jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yi-Shen Lin | G. P. Purja Pun | Y. Mishin
Source: Computational Materials Science (article, 2022-01-22)
Field: Materials Science — Materials Chemistry

Development of a physically-informed neural network interatomic potential for tantalum Interatomic potential ; Extrapolation ; Embedded atom model ; Tantalum ; Molecular dynamics ; Interpolation (computer graphics) ; Atomic units ; Lattice (music) ; Statistical physics ; Materials science ; Physics ; Chemistry ; Computational chemistry ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; X-ray Diffraction in Crystallography
Authors: Verena Streibel | Hassan Aljama | An‐Chih Yang | Tej S. Choksi | Roel S. Sánchez‐Carrera | Ansgar Schäfer | Yuejin Li | Matteo Cargnello | Frank Abild‐Pedersen
Source: ACS Catalysis (article, 2022-01-18)
Field: Materials Science — Materials Chemistry

Microkinetic Modeling of Propene Combustion on a Stepped, Metallic Palladium Surface and the Importance of Oxygen Coverage Better emission control of internal combustion engines can be achieved by more efficient catalytic hydrocarbon combustion at low temperatures. For a knowledge-based design of suitable catalyst candidates, a promising approach integrates theoretical models with experimental benchmarks. Studying catalytic hydrocarbon combustion theoretically is challenging, however, since hydrocarbon reaction networks are complex: even simple C2 combustion reactions include hundreds of possible elementary steps. Herein, we present a paradigm to address this challenge by (1) supplementing experimental insights with extensive density functional theory studies to derive a proxy combustion reaction network; (2) using machine learning-enhanced transition state search to rigorously scan reaction coordinates; and (3) combining ab initio thermodynamics, microkinetic modeling, and a degree of rate control analysis to unveil adsorbate coverage effects and rate-limiting reaction steps. Our systematic approach permits modeling a reaction as complex as propene combustion on palladium while maintaining the computationally efficient mean-field approximation. Using a partially oxygen-covered palladium surface model, which includes adsorbate–adsorbate interactions, we predict experimentally measured rates within 2 orders of magnitude without parameter fitting. The corresponding degree of rate control analysis yields that the second and penultimate dehydrogenation step in the reaction network limit the rate of propene combustion on palladium. Propene ; Combustion ; Chemistry ; Palladium ; Catalysis ; Hydrocarbon ; Dehydrogenation ; Ab initio ; Reaction rate ; Density functional theory ; Catalytic combustion ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Physics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Weike Ye | Hui Zheng | Chi Chen | Shyue Ping Ong
Source: Scripta Materialia (article, 2022-05-13)
Field: Materials Science — Materials Chemistry

A Universal Machine Learning Model for Elemental Grain Boundary Energies The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric features. By machine learning on a large computed database of 361 small Σ (Σ<10) GBs of more than 50 metals, we develop a model that can predict the grain boundary energies to within a mean absolute error of 0.13 J m−2 . More importantly, this universal GB energy model can be extrapolated to the energies of high Σ GBs without loss in accuracy. These results highlight the importance of capturing fundamental scaling physics and domain knowledge in the design of interpretable, extrapolatable machine learning models for materials science. Grain boundary ; Materials science ; Crystallite ; Scaling ; Boundary (topology) ; Energy (signal processing) ; Domain (mathematical analysis) ; Statistical physics ; Artificial intelligence ; Condensed matter physics ; Machine learning ; Metallurgy ; Computer science ; Physics ; Geometry ; Mathematical analysis ; Mathematics ; Statistics ; Microstructure Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Computational Drug Discovery Methods
Authors: Alan M. Bond | Jie Zhang | Luke Gundry | Gareth F. Kennedy
Source: Current Opinion in Electrochemistry (article, 2022-04-09)
Field: Chemistry — Electrochemistry

Opportunities and challenges in applying machine learning to voltammetric mechanistic studies Computer science ; Mechanism (biology) ; Field (mathematics) ; Machine learning ; Artificial intelligence ; Range (aeronautics) ; Transfer of learning ; Data science ; Engineering Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Euclides Lourenço Chuma | Yuzo Iano
Source: 2022 IEEE Sensors (article, 2022-10-30)
Field: Chemistry — Analytical Chemistry

Novelty Sensor using Integrated Fluorescence and Dielectric Spectroscopy to Improve Food Quality Identification Sensors are essential components of any modern device, and currently, any modern device has many types of sensors for several applications. However, there is a trade-off between cost and accuracy in sensors, and this frontier should be overcome with the use of new sensor types and algorithms for data processing. This study presents a novelty sensor by combining fluorescence and microwave dielectric spectroscopy into the same physical structure to improve food quality identification. The proposed sensor incorporates a planar circular complementary split-ring resonator for the dielectric measurement and an optical path to fluorescence spectroscopy into the same physical structure to make measurements simultaneously. The proposed sensor was tested to identify adulterated honey. Using the data from fluorescence spectroscopy and microwave dielectric spectroscopy, a greater diversity of data from the electromagnetic spectrum made it possible to improve the accuracy of honey adulteration identification while using fewer samples to train machine learning classification algorithms. The proposed sensor was tested with a low-cost Vis-NIR sensor; it achieved an accuracy of 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> using 20 training samples only with the Bilayered neural network classification algorithm. Therefore, the proposed sensor paves the path for material characterization and identification by combining several spectroscopy techniques in the same structure. Fluorescence spectroscopy ; Spectroscopy ; Computer science ; Dielectric ; Sensor fusion ; Identification (biology) ; Artificial intelligence ; Electronic engineering ; Materials science ; Biological system ; Optoelectronics ; Fluorescence ; Engineering ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Carlo Cavallotti
Source: Proceedings of the Combustion Institute (article, 2022-07-12)
Field: Materials Science — Materials Chemistry

Automation of chemical kinetics: Status and challenges Reaction rate constant ; Thermochemistry ; Automation ; Computer science ; Field (mathematics) ; Biochemical engineering ; Reaction rate ; Abstraction ; Chemistry ; Computational chemistry ; Physical chemistry ; Kinetics ; Mathematics ; Physics ; Mechanical engineering ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Chemical Thermodynamics and Molecular Structure
Authors: Jun Li | Qi An
Source: Journal of the European Ceramic Society (article, 2022-10-12)
Field: Materials Science — Materials Chemistry

Quasiplastic deformation in shocked nanocrystalline boron carbide: Grain boundary sliding and local amorphization Materials science ; Boron carbide ; Grain Boundary Sliding ; Nanocrystalline material ; Deformation mechanism ; Grain boundary ; Deformation (meteorology) ; Intergranular corrosion ; Ceramic ; Shock (circulatory) ; Carbide ; Composite material ; Metallurgy ; Microstructure ; Nanotechnology Diamond and Carbon-based Materials Research ; Boron and Carbon Nanomaterials Research ; High-pressure geophysics and materials
Authors: Minggao Feng | Min Cheng | Xu Ji | Li Zhou | Yagu Dang | Kexin Bi | Zhongde Dai | Yiyang Dai
Source: Separation and Purification Technology (article, 2022-09-15)
Field: Chemistry — Inorganic Chemistry

Finding the optimal CO2 adsorption material: Prediction of multi-properties of metal-organic frameworks (MOFs) based on DeepFM Metal-organic framework ; Adsorption ; Porosity ; Artificial neural network ; Materials science ; Matrix (chemical analysis) ; Computer science ; Process engineering ; Artificial intelligence ; Chemistry ; Engineering ; Organic chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: Srinivas Soumitri Miriyala | Keerthi NagaSree Pujari | Sakshi Naik | Kishalay Mitra
Source: Powder Technology (article, 2022-05-20)
Field: Materials Science — Materials Chemistry

Evolutionary neural architecture search for surrogate models to enable optimization of industrial continuous crystallization process Artificial neural network ; Heuristics ; Process (computing) ; Differential evolution ; Surrogate model ; Computer science ; Heuristic ; Population ; Mathematical optimization ; Artificial intelligence ; Machine learning ; Mathematics Crystallization and Solubility Studies ; Advanced Multi-Objective Optimization Algorithms ; Optimal Experimental Design Methods
Authors: Yongtao Liu | Kyle P. Kelley | Hiroshi Funakubo | Sergei V. Kalinin | Maxim Ziatdinov
Source: Advanced Science (article, 2022-09-05)
Field: Materials Science — Materials Chemistry

Exploring Physics of Ferroelectric Domain Walls in Real Time: Deep Learning Enabled Scanning Probe Microscopy The functionality of ferroelastic domain walls in ferroelectric materials is explored in real-time via the in situ implementation of computer vision algorithms in scanning probe microscopy (SPM) experiment. The robust deep convolutional neural network (DCNN) is implemented based on a deep residual learning framework (Res) and holistically nested edge detection (Hed), and ensembled to minimize the out-of-distribution drift effects. The DCNN is implemented for real-time operations on SPM, converting the data stream into the semantically segmented image of domain walls and the corresponding uncertainty. Further the pre-defined experimental workflows perform piezoresponse spectroscopy measurement on thus discovered domain walls, and alternating high- and low-polarization dynamic (out-of-plane) ferroelastic domain walls in a PbTiO<sub>3</sub> (PTO) thin film and high polarization dynamic (out-of-plane) at short ferroelastic walls (compared with long ferroelastic walls) in a lead zirconate titanate (PZT) thin film is reported. This work establishes the framework for real-time DCNN analysis of data streams in scanning probe and other microscopies and highlights the role of out-of-distribution effects and strategies to ameliorate them in real time analytics. Ferroelectricity ; Piezoresponse force microscopy ; Lead zirconate titanate ; Polarization (electrochemistry) ; Materials science ; Convolutional neural network ; Domain (mathematical analysis) ; Computer science ; Artificial intelligence ; Optics ; Optoelectronics ; Physics ; Chemistry ; Dielectric ; Mathematics Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: Bohayra Mortazavi | Xiaoying Zhuang
Source: Coatings (article, 2022-08-12)
Field: Materials Science — Materials Chemistry

Low and Anisotropic Tensile Strength and Thermal Conductivity in the Single-Layer Fullerene Network Predicted by Machine-Learning Interatomic Potentials In the latest ground-breaking experimental advancement (Nature (2022), 606, 507), zero-dimensional fullerenes (C60) have been covalently bonded to form single-layer two-dimensional (2D) fullerene network, namely quasi-hexagonal-phase fullerene (qHPC60). Motivated by the aforementioned accomplishment, in this communication, for the first time, we explore the phononic and mechanical properties of the qHPC60 monolayer, employing state-of-the-art machine-learning interatomic potentials. By employing an efficient passive-training methodology, the thermal and mechanical properties were examined with an ab-initio level of accuracy using the classical molecular dynamics simulations. Predicted phonon dispersion confirmed the desirable dynamical stability of the qHPC60 monolayer. Room temperature lattice thermal conductivity is predicted to be ultralow and around 2.9 (5.7) W/m·K along the x(y) directions, which are by three orders of magnitude lower than that of the graphene. Close to the ground state and at room temperature, the ultimate tensile strength of the qHPC60 monolayer along the x(y) directions is predicted to be 7.0 (8.8) and 3.3 (4.2) GPa, respectively, occurring at corresponding strains of around 0.07 and 0.029, respectively. The presented computationally accelerated first-principles results confirm highly anisotropic and remarkably low tensile strength and phononic thermal conductivity of the qHPC60 fullerene network nanosheets. Materials science ; Phonon ; Anisotropy ; Thermal conductivity ; Ultimate tensile strength ; Monolayer ; Fullerene ; Interatomic potential ; Graphene ; Condensed matter physics ; Molecular dynamics ; Composite material ; Nanotechnology ; Computational chemistry ; Optics ; Physics ; Chemistry Thermal properties of materials ; Graphene research and applications ; Fullerene Chemistry and Applications
Authors: Yichao Bai | Linxuan Sun | Qiangmin Yu | Yu Lei | Bilu Liu
Source: Nano Research Energy (article, 2022-11-14)
Field: Materials Science — Materials Chemistry

Biomolecule capturing and sensing on 2D transition metal dichalcogenide canvas Since the isolation of graphene in 2004, two-dimensional (2D) materials such as transition metal dichalcogenide (TMD) have attracted numerous interests due to their unique van der Waals structure, atomically thin body, and thickness-dependent properties. In recent years, the applications of TMD in public health have emerged due to their large surface area and high surface sensitivities, as well as their unique electrical, optical, and electrochemical properties. In this review, we focus on state-of-the-art methods to modulate the properties of 2D TMD and their applications in biosensing. Particularly, this review provides methods for designing and modulating 2D TMD via defect engineering and morphology control to achieve multi-functional surfaces for molecule capturing and sensing. Furthermore, we compare the 2D TMD-based biosensors with the traditional sensing systems, deepening our understanding of their action mechanism. Finally, we point out the challenges and opportunities of 2D TMD in this emerging area. Nanotechnology ; Graphene ; Biosensor ; Biomolecule ; Materials science ; van der Waals force ; Transition metal ; Computer science ; Molecule ; Chemistry 2D Materials and Applications ; Advanced biosensing and bioanalysis techniques ; MXene and MAX Phase Materials
Authors: Yen Dang | Aoife Power | Daniel Cozzolino | Khuong Ba Dinh | Binh Son Ha | Adam Kolobaric | Jitraporn Vongsvivut | Vi Khanh Truong | James Chapman
Source: Journal of Bio- and Tribo-Corrosion (article, 2022-03-06)
Field: Materials Science — Materials Chemistry

Analytical Characterisation of Material Corrosion by Biofilms Abstract Almost every abiotic surface of a material is readily colonised by bacteria, algae, and fungi, contributing to the degradation processes of materials. Both biocorrosion and microbially influenced corrosion (MIC) refer to the interaction of microbial cells and their metabolic products, such as exopolymeric substances (EPS), with an abiotic surface. Therefore, biofouling and biodeterioration of manufactured goods have economic and environmental ramifications for the user to tackle or remove the issue. While MIC is typically applied to metallic materials, newly developed and evolving materials frequently succumb to the effects of corrosion, resulting in a range of chemical reactions and transport mechanisms occurring in the material. Recent research on biocorrosion and biofouling of conventional and novel materials is discussed in this paper, showcasing the current knowledge regarding microbial and material interactions that contribute to biocorrosion and biofouling, including biofilms, anaerobic and aerobic environments, microbial assault, and the various roles microorganisms’ play. Additionally, we show the latest analytical techniques used to characterise and identify MIC on materials using a borescope, thermal imaging, Fourier transform infrared (FTIR), atomic force microscopy (AFM), scanning electron microscopy (SEM), X-ray photoelectron microscopy (XPS), X-ray diffraction (XRD), optical and epifluorescence microscopy, electrochemical impedance spectroscopy, and mass spectrometry, and chemometrics. Biofouling ; Biofilm ; Corrosion ; X-ray photoelectron spectroscopy ; Materials science ; Extracellular polymeric substance ; Fourier transform infrared spectroscopy ; Environmental chemistry ; Microbial fuel cell ; Scanning electron microscope ; Nanotechnology ; Chemical engineering ; Chemistry ; Metallurgy ; Composite material ; Bacteria Corrosion Behavior and Inhibition ; Bacterial biofilms and quorum sensing ; Marine Biology and Environmental Chemistry
Authors: Xiao Kong | Chengrui Fu | Vladislav Gladkikh | Feng Ding
Source: SmartMat (article, 2022-10-20)
Field: Materials Science — Materials Chemistry

The shapes of synthesized two‐dimensional materials Abstract The shapes of as‐grown/as‐etched two‐dimensional (2D) islands are the most recognizable features of the chemical vapor deposition (CVD) process which reveal a lot of information about the chemical environment of growth/etching, the effect of the substrate and the growth/etching kinetics. Here, we summarize experimentally observed shapes of CVD‐grown 2D materials from previous reports, theoretical understanding on the corresponding mechanisms and the simulations of the corresponding growth/etching processes. Each part of the review article is organized by combining the fundamental principles that govern the shapes of crystals during growth/etching and the related experimental observations to facilitate the readers to read and understand the content. The content of the review includes attachment‐limited 2D materials growth, diffusion‐limited 2D materials growth (both with and without influence of the substrate), etching and merging of 2D materials with and without considering the effect of the substrate. We also discuss growth and etching of multilayered 2D islands during CVD—a topic still under development. We wish this review will also guide the audience to understand the kinetics of 2D materials growth and to improve controllable synthesis of various 2D materials. Etching (microfabrication) ; Substrate (aquarium) ; Chemical vapor deposition ; Nanotechnology ; Materials science ; Isotropic etching ; Diffusion ; Kinetics ; Deposition (geology) ; Engineering physics ; Geology ; Engineering ; Physics ; Thermodynamics ; Layer (electronics) 2D Materials and Applications ; Graphene research and applications ; Ga2O3 and related materials
Authors: Dennis T. Lee | Peter Corkery | Sunghwan Park | Hae‐Kwon Jeong | Michael Tsapatsis
Source: Annual Review of Chemical and Biomolecular Engineering (review, 2022-04-13)
Field: Chemistry — Inorganic Chemistry

Zeolitic Imidazolate Framework Membranes: Novel Synthesis Methods and Progress Toward Industrial Use In the last decade, zeolitic imidazolate frameworks (ZIFs) have been studied extensively for their potential as selective separation membranes. In this review, we highlight unique structural properties of ZIFs that allow them to achieve certain important separations, like that of propylene from propane, and summarize the state of the art in ZIF thin-film deposition on porous substrates and their modification by postsynthesis treatments. We also review the reported membrane performance for representative membrane synthesis approaches and attempt to rank the synthesis methods with respect to potential for scalability. To compare the dependence of membrane performance on membrane synthesis methods and operating conditions, we map out fluxes and separation factors of selected ZIF-8 membranes for propylene/propane separation. Finally, we provide future directions considering the importance of further improvements in scalability, cost effectiveness, and stable performance under industrially relevant conditions. Zeolitic imidazolate framework ; Membrane ; Propane ; Scalability ; Imidazolate ; Materials science ; Nanotechnology ; Computer science ; Biochemical engineering ; Chemical engineering ; Metal-organic framework ; Chemistry ; Engineering ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Covalent Organic Framework Applications
Authors: Shiyue Yang | Graeme M. Day
Source: Communications Chemistry (article, 2022-07-28)
Field: Materials Science — Materials Chemistry

Global analysis of the energy landscapes of molecular crystal structures by applying the threshold algorithm Polymorphism in molecular crystals has important consequences for the control of materials properties and our understanding of crystallization. Computational methods, including crystal structure prediction, have provided important insight into polymorphism, but have usually been limited to assessing the relative energies of structures. We describe the implementation of the Monte Carlo threshold algorithm as a method to provide an estimate of the energy barriers separating crystal structures. By sampling the local energy minima accessible from multiple starting structures, the simulations yield a global picture of the crystal energy landscapes and provide valuable information on the depth of the energy minima associated with crystal structures. We present results from applying the threshold algorithm to four polymorphic organic molecular crystals, examine the influence of applying space group symmetry constraints during the simulations, and discuss the relationship between the structure of the energy landscape and the intermolecular interactions present in the crystals. Algorithm ; Energy (signal processing) ; Computer science ; Mathematics ; Statistics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Chemical Thermodynamics and Molecular Structure