Furthermore, comparative validations, based on first-principles calculations, indicate that the degree of attention of the atoms’ local structures to the representation of the material structure is critical when interpreting structure–property relationships with respect to physical properties. These properties encompass molecular orbital energies and the formation energies of crystals. The proposed architecture shows great potential in accelerating material design by predicting material properties and explicitly identifying crucial features within the corresponding structures. Property (philosophy) ; Representation (politics) ; Computer science ; Architecture ; Artificial intelligence ; Mechanism (biology) ; Machine learning ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Soroosh Hakimian | Shamim Pourrahimi | Abdel‐Hakim Bouzid | Lucas A. Hof
Source: Computational Materials Science (article, 2023-06-24)
Field: Materials Science — Materials Chemistry

Application of machine learning for the classification of corrosion behavior in different environments for material selection of stainless steels Random forest ; Support vector machine ; Corrosion ; Decision tree ; Classifier (UML) ; Machine learning ; Artificial intelligence ; Confusion matrix ; Computer science ; Confusion ; Predictive modelling ; Materials science ; Metallurgy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Rui Zhao | Shucheng Wang | Zhuangzhuang Kong | Yunlei Xu | Kuan Fu | Ping Peng | Cuilan Wu
Source: Materials & Design (article, 2023-05-19)
Field: Materials Science — Materials Chemistry

Development of a neuroevolution machine learning potential of Pd-Cu-Ni-P alloys Pd-Cu-Ni-P alloy is an ideal model system of metallic glass known for its exceptional glass-forming ability. However, few correlation of structures with properties was systematically investigated owing to a lack of interatomic potential. In this work, a neuroevolution machine learning potential (NEP) with efficiency close to embedded atom method (EAM) potentials is developed. Its accuracy has been compared to density functional theory (DFT) calculations. For energy, force and virial, the training errors are 6.0 meV/atom, 111.1 meV/Å and 21.5 meV/atom, respectively. By means of this NEP, several thermodynamic parameters such as glass transition temperatures and pair distribution functions of Pd40Cu30Ni10P20 and Pd40Ni40P20 liquid and glassy alloys as well as their short-range orders, tensile and compression strengths, transport properties etc. have been evaluated by a series of molecular dynamics simulations. A good agreement with DFT calculations and previous experiments indicates this NEP provides an accurate and efficient scheme in the analysis and exploration of microstructures, thermodynamic and kinetic properties of Pd-Cu-Ni-P alloys. Materials science ; Molecular dynamics ; Atom (system on chip) ; Embedded atom model ; Amorphous metal ; Density functional theory ; Virial theorem ; Kinetic energy ; Thermodynamics ; Interatomic potential ; Alloy ; Atomic radius ; Microstructure ; Metallurgy ; Physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Phase-change materials and chalcogenides ; Metallic Glasses and Amorphous Alloys
Authors: Yang Gao | Ludi Wang | Xueqing Chen | Yi Du | Bin Wang
Source: ACS Catalysis (article, 2023-06-13)
Field: Materials Science — Materials Chemistry

Revisiting Electrocatalyst Design by a Knowledge Graph of Cu-Based Catalysts for CO<sub>2</sub> Reduction Electrocatalysis takes a significant role in the production of sustainable fuels and chemicals. The combination of artificial intelligence and catalytic science is exhibiting great potential to extract, analyze, and predict electrocatalysts. However, the currently developed machine learning approach usually requires a mass of data from density functional theory calculations to train and optimize models. In contrast, a knowledge graph has the potential to extract useful information from a large amount of the literature without referring to density functional theory. Herein, a knowledge graph of Cu-based electrocatalysts for electrocatalytic CO2 reduction is constructed based on a linguistically enriched SciBERT-based framework. This framework retrieves multiple types of entities including material, regulation method, product, Faradaic efficiency, etc. from 757 scientific literature, generates representations with abundant domain-specific semantic information, and exhibits the capability to deal with electrocatalysts for CO2 reduction. The obtained graph shows the development history of related catalysts, builds relationships between the factors associated with catalysis, and provides intuitive charts for researchers to gain useful information. Furthermore, we propose a deep learning-based prediction model, which integrates the semantic information from the scientific literature (word embedding) with the correlation of knowledge triples (graph embedding) and realizes the prediction of the Faradaic efficiency for a targeted case. This work paves the way for catalyst design in the manner of merging artificial intelligence with catalytic science. Embedding ; Electrocatalyst ; Computer science ; Graph ; Graph theory ; Density functional theory ; Artificial intelligence ; Catalysis ; Chemistry ; Biochemical engineering ; Theoretical computer science ; Machine learning ; Electrochemistry ; Mathematics ; Computational chemistry ; Engineering Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Tingting Wu | Gaole Dai | Jinjia Xu | Fang Cao | Xiaohong Zhang | Yu Zhao | Yumin Qian
Source: Rare Metals (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Structural design of organic battery electrode materials: from DFT to artificial intelligence Abstract Redox‐active organic materials are emerging as the new playground for the design of new exciting battery materials for rechargeable batteries because of the merits including structural diversity and tunable electrochemical properties that are not easily accessible for the inorganic counterparts. More importantly, the sustainability developed by using naturally abundant chemical elements (C, H, N, O and S) makes them as an ideal alternative material for Li‐ion batteries (LIBs). However, the identification and screening of proper organic materials is still challenging in the past decades. Assisted by the artificial intelligence, this review will give a summary of the theoretical design aspects of redox‐active organic materials from density‐functional theory (DFT) to machine learning (ML) methods in the past two decades, with a particular emphasis on the calculation method to predict the chemical/electrochemical stability and reversibility. This review will also analyze and discuss the challenges and perspectives for the development of organic battery materials. Materials science ; Battery (electricity) ; Electrode ; Nanotechnology ; Engineering physics ; Engineering ; Thermodynamics ; Physical chemistry ; Chemistry Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Advancements in Battery Materials
Authors: Hongliang Li | Jin Tae Kim | Jinsoo Kim | Duk‐Yong Choi | Sang‐Shin Lee
Source: ACS Photonics (article, 2023-03-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface-Incorporated Optofluidic Refractive Index Sensing for Identification of Liquid Chemicals through Vision Intelligence Conventional approaches for the identification of liquid chemicals are bulky and harmful to the environment, detect a limited number of chemical species, produce high false alarm rates, or rely on complex/expensive spectrometers. In this study, a spectrometer-free, accurate metasurface-mediated liquid identification scheme was demonstrated based on optofluidic refractive index (RI) sensing in conjunction with vision intelligence algorithms. A metasurface device integrated into an optofluidic channel provides a polarization-independent focused vortex beam at a single wavelength of 1550 nm, which is highly sensitive to liquid chemicals. The beam patterns respond to the RI and transmission of chemicals, and thus effectively serve as their unique optical “fingerprints”. To realize vision intelligence, two deep-learning architectures─a convolutional neural network and a vision transformer─were adopted and trained to classify the beam patterns. A variety of liquid chemicals were successfully identified in situ with over 99% accuracy, requiring no spectrometers. The proposed approach is expected to corroborate the feasibility of artificial intelligence-powered detection schemes that can classify at single wavelengths, unlike conventional instrument-intensive techniques that are attentive to entire spectral responses. Spectrometer ; Computer science ; Convolutional neural network ; Materials science ; Refractive index ; Optics ; Artificial intelligence ; Optoelectronics ; Physics Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Plasmonic and Surface Plasmon Research
Authors: Keke Tang | Yujie Xiang | Jie Tian | Jixin Hou | Xianyan Chen | Xianqiao Wang | Zheng Zhong
Source: International Journal of Mechanical Sciences (article, 2023-12-29)
Field: Materials Science — Polymers and Plastics

Machine learning-based morphological and mechanical prediction of kirigami-inspired active composites Preprocessor ; Deformation (meteorology) ; Finite element method ; Artificial intelligence ; Stress (linguistics) ; Field (mathematics) ; Transformation (genetics) ; Computer science ; Displacement (psychology) ; Data pre-processing ; Machine learning ; Structural engineering ; Engineering ; Materials science ; Composite material ; Mathematics Polymer composites and self-healing ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: Jianhang Hu | Wei Zhang | Chuang-Xin Ren | Yu Xiong | Jiayi Zhang | Jiao He | Ying Huang | Zhu Tao | Xin Xiao
Source: Analytica Chimica Acta (article, 2023-03-21)
Field: Materials Science — Materials Chemistry

A novel portable smart phone sensing platform based on a supramolecular fluorescence probe for quick visual quantitative detection of picric acid Picric acid ; Chemistry ; Supramolecular chemistry ; Fluorescence ; Analyte ; Smart phone ; Analytical Chemistry (journal) ; Nanotechnology ; Chromatography ; Organic chemistry ; Computer science ; Molecule Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes