jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abdourahman Khaireh-Walieh | Alexandre Arnoult | Sébastien Plissard | Peter R. Wiecha
Source: Crystal Growth & Design (article, 2023-01-16)
Field: Materials Science — Materials Chemistry

Monitoring MBE Substrate Deoxidation via RHEED Image-Sequence Analysis by Deep Learning Reflection high-energy electron diffraction (RHEED) is a powerful tool in molecular beam epitaxy (MBE), but RHEED images are often difficult to interpret, requiring experienced operators. We present an approach for automated surveillance of GaAs substrate deoxidation in MBE reactors using deep-learning-based RHEED image-sequence classification. Our approach consists of an nonsupervised autoencoder (AE) for feature extraction, combined with a supervised convolutional classifier network. We demonstrate that our lightweight network model can accurately identify the exact deoxidation moment. Furthermore, we show that the approach is very robust and allows accurate deoxidation detection for months without requiring retraining. The main advantage of the approach is that it can be applied to raw RHEED images without requiring further information such as the rotation angle, temperature, etc. Reflection high-energy electron diffraction ; Substrate (aquarium) ; Molecular beam epitaxy ; Sequence (biology) ; Materials science ; Deep learning ; Crystallography ; Nanotechnology ; Chemistry ; Epitaxy ; Artificial intelligence ; Computer science ; Biology ; Biochemistry Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Industrial Vision Systems and Defect Detection
Authors: Caihua Wang | Yangyang Wan | Shaokang Yang | Yuee Xie | S. N. G. Chu | Yuanping Chen | Xiaohong Yan
Source: Advanced Functional Materials (article, 2023-12-15)
Field: Chemistry — Inorganic Chemistry

Revealing the Untapped Potential of Photocatalytic Overall Water Splitting in Metal Organic Frameworks Abstract The past decade witnessed substantial attention toward metal‐organic frameworks (MOFs) for photocatalytic water splitting owing to their versatile structural and optoelectronic characteristics. However, MOFs capable of efficient photocatalytic overall water splitting (OWS) remain notably scarce. Although MOF‐based photocatalysts with OWS potential are highly promising due to their diverse building blocks and topological configurations, the vast number of possible MOFs renders traditional trial‐and‐error materials discovery approaches impractical. Herein, a data‐driven methodology that integrates machine learning with high‐throughput first‐principles computations to identify MOFs with OWS capability is presented. By systematically assessing factors including water stability, band gap, band positions, charge carrier transport, and optical absorption properties, 14 MOFs from the Quantum‐MOF (QMOF) database containing over 20,000 MOFs as promising candidates for visible‐light‐driven OWS are identified. Notably, five of them exhibit exceptional electronic and optical properties, outperforming previously reported MOF OWS photocatalysts, such as UIO66(Zr)‐NH 2 , MIL125(Ti)‐NH 2 , and MIL53(Al)‐NH 2 is established. This work represents a large‐scale, data‐driven exploration of MOF‐based photocatalysts for water splitting, shedding light on the untapped potential of photocatalysis in MOFs. Photocatalysis ; Metal-organic framework ; Materials science ; Water splitting ; Photocatalytic water splitting ; Nanotechnology ; Electronic band ; Band gap ; Visible spectrum ; Optoelectronics ; Catalysis ; Adsorption ; Chemistry ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Ga2O3 and related materials
Authors: Ya Chen | Yaoxiang Li | Roger A. Williams | Zheyu Zhang | Rundong Peng | Xiaoli Liu | Tao Xing
Source: Journal of Food Science (article, 2023-09-27)
Field: Chemistry — Analytical Chemistry

Modeling of soluble solid content of PE‐packaged blueberries based on near‐infrared spectroscopy with back propagation neural network and partial least squares (BP–PLS) algorithm Blueberries are a nutritious and popular berry worldwide. The physical and chemical properties of blueberries constantly change through the cycle of the supply chain (from harvest to sale). The purpose of this study was to develop a rapid method for detecting the properties of packaged blueberries based on near-infrared (NIR) spectroscopy. NIR was applied to quantitatively determine the soluble solid content (SSC) of polyethylene (PE)-packaged blueberries. An orthogonal partial least squares discriminant analysis model was established to show the correlation between spectral data and the measured SSC. Multiplicative scattering correction, standard normal variable, Savitzky-Golay convolution first derivative, and normalization (Normalize) were used for spectra preprocessing. Uninformative variables elimination, competitive adaptive reweighted sampling, and iteratively retaining informative variables were jointly used for wavelength optimization. NIR-based SSC prediction models for unpacked blueberries and PE-packaged blueberries were developed using partial least squares (PLS). The prediction model for PE-packaged samples (R<sub>P</sub> <sup>2</sup> = 0.876, root mean square error of prediction [RMSEP] = 0.632) had less precision than the model for unpacked samples (R<sub>P</sub> <sup>2</sup> = 0.953, RMSEP = 0.611). To reduce the effect of PE, the back propagation (BP) neural network and PLS were combined into the BP-PLS algorithm based on the residual learning algorithm. The model of BP-PLS (R<sub>P</sub> <sup>2</sup> = 0.947, RMSEP = 0.414) was successfully developed to improve the prediction accuracy of SSC for PE-packaged blueberries. The results suggested a promising way of using the BP-PLS method in tandem with NIR for the rapid detection of the SSC of PE-packaged blueberries. PRACTICAL APPLICATION: Most of the NIR-based research used unpacked blueberries as samples, while the use of packaged blueberries would provide researchers with a better understanding of the crucial factors at different phases of the blueberry supply chain (from harvest to sale). To meet market demands and minimize losses, NIR spectroscopy has been proven to be a rapid and nondestructive method for the determination of the SSC of PE-packaged blueberries. This study provides an effective method for monitoring the properties of blueberries in the entire supply chain. Partial least squares regression ; Near-infrared spectroscopy ; Mean squared error ; Algorithm ; Second derivative ; Normalization (sociology) ; Mathematics ; Chemistry ; Artificial intelligence ; Computer science ; Statistics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Xiaohua Liu | Ruihan Wang | Xin Wang | Dingguo Xu
Source: The Journal of Physical Chemistry C (article, 2023-06-02)
Field: Chemistry — Inorganic Chemistry

High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption Some hazardous gases, like toluene vapor, have caused serious environmental pollution. The adsorption of toluene using metal–organic frameworks (MOFs) has been considered a useful mechanism to reduce environmental pollution. High-throughput computation using the grand canonical Monte Carlo (GCMC) approach was used to screen high-performance MOFs from the CoRE MOF database. A total of 802 MOFs are selected with a toluene uptake larger than HKUST-1 (6.34 mmol/g at 1900 Pa, 298 K) and CMOF-3b ([(L3b)Cu2]n, L3b = 4,4′,4″,4‴-(2,2′-dihydroxy-1,1′-binaphthalene-4,4′,6,6′-tetrayl)), showing the highest toluene vapor adsorption capacity (25.57 mmol/g). Approximately 80% of high-performance MOFs contain open metal sites. Further analyses of the quantitative structure–property relationships reveal that the MOF adsorption capacity for toluene could be primarily correlated with gravimetric surface area and void fraction. Moreover, using the HKUST-1 as a template, center-metal element replacement is suggested to be effective in improving toluene vapor adsorption. Finally, based on our previously proposed MOF-CGCNN algorithm, a regression model is developed to predict toluene adsorption capacity. Combined with high-throughput GCMC calculation, the machine learning model is applied to screen a larger MOF database (containing 137,953 hypothetical MOFs), which accelerates the virtual discovery of new high-performance candidate MOFs for toluene adsorption. The proposed strategy will be useful in material design or discovery for reducing toluene, thereby benefitting the environment. Toluene ; Adsorption ; Metal-organic framework ; Gravimetric analysis ; Chemical engineering ; Materials science ; Metal ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Xin Liu | W.A. Curtin
Source: Acta Materialia (article, 2023-10-13)
Field: Materials Science — Biomaterials

Mechanism of thermally-activated prismatic slip in Mg Prismatic slip of the screw <a> dislocation in magnesium at temperatures ≳ 150 K is understood to be governed by double-cross-slip of the stable basal screw through the unstable prism screw and back to the basal screw, with the activation energy controlled by the formation energy of two basal-basal kinks. However, atomistic studies of the double-kink process predict activation energies roughly twice those derived experimentally. Here, a new mechanism of prism glide is proposed, analyzed theoretically, and demonstrated qualitatively and quantitatively via direct molecular dynamics (MD) simulations. The new mechanism is intrinsically 3d, and involves the nucleation of a single kink at the junction where a 3d prismatic dislocation loop transitions from the basal screw segment to non-screw prismatic character. The relevant kink energies are calculated using recently-developed Neural-Network Potentials (NNPs) for Mg that show good agreement versus DFT for basal and prism <a> dislocations, enabling a parameter-free analytic model for the activation barrier. Direct MD simulations show both operation of the precise proposed mechanism and a stress-dependent activation barrier that agrees reasonably with the analytic model. Predictions of dislocation velocity compare very well with in-situ TEM data, and macroscopic strength versus temperature can be also be understood. Overall, the new intrinsically 3d mechanism for dislocation glide due to single kink nucleation rather than double-kink nucleation explains key features of the prismatic slip in Mg and may have broader applicability in other metals where kink nucleation processes control thermally-activated flow. Nucleation ; Slip (aerodynamics) ; Materials science ; Dislocation ; Activation energy ; Crystallography ; Deformation mechanism ; Molecular dynamics ; Condensed matter physics ; Composite material ; Thermodynamics ; Physics ; Computational chemistry ; Physical chemistry ; Chemistry ; Microstructure Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Microstructure and mechanical properties
Authors: Henrique Gasparetto | Ana Carolina Ferreira Piazzi Fuhr | Nina Paula Gonçalves Salau
Source: Journal of Industrial and Engineering Chemistry (article, 2023-03-24)
Field: Chemistry — Organic Chemistry

Forecasting soybean oil extraction using cyclopentyl methyl ether through soft computing models with a density functional theory study Extraction (chemistry) ; Adaptive neuro fuzzy inference system ; Hexane ; Chemistry ; Coefficient of determination ; Biological system ; Mathematics ; Chromatography ; Computer science ; Statistics ; Fuzzy logic ; Artificial intelligence Edible Oils Quality and Analysis ; Meat and Animal Product Quality ; Biodiesel Production and Applications
Authors: Akhil Shajan | Madushanka Manathunga | Andreas W. Götz | Kenneth M. Merz
Source: Journal of Chemical Theory and Computation (article, 2023-10-23)
Field: Materials Science — Materials Chemistry

Geometry Optimization: A Comparison of Different Open-Source Geometry Optimizers Based on a series of energy minimizations with starting structures obtained from the Baker test set of 30 organic molecules, a comparison is made between various open-source geometry optimization codes that are interfaced with the open-source QUantum Interaction Computational Kernel (QUICK) program for gradient and energy calculations. The findings demonstrate how the choice of the coordinate system influences the optimization process to reach an equilibrium structure. With fewer steps, internal coordinates outperform Cartesian coordinates, while the choice of the initial Hessian and Hessian update method in quasi-Newton approaches made by different optimization algorithms also contributes to the rate of convergence. Furthermore, an available open-source machine learning method based on Gaussian process regression (GPR) was evaluated for energy minimizations over surrogate potential energy surfaces with both Cartesian and internal coordinates with internal coordinates outperforming Cartesian. Overall, geomeTRIC and DL-FIND with their default optimization method as well as with the GPR-based model using Hartree-Fock theory with the 6-31G** basis set needed a comparable number of geometry optimization steps to the approach of Baker using a unit matrix as the initial Hessian to reach the optimized geometry. On the other hand, the Berny and Sella offerings in ASE outperformed the other algorithms. Based on this, we recommend using the file-based approaches, ASE/Berny and ASE/Sella, for large-scale optimization efforts, while if using a single executable is preferable, we now distribute QUICK integrated with DL-FIND. Hessian matrix ; Cartesian coordinate system ; Computer science ; Energy minimization ; Geometry ; Algorithm ; Optimization problem ; Coordinate descent ; Curvilinear coordinates ; Mathematical optimization ; Mathematics ; Applied mathematics ; Physics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Jisu Jung | Suyeon Ju | Purun-hanul Kim | Deokgi Hong | Wonseok Jeong | Jinhee Lee | Seungwu Han | Sungwoo Kang
Source: ACS Catalysis (article, 2023-11-30)
Field: Materials Science — Materials Chemistry

Electrochemical Degradation of Pt<sub>3</sub>Co Nanoparticles Investigated by Off-Lattice Kinetic Monte Carlo Simulations with Machine-Learned Potentials In fuel cell applications, the durability of catalysts is critical for large-scale industrial implementation. However, limited synthesis controllability and spectroscopic resolution impede a comprehensive understanding of degradation mechanisms at the atomic level. In this study, we develop a machine-learned potential (MLP) to simulate the degradation processes for Pt3Co nanoparticles. The precision of MLP is determined to be comparable to that of density functional theory calculations. Using off-lattice kinetic Monte Carlo simulations with MLP, we successfully replicate established experimental trends and offer a logical resolution to ongoing debates regarding atomic orderings. Based on the simulation results, we suggest design principles for Pt3Co nanoparticles that combine high activity and durability. Finally, we validate the wide applicability of our method by successfully applying it to Pt3Ni and Pt3Co0.5Ni0.5 nanoparticles. Our research serves as a guideline for developing MLPs for alloy electrochemical catalysts and lays the foundation for designing more durable and active fuel-cell catalysts. Nanoparticle ; Monte Carlo method ; Kinetic Monte Carlo ; Materials science ; Durability ; Electrochemistry ; Fuel cells ; Catalysis ; Controllability ; Lattice (music) ; Computer science ; Nanotechnology ; Chemical engineering ; Chemistry ; Physics ; Physical chemistry ; Electrode ; Mathematics ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Xiaoyong Yang | Rajeev Ahuja | Wei Luo
Source: Nano Energy (article, 2023-05-30)
Field: Materials Science — Materials Chemistry

Strain engineering of the electrocatalytic activity of nitrogen-rich BeN4 Dirac monolayer for hydrogen evolution reaction The strong bond energy and short bond length of N≡N triple bond make it a challenging target for synthesizing nitrogen-rich compounds. However, recent research has successfully fabricated atomic-thick BeN4 layers under high pressure (Bykov et al., 2021). Beryllonitrene, a new 2D material, consists of a Be atom and polymeric nitrogen chains and has anisotropic Dirac cones located near the Fermi level. This distinguishes it from graphene, which has isotropic Dirac cones, bulk PtTe2 and 2D borophene, which have Dirac cones located far from the Fermi energy. The anisotropic Dirac cones in beryllonitrene result in ultrahigh carrier mobility and the potential for direction-dependent quantum devices. In this study, we systematically investigated the hydrogen evolution reaction (HER) catalytic activity of nitrogen-rich, non-precious BeN4 monolayer using first-principles DFT calculations. Our results demonstrate that BeN4 monolayer is thermally stable, and Be vacancy is the most energetically favorable site for hydrogen adsorption. We also found the Gibbs free energy (ΔGH∗) of H∗ coverage can be tuned to an optimal value of |ΔGH∗|≤ 0.2 eV through strain engineering, significantly enhancing the HER electrocatalytic activity of BeN4 monolayer. Furthermore, we examined both the homolytic Tafel reaction and heterolytic Heyrovsky reaction for HER mechanism using reaction kinetics and AIMD simulations. These findings can contribute to the development of high-performance, non-precious, and nitrogen-rich 2D catalysts for HER in future research. Monolayer ; Materials science ; Graphene ; Tafel equation ; Dirac (video compression format) ; Heterolysis ; Chemical physics ; Catalysis ; Vacancy defect ; Electrocatalyst ; Nanotechnology ; Physical chemistry ; Chemistry ; Crystallography ; Electrode ; Electrochemistry ; Organic chemistry ; Physics MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Muhammad Usman Sadiq | Afzal Shah | Jan Nisar | Iltaf Shah
Source: Nanomaterials (article, 2023-07-30)
Field: Chemistry — Electrochemistry

Photoelectrocatalytic Detection and Degradation Studies of a Hazardous Textile Dye Safranin T Herein, we report an electrochemical scaffold consisting of functionalized multiwalled carbon nanotubes (COOH-<i>f</i>MWCNTs) and iron-doped zinc oxide nanoparticles (Fe-ZnO) for the detection of a hazardous textile dye safranin T (ST) and monitoring of its photocatalytic degradation. Prior to the detection and degradation analysis, Fe-ZnO NPs were synthesized by the sol-gel method and characterized by a number of structural and morphological techniques. The carboxyl moiety of COOH-<i>f</i>MWCNTs possessing a strong affinity for the amino functionality of ST led to significant enhancement of the current response at the designed electrochemical platform, whereas the electrocatalytic role, surface area enhancement, and the provision of binding sites of Fe-ZnO led to a further increase in the peak current intensity of ST. Electrochemical impedance spectroscopy showed that the sensing scaffold made of the glassy carbon electrode modified with COOH-<i>f</i>MWCNTs and Fe-ZnO efficiently transfers charge between the transducer and the redox probe. Under optimized conditions, the developed sensor showed a 2.3 nM limit of detection for ST. Moreover, recovery experiments and anti-interference tests qualified the sensing platform for practical applications. The dye was photocatalytically degraded using Fe-ZnO NPs up to 99% in 60 min with a rate constant of 0.068 min<sup>-1</sup>. The designed sensor was used to probe the degradation kinetics of the target dye, and the results were found consistent with the findings obtained from electronic absorption method. To the best of our knowledge, the present work is the first approach for the efficient detection and almost absolute degradation of ST. Materials science ; Detection limit ; Dielectric spectroscopy ; Safranin ; Electrochemistry ; Electrode ; Degradation (telecommunications) ; Chemical engineering ; Nuclear chemistry ; Chemistry ; Chromatography Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Dye analysis and toxicity
Authors: Vinson Liao | Maximilian Cohen | Yifan Wang | Dionisios G. Vlachos
Source: Nature Communications (article, 2023-04-08)
Field: Materials Science — Materials Chemistry

Deducing subnanometer cluster size and shape distributions of heterogeneous supported catalysts Infrared (IR) spectra of adsorbate vibrational modes are sensitive to adsorbate/metal interactions, accurate, and easily obtainable in-situ or operando. While they are the gold standards for characterizing single-crystals and large nanoparticles, analogous spectra for highly dispersed heterogeneous catalysts consisting of single-atoms and ultra-small clusters are lacking. Here, we combine data-based approaches with physics-driven surrogate models to generate synthetic IR spectra from first-principles. We bypass the vast combinatorial space of clusters by determining viable, low-energy structures using machine-learned Hamiltonians, genetic algorithm optimization, and grand canonical Monte Carlo calculations. We obtain first-principles vibrations on this tractable ensemble and generate single-cluster primary spectra analogous to pure component gas-phase IR spectra. With such spectra as standards, we predict cluster size distributions from computational and experimental data, demonstrated in the case of CO adsorption on Pd/CeO<sub>2</sub>(111) catalysts, and quantify uncertainty using Bayesian Inference. We discuss extensions for characterizing complex materials towards closing the materials gap. Cluster (spacecraft) ; Catalysis ; Cluster size ; Heterogeneous catalysis ; Materials science ; Nanotechnology ; Chemical physics ; Chemistry ; Computational chemistry ; Molecular dynamics ; Computer science ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Yiannis Georgantas | Francis Peter Moissinac | Mark A. Bissett
Source: Graphene and 2D Materials (article, 2023-12-06)
Field: Materials Science — Materials Chemistry

MXenes-mining: a decade of discovery Abstract MXenes are a material family of transition metal carbides and/or nitrides that include more than 48 different chemical compounds, with different synthesis and post-processing routes, enabling them to be exploited in a variety of applications. Consequentially, the permutability of such materials has led to the publication of several thousand articles that either mention MXenes or specifically focus on them. This results in a common challenge shared by many large research domains, where researchers often face significant obstacles in obtaining a swift and all-encompassing macro-perspective. Herein, we have utilized data mining tool to gather 25.355 articles from the inception of MXenes in 2011 up until 31 December 2022. Accordingly, we processed the collated data by using clustering, link analysis and frequent itemset analysis in a variety of ways to visualize the results in a range of plots and figures to ascertain trends and correlations to identify the gaps that this research domain may have for future investigations. MXenes ; Variety (cybernetics) ; Data science ; Computer science ; Cluster analysis ; Macro ; Perspective (graphical) ; Domain (mathematical analysis) ; Data mining ; Nanotechnology ; Artificial intelligence ; Materials science ; Mathematics MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices