jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bohayra Mortazavi | Yves Rémond | Hongyuan Fang | Timon Rabczuk | Xiaoying Zhuang
Source: Materials Today Communications (article, 2023-08-01)
Field: Chemistry — Organic Chemistry

Hexagonal boron-carbon fullerene heterostructures; Stable two-dimensional semiconductors with remarkable stiffness, low thermal conductivity and flat bands Materials science ; Fullerene ; Heterojunction ; Boron ; Thermal conductivity ; Hexagonal boron nitride ; Hexagonal crystal system ; Semiconductor ; Carbon fibers ; Condensed matter physics ; Nanotechnology ; Composite material ; Optoelectronics ; Crystallography ; Graphene ; Composite number ; Organic chemistry Fullerene Chemistry and Applications ; Graphene research and applications ; Thermal properties of materials
Authors: Abhishek Subedi | Alessandro Campari | Antonio Alvaro | Biraj Singh Thapa | Nicola Paltrinieri
Source:  (article, 2023-01-01)
Field: Materials Science — Metals and Alloys

Evaluation of the Factors Determining Hydrogen Embrittlement in Pipeline Steels: An Artificial Intelligence Approach Hydrogen is an emerging energy carrier with inherent environmental benefits.It has the potential to decarbonize industrial applications that require high-grade heat.In addition, hydrogen allows centralized clean energy production and distribution to remote end-use sites.For a smooth transition to hydrogen technologies, it is important to guarantee a safe and reliable distribution system.Hydrogen could be transported through the existing widespread pipeline network.Nevertheless, most pipeline steels were not designed for hydrogen service and are prone to hydrogen-induced degradation, which could result in sudden component failures and undesired releases with severe consequences.Hydrogen embrittlement depends on the interplay of three factors, i.e., the mechanical loading, the operating environment, and the material properties.The synergistic interaction of these parameters has significant safety implications.This study introduces a machine learning approach to evaluate the role of these factors in the occurrence of hydrogen-induced damages.Several pipeline steels have been assessed for embrittlement under different environmental and loading conditions.An extensive database has been created, and a decision tree model has been trained to predict the hydrogen embrittlement of materials.The main advantages of this model are its "white box" nature and simple interpretability.This artificial intelligence approach can ensure the safe application of hydrogen systems and allow advancements in inspection planning and predictive maintenance. Hydrogen embrittlement ; Hydrogen production ; Hydrogen ; Hydrogen technologies ; Embrittlement ; Hydrogen fuel ; Pipeline (software) ; Computer science ; Interpretability ; Materials science ; Environmental science ; Hydrogen economy ; Process engineering ; Forensic engineering ; Artificial intelligence ; Engineering ; Metallurgy ; Corrosion ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Material Properties and Failure Mechanisms ; Fatigue and fracture mechanics
Authors: Federico J. Gonzalez | Giulia N. Seminara | Miranda I. López | Juan M. Lombardi | M. Ramos | C A Tachino | A. Martinez | H. F. Busnengo
Source: The Journal of Chemical Physics (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

Assessing the dynamics of CO adsorption on Cu(110) using the vdW-DF2 functional and artificial neural networks In this work, we revisit the dynamics of carbon monoxide molecular chemisorption on Cu(110) by using quasi-classical trajectory calculations. The molecule-surface interaction is described through an atomistic neural network approach based on Density Functional Theory calculations using a nonlocal exchange-correlation (XC) functional that includes the effect of long-range dispersion forces: vdW-DF2 [Lee et al. Phys. Rev. B, 82, 081101 (2010)]. With this approach, we significantly improve the agreement with experiments with respect to a similar previous study based on a semi-local XC functional. In particular, we obtain excellent agreement with molecular beam experimental data concerning the dependence of the initial sticking probability on surface temperature and impact energy at normal incidence. For off-normal incidence, our results also reproduce two trends observed experimentally: (i) the preferential sticking for molecules impinging parallel to the [1̄10] direction compared to [001] and (ii) the change from positive to negative scaling as the impact energy increases. Nevertheless, understanding the origin of some remaining quantitative discrepancies with experiments requires further investigations. Artificial neural network ; Adsorption ; Biological system ; Materials science ; Dynamics (music) ; Artificial intelligence ; Computer science ; Mathematics ; Chemistry ; Physics ; Physical chemistry ; Biology ; Acoustics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Catalysis and Oxidation Reactions
Authors: Beatriz Amaya Dolores | Andrés Ruiz Flores | Andrés Núñez | José J. Calvino | J. Almagro | Luc Lajaunie
Source: Materials (article, 2023-02-23)
Field: Materials Science — Metals and Alloys

Textural, Microstructural and Chemical Characterization of Ferritic Stainless Steel Affected by the Gold Dust Defect The "gold dust defect" (GDD) appears at the surface of ferritic stainless steels (FSS) and degrades their appearance. Previous research showed that this defect might be related to intergranular corrosion and that the addition of aluminium improves surface quality. However, the nature and origin of this defect are not properly understood yet. In this study, we performed detailed electron backscatter diffraction analyses and advanced monochromated electron energy-loss spectroscopy experiments combined with machine-learning analyses in order to extract a wealth of information on the GDD. Our results show that the GDD leads to strong textural, chemical, and microstructural heterogeneities. In particular, the surface of affected samples presents an α-fibre texture which is characteristic of poorly recrystallised FSS. It is associated with a specific microstructure in which elongated grains are separated from the matrix by cracks. The edges of the cracks are rich in chromium oxides and MnCr<sub>2</sub>O<sub>4</sub> spinel. In addition, the surface of the affected samples presents a heterogeneous passive layer, in contrast with the surface of unaffected samples, which shows a thicker and continuous passive layer. The quality of the passive layer is improved with the addition of aluminium, explaining the better resistance to the GDD. Materials science ; Microstructure ; Spinel ; Electron backscatter diffraction ; Aluminium ; Metallurgy ; Layer (electronics) ; Texture (cosmology) ; Intergranular corrosion ; Corrosion ; Chromium ; Characterization (materials science) ; Composite material ; Nanotechnology Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques
Authors: Jiaxi Deng | Yi Ma | Xiaofang Liu | Jingbing Xu | Huibo Luo | Xiaogang Luo | Danqun Huo | Changjun Hou
Source: Analytical Methods (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Identification of Chinese baijiu from the same brand based on a graphene quantum dots fluorescence sensing array The identification of Chinese baijiu is crucial to regulating the international market and maintaining legitimate rights, as the popularity, influence and awareness of baijiu are growing. A graphene quantum dot (GQD) based fluorescence sensor array is designed in this paper. Upon using only GQDs as a single sensing element, combining three different solvents improves the sensing array's detection sensitivity while simplifying material preparation and experimental detection. Adding organic substances creates intermolecular forces between the GQDs and the solvent, causing the fluorescence intensity to change. The sensor array was able to distinguish 21 types of organic matter, different ratios of quaternary mixed organic materials and 17 types of baijiu of the same brand. It also showed excellent performance in the detection of species in blind samples, with the machine learning algorithm successfully distinguishing baijiu from five other distilled spirits. The experiment provides guidance for the practical application of GQDs and provides a simple but effective reference for sensor arrays to detect baijiu. Graphene ; Identification (biology) ; Fluorescence ; Quantum dot ; Chemistry ; Nanotechnology ; Optoelectronics ; Materials science ; Physics ; Optics ; Biology ; Botany Dye analysis and toxicity ; Microbial Metabolism and Applications
Authors: Ling Liu | Xiaoqing Wang | Xiaoming Lyu | Ruiyao Ma | Yuting Li | Ting Zhang | Yongdan Liu | Changhao Sun | Yang Li
Source: Microchimica Acta (article, 2023-11-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Label-free detection of vitamin B by two-step enhanced Raman technique using dynamic borohydride-reduced silver nanoparticles Sodium borohydride ; Silver nanoparticle ; Surface plasmon resonance ; Raman spectroscopy ; Nanoparticle ; Molecule ; Raman scattering ; Adsorption ; Chemistry ; Analyte ; Nanotechnology ; Borohydride ; Plasmon ; Materials science ; Analytical Chemistry (journal) ; Chromatography ; Organic chemistry ; Optoelectronics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Artem Fediai | Patrick Reiser | Jorge Enrique Olivares Peña | Wolfgang Wenzel | Pascal Friederich
Source: Machine Learning Science and Technology (article, 2023-08-30)
Field: Materials Science — Materials Chemistry

Interpretable delta-learning of GW quasiparticle energies from GGA-DFT Abstract Accurate prediction of the ionization potential and electron affinity energies of small molecules are important for many applications. Density functional theory (DFT) is computationally inexpensive, but can be very inaccurate for frontier orbital energies or ionization energies. The GW method is sufficiently accurate for many relevant applications, but much more expensive than DFT. Here we study how we can learn to predict orbital energies with GW accuracy using machine learning (ML) on molecular graphs and fingerprints using an interpretable delta-learning approach. ML models presented here can be used to predict quasiparticle energies of small organic molecules even beyond the size of the molecules used for training. We furthermore analyze the learned DFT-to-GW corrections by mapping them to specific localized fragments of the molecules, in order to develop an intuitive interpretation of the learned corrections, and thus to better understand DFT errors. Quasiparticle ; Density functional theory ; Ionization ; Ionization energy ; Molecule ; Physics ; Interpretation (philosophy) ; Atomic orbital ; Electron affinity (data page) ; GW approximation ; Molecular orbital ; Statistical physics ; Computer science ; Electron ; Machine learning ; Quantum mechanics ; Ion Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Simon Axelrod | Eugene I. Shakhnovich | Rafael Gómez‐Bombarelli
Source: Journal of Chemical Information and Modeling (article, 2023-09-06)
Field: Materials Science — Materials Chemistry

Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling Light-activated drugs are a promising way to localize biological activity and minimize side effects. However, their development is complicated by the numerous photophysical and biological properties that must be simultaneously optimized. To accelerate the design of photoactive drugs, we describe a procedure that combines ligand-protein docking with chemical property prediction based on machine learning (ML). We apply this procedure to 58 proteins and 9000 photo-drug candidates based on azobenzene cis-trans isomerism. We find that most proteins display a preference for trans isomers over cis and that the binding affinities of nominally active/inactive pairs are in fact highly correlated. These findings have significant value for photopharmacology research, and reinforce the need for virtual screening to identify compounds with rare desirable properties. Further, we combine our procedure with quantum chemical validation to identify promising candidates for the photoactive inhibition of PARP1, an enzyme that is over-expressed in cancer cells. The top compounds are predicted to have long-lived active forms, differential bioactivity, and absorption in the near-infrared therapeutic window. Azobenzene ; Virtual screening ; Docking (animal) ; Chemistry ; Ligand (biochemistry) ; Combinatorial chemistry ; Affinities ; Active site ; Drug discovery ; Chemical space ; Computational biology ; Enzyme ; Stereochemistry ; Molecule ; Biology ; Biochemistry ; Organic chemistry Photochromic and Fluorescence Chemistry ; Click Chemistry and Applications ; Chemical Synthesis and Analysis
Authors: Daniil A. Boiko | Alexey S. Kashin | Vyacheslav R. Sorokin | Yury V. Agaev | Roman G. Zaytsev | Valentine P. Ananikov
Source: Journal of Molecular Liquids (article, 2023-02-09)
Field: Chemistry — Electrochemistry

Analyzing ionic liquid systems using real-time electron microscopy and a computational framework combining deep learning and classic computer vision techniques Computer science ; Software ; Transformation (genetics) ; Artificial intelligence ; Computational science ; Chemistry Electrochemical Analysis and Applications ; Ionic liquids properties and applications ; Machine Learning in Materials Science
Authors: A. Flanagan | Frank G. Glavin
Source:  (review, 2023-12-07)
Field: Chemistry — Analytical Chemistry

A Systematic Review of Multi-Class and One-vs-Rest Classification Techniques for Near-Infrared Spectra of Crop Cultivars Near-infrared (NIR) spectroscopy is a simple and non-destructive technique that provides a wealth of information on the chemical composition and physical properties of a sample. NIR spectra are produced as a function of the energy transition between electromagnetic radiation in the near-infrared region (700 - 2500 nm) and matter. The spectra are complex and typically contain broad overlapping absorption bands that are difficult to interpret. Chemometrics is the field of study that encompasses various multivariate data analysis methods for qualitative and quantitative analysis of chemical data via methods such as spectroscopy. Due to the volatile nature of spectroscopic data, there is no one-size-fits-all approach to modelling such tasks. In this work, we perform a systematic review of various modelling approaches for the task of crop cultivar identification of Barley, Chickpea, and Sorghum grains. Our analysis includes two established discriminant analysis methods commonly applied in chemometrics, and three Machine Learning algorithms. Fur-thermore, we compare multiclass classification, as the task was originally represented, with a one-vs-rest classification approach. We demonstrate that one-vs-rest classification is a strong al-ternative modelling approach with the ability to improve the classification score and highlight the weaker target classes that affect performance in multiclass classification problems. Rest (music) ; Cultivar ; Crop ; Class (philosophy) ; Computer science ; Spectral line ; Remote sensing ; Agricultural engineering ; Artificial intelligence ; Agronomy ; Physics ; Engineering ; Geology ; Biology ; Acoustics ; Astronomy Spectroscopy and Chemometric Analyses
Authors: Zibing Su | Lin Xing | H. Elhosiny Ali | Tamim Alkhalifah | Fahad Alturise | Mohamed Amine Khadimallah | Hamid Assilzadeh
Source: Chemosphere (article, 2023-04-10)
Field: Chemistry — Inorganic Chemistry

Latest insights on separation and storage of carbon compounds in buildings towards sustainable environment: Recent innovations, challenges, future perspectives and application of machine learning Renewable energy ; Sustainable development ; Environmental remediation ; Environmental science ; Metal-organic framework ; Greenhouse gas ; Environmental economics ; Business ; Engineering ; Chemistry ; Political science ; Ecology Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis
Authors: Yangxiao Ma | Xiaohua Yu | Zhongwei Liu | Kun Meng | Jiongjiong Xu | Xiuhan Li | Hongying Hou | Zhaohua Liu
Source: Materials Today Communications (article, 2023-03-06)
Field: Materials Science — Polymers and Plastics

Full-scale simulation and experimental verification of the phase-transition temperature of a VO2 nanofilm as smart window materials Materials science ; Phase transition ; Nanotechnology ; Atomic layer deposition ; Nanoscopic scale ; Nanometre ; Nanomaterials ; Phase (matter) ; Transition temperature ; Molecular dynamics ; Chemical physics ; Layer (electronics) ; Thermodynamics ; Condensed matter physics ; Composite material ; Computational chemistry ; Physics Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; ZnO doping and properties
Authors: Shuming Zeng | Lei Fang | Zonglin Gu | Xinming Wang | Yinchang Zhao | Geng Li | Yusong Tu | Jun Ni
Source: The Journal of Chemical Physics (article, 2023-07-05)
Field: Materials Science — Materials Chemistry

Ultralow lattice thermal conductivities and excellent thermoelectric properties of hypervalent triiodides XI3 (X = Rb, Cs) discovered by machine learning method Thermal conductivity and power factor are key factors in evaluating heat transfer performance and designing thermoelectric conversion devices. To search for materials with ultralow thermal conductivity and a high power factor, we proposed a set of universal statistical interaction descriptors (SIDs) and developed accurate machine learning models for the prediction of thermoelectric properties. For lattice thermal conductivity prediction, the SID-based model achieved the state-of-the-art results with an average absolute error of 1.76 W m-1 K-1. The well-performing models predicted that hypervalent triiodides XI3 (X = Rb, Cs) have ultralow thermal conductivities and high power factors. Combining first-principles calculations, the self-consistent phonon theory, and the Boltzmann transport equation, we obtained the anharmonic lattice thermal conductivities of 0.10 and 0.13 W m-1 K-1 for CsI3 and RbI3 in the c-axis direction at 300 K, respectively. Further studies show that the ultralow thermal conductivity of XI3 arises from the competition of vibrations between alkali metal atoms and halogen atoms. In addition, at 700 K, the thermoelectric figure of merit ZT values of CsI3 and RbI3 are 4.10 and 1.52, respectively, at the optimal hole doping level, which indicates hypervalent triiodides are potential high performance thermoelectric materials. Hypervalent molecule ; Thermoelectric effect ; Materials science ; Thermal ; Lattice (music) ; Thermal conductivity ; Thermoelectric materials ; Engineering physics ; Condensed matter physics ; Thermodynamics ; Composite material ; Engineering ; Physics ; Metallurgy ; Acoustics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Jon Bonnar anak Jores | Elammaran Jayamani | Tasha Sie Ming Lai | Jeyanthi Subramanian | C.R. Rejeesh
Source: Materials Today Proceedings (article, 2023-09-09)
Field: Materials Science — Polymers and Plastics

Evaluation of mechanical property of banana HDPE composite through experiment and artificial neural network (ANN) In this study, two post-curing techniques using microwave and oven were utilized to examine the mechanical characteristics and behavior of composites reinforced with banana fibers with its matrix made of high-density polyethylene polymer. The research involved experimental work through tensile tests and predictive modelling using the artificial neural network (ANN). The first part of the research involved the collection of datasets which were gathered from the tensile tests based on the ASTM D638 using dog bone specimens of various fiber percentages to compare the mechanical properties such as the specific energy and maximum load after post-curing using an oven and microwave at various powers and time. The trend and behavior of the composite due to the fiber percentages and the effects of post curing methods were analyzed. The second part of the research involved the ANN approach to predict the maximum gage length based on the data collected from the experiments using inputs such as the fiber content, maximum load, and displacement. Further optimization was done on the ANN model by fine-tuning variables such as the learning function, transfer function, and number of neurons to predict the stress and strain values. The accuracy of the results was calculated using statistical evaluation equations. The optimized settings were utilized to generate the predicted results and the deviation from the experimental results were evaluated. Artificial neural network ; Materials science ; Ultimate tensile strength ; Composite number ; Composite material ; High-density polyethylene ; Curing (chemistry) ; Design of experiments ; Structural engineering ; Polyethylene ; Computer science ; Machine learning ; Mathematics ; Engineering ; Statistics Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Innovative concrete reinforcement materials
Authors: Jeong-Hun Kim | Hyunseok Ko | Dong-Hun Yeo | Zeehoon Park | Upendra Kumar | Kwan‐Hee Yoo | Aziz Nasridinov | Sung Beom Cho
Source: Materials & Design (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

A versatile strategy for hybridizing small experimental and large simulation data: A case for ceramic tape-casting process In manufacturing industry, finding optimal design parameters for targeted properties has traditionally been guided by trial and error. However, limited data availability to few hundreds sets of experimental data in typical materials processes, the machine-learning capabilities and other data-driven modeling (DDM) techniques are too far from it to be practical. In this study, we show how a versatile design strategy, tightly coupled with physics-based modeling (PBM) data, can be applied to small set of experimental data to improve the optimization of process parameters. Our strategy uses PBM to achieve augmented data that includes essential physics: in other words, the PBM data allows the inverse design model to ‘learn’ physics, indirectly. We demonstrated the accuracy of both forward-prediction and inverse-optimization have been dramatically improved with the help of PBM data, which are relatively cheap and abundant. Furthermore, we found that the inverse model with augmented data can accurately optimize process parameters, even for ones those were not considered in the simulation. Such versatile strategy can be helpful for processes/experiments for the cases where the number of collectable data is limited, which is most of the case in industries. Process (computing) ; Experimental data ; Inverse ; Sand casting ; Set (abstract data type) ; Data set ; Design of experiments ; Computer science ; Mechanical engineering ; Materials science ; Engineering ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Injection Molding Process and Properties
Authors: Uttam Kumar Kar | J. Srinivas
Source: European Journal of Mechanics - A/Solids (article, 2023-02-02)
Field: Materials Science — Materials Chemistry

Dynamic analysis and identification of bi-directional functionally graded elastically supported cracked microbeam subjected to thermal shock loads Microbeam ; Finite element method ; Vibration ; Mechanics ; Materials science ; Boundary value problem ; Structural engineering ; Natural frequency ; Nonlinear system ; Thermal shock ; Shock (circulatory) ; Acoustics ; Engineering ; Mathematics ; Physics ; Mathematical analysis ; Optics ; Composite material Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering