jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chaofang Dong | Yucheng Ji | Xin Wei | Dihao Chen | Li Ni | Aoni Xu | Decheng Kong | Wang Li | Menglin Li | Zhanming Cheng | Kui Xiao | Xiaogang Li
Source:  (article, 2021-05-06)
Field: Materials Science — Materials Chemistry

The development, validity and applicability of integrated computational corrosion In the last decade, integrated computation of corrosion has made significant progress towards the atomic-scale clarifying of corrosion mechanism and the computer-aided designing of advanced materials with excellent corrosion resistance. This presentation focuses on of theoretical calculation methods and developing tendency in the corrosion study, and three specific applications are presented. First-principle technique combined with molecular dynamics method, peridynamic theory and finite element method provide multiscale models to investigate the micro-mechanism of stress corrosion cracking and hydrogen-induced cracking. The calculation of passivity and passive film breakdown are discoursed elaborately through the point defects diffusion and its correlation of the energy level degeneracy. By surveying the publications, the artificial intelligence technology is pointed out how the computer can pave the way of predicting the corrosion degree as well as designing new corrosion resistant materials. To get better and efficient development of integrated computation of corrosion, extensive cooperation and powerful data infrastructure are needed by stronger joint efforts in the future. Corrosion ; Computation ; Stress corrosion cracking ; Mechanism (biology) ; Computer science ; Cracking ; Materials science ; Scale (ratio) ; Metallurgy ; Algorithm ; Composite material ; Physics Corrosion Behavior and Inhibition ; Material Properties and Failure Mechanisms ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Daniel Schwalbe‐Koda | Avelino Corma | Yuriy Román‐Leshkov | Manuel Moliner | Rafael Gómez‐Bombarelli
Source:  (preprint, 2021-09-24)
Field: Chemistry — Inorganic Chemistry

Data-driven design of bi-selective OSDAs for intergrowth zeolites Zeolites are inorganic materials with wide industrial applications due to their topological diversity. Tailoring confinement effects in zeolite pores, for instance by crystallizing intergrown frameworks, can improve their catalytic and transport properties, but controlling zeolite crystallization often relies on heuristics. In this work, we use computational simulations and data mining to design organic structure-directing agents (OSDAs) to favor the synthesis of intergrown zeolites. First, we propose design principles to identify OSDAs which are selective towards both end members of the disordered structure. Then, we mine a database of hundreds of thousands of zeolite-OSDA pairs and downselect OSDA candidates to synthesize known intergrowth zeolites such as CHA/AFX, MTT/TON, and BEC/ISV. The computationally designed OSDAs balance phase competition metrics and shape selectivity towards the frameworks, thus bypassing expensive dual-OSDA approaches typically used in the synthesis of intergrowths. Finally, we propose potential OSDAs to obtain hypothesized disordered frameworks such as AEI/SAV. This work may accelerate zeolite discovery through data-driven synthesis optimization and design. Zeolite ; Crystallization ; Heuristics ; Materials science ; Nanotechnology ; Computer science ; Chemical engineering ; Catalysis ; Engineering ; Chemistry Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications
Authors: Renay Su | Joseph Yu | Steven Chiao-Chien Wei
Source:  (article, 2021-12-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Novel Electrolytic Plating Solution for RDL in Advanced Packaging Applications With the emerging development of high-tech applications such as consumer electronics, 5G, artificial intelligence, and internet of vehicles, the need for IC chips featuring higher functionality, performance, and reliability but smaller volume is pushing close to the limit of Moore's law. To keep extending the Moore's law, besides development of novel nano technology for transistors, technology in advanced packaging in 2.5D and 3D are evolving rapidly. Introduction of redistribution layer (RDL) in an integrated fan-out (InFO) package allows better integration capability and lower cost. Copper electrolytic plating is one of the important steps in the fabrication of RDL process. RDL features blind vias with diameter smaller than 50 um and fine lines with pitch ranging from 50 um to 20 um or even smaller. Both via-filling capability and uniformity have remained two major key challenges for this application. Proprietary design of organic additives provides good via-filling performance down to 30 um vias without voids. To improve uniformity, simulation on COMSOL Multiphysics predicts difference of electric field distribution from ground surface to unit edge. Along with the simulation of mass-transfer mechanism in fine-line areas, novel selection of organic additives demonstrates differential adsorption along the electric field, creating differential suppression levels on different features, leading to achieve superior uniformity within unit with various pattern features. Besides extraordinary via filling and uniformity performance, a wide range of operating conditions and stable reliability offers key features for easy bath maintenance with this unconventional electrolytic plating solution. In summary, this paper demonstrates an innovative electroplating solution with superior uniformity for advanced RDL applications. Fabrication ; Multiphysics ; Electronics ; Electronic packaging ; Materials science ; Computer science ; Mechanical engineering ; Electrical engineering ; Engineering ; Finite element method Copper Interconnects and Reliability ; Electrodeposition and Electroless Coatings ; Electronic Packaging and Soldering Technologies
Authors: Tung-Huan Su | Nian-Hu Lu | Chih‐Hsuan Chen | Chuin‐Shan Chen
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Degradation of Transformation Stress in a Bicrystal Cu-Al-Mn Shape-Memory Alloy Using Full-Field Stress and Strain Measurements Materials science ; Stress (linguistics) ; Metallurgy ; Alloy ; Degradation (telecommunications) ; Shape-memory alloy ; Strain (injury) ; Stress field ; Transformation (genetics) ; Structural engineering ; Finite element method ; Chemistry ; Electrical engineering ; Engineering Shape Memory Alloy Transformations ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Jonathan J. Heath | Marcelo A. Kuroda
Source: Physical Review Applied (article, 2021-10-15)
Field: Materials Science — Materials Chemistry

Spin-Injection Enhancements in van der Waals Magnetic Tunnel Junctions through Barrier Engineering Magnetic tunnel junctions (MTJs) composed of different two-dimensional (2D) materials are promising for spin-valve applications. However, as the number of material combinations grows, guiding principles for heterostructure composition-property relationships become very desirable. The authors use first-principles calculations to provide design guidelines for spin-dependent transport in MTJs, based on the bulk properties of the constituent 2D materials, and find that low-work-function electrodes enable greater performance with CrI${}_{3}$ channels. This data-driven scheme offers a fast, systematic method to accelerate the design of spintronic devices based on 2D materials. Spintronics ; van der Waals force ; Heterojunction ; Materials science ; Spin (aerodynamics) ; Work (physics) ; Condensed matter physics ; Tunnel magnetoresistance ; Spin valve ; Tunnel junction ; Nanotechnology ; Engineering physics ; Optoelectronics ; Ferromagnetism ; Magnetic field ; Magnetoresistance ; Physics ; Quantum tunnelling ; Mechanical engineering ; Engineering ; Quantum mechanics ; Layer (electronics) 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Graphene research and applications
Authors: Aaron E. Tallman | Reeju Pokharel | Darshan Bamney | Douglas E. Spearot | B. Clausen | Ricardo A. Lebensohn | Donald W. Brown | Laurent Capolungo
Source: Research Square (preprint, 2021-09-02)
Field: Materials Science — Materials Chemistry

Data-Driven Analysis of Neutron Diffraction Line Profiles: Application to Plastically Deformed Ta Abstract Non-destructive evaluation of plastically deformed metals, particularly diffraction line profile analysis (DLPA), is valuable both to estimate dislocation densities and arrangements and to validate microstructure-aware constitutive models. To date, the interpretation of whole line diffraction profiles relies on the use of semi-analytical models such as the extended convolutional multiple whole profile (eCMWP) method. This study introduces and validates two data-driven DLPA models to extract dislocation densities from experimentally gathered whole line diffraction profiles. Using two distinct virtual diffraction models accounting for both strain and instrument induced broadening, a database of virtual diffraction whole line profiles of Ta single crystals is generated using discrete dislocation dynamics. The databases are mined to create Gaussian process regression-based surrogate models, allowing dislocation densities to be extracted from experimental profiles. The method is validated against 11 experimentally gathered whole line diffraction profiles from plastically deformed Ta polycrystals. The newly proposed model predicts dislocation densities consistent with estimates from eCMWP. Advantageously, this data driven LPA model can distinguish broadening originating from the instrument and from the dislocation content even at low dislocation densities. Finally, the data-driven model is used to explore the effect of heterogeneous dislocation densities in microstructures containing grains, which may lead to more accurate data-driven predictions of dislocation density in plastically deformed polycrystals. Dislocation ; Diffraction ; Materials science ; Line (geometry) ; Neutron diffraction ; X-ray crystallography ; Crystallography ; Optics ; Physics ; Composite material ; Chemistry ; Geometry ; Mathematics Microstructure and mechanical properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Tianqi Deng | Jose Recatala‐Gomez | Masato Ohnishi | D. V. Maheshwar Repaka | Pawan Kumar | Ady Suwardi | Anas Abutaha | Iris Nandhakumar | Kanishka Biswas | Michael B. Sullivan | Gang Wu | Junichiro Shiomi | Shuo‐Wang Yang | Kedar Hippalgaonkar
Source: arXiv (Cornell University) (preprint, 2021-01-09)
Field: Materials Science — Materials Chemistry

Electronic transport descriptors for the rapid screening of thermoelectric materials The discovery of novel materials for thermoelectric energy conversion has potential to be accelerated by data-driven screening combined with high-throughput calculations. One way to increase the efficacy of successfully choosing a candidate material is through its evaluation using transport descriptors. Using a data-driven screening, we selected 12 potential candidates in the trigonal ABX2 family, followed by charge transport property simulations from first principles. The results suggest that carrier scattering processes in these materials are dominated by ionised impurities and polar optical phonons, contrary to the oft-assumed acoustic-phonon-dominated scattering. Combined with calculations of thermal conductivity based on three-phonon scattering, we predict p-type AgBiS2 and TlBiTe2 as potential high-performance thermoelectrics in the intermediate temperature range for low grade waste heat harvesting, with a predicted zT above 1 at 500 K. Using these data, we further derive ground-state transport descriptors for the carrier mobility and the thermoelectric power factor. In addition to low carrier mass, high dielectric constant was found to be an important factor towards high carrier mobility. A quadratic correlation between dielectric constant and transport performance was established and further validated with literature. Looking ahead, dielectric constant can potentially be exploited as an independent tuning knob for improving the thermoelectric performance. Thermoelectric materials ; Thermoelectric effect ; Dielectric ; Electron mobility ; Phonon ; Materials science ; Charge carrier ; Scattering ; Condensed matter physics ; Phonon scattering ; Thermal conductivity ; Effective mass (spring–mass system) ; Seebeck coefficient ; Optoelectronics ; Thermodynamics ; Physics ; Optics ; Composite material Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Simon Reese | Robert Eggersmann | E. Prume | Laurent Stainier | Miguel Ortiz
Source:  (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Efficient data structures for data-driven mechanics The data-driven computing paradigm initially introduced by Kirchdoerfer &amp; Ortiz (2016)&nbsp;enables finite element computations in solid mechanics to be performed directly from material&nbsp;data sets. From the point of view of computational effort, the most challenging task is the&nbsp;projection of admissible states at material points onto their closest states in the material data&nbsp;set. In this study, we compare and develop several possible data structures for solving this&nbsp;nearest-neighbor problem. We show that approximate nearest-neighbor algorithms can&nbsp;accelerate material data searches by several orders of magnitude relative to exact searching&nbsp;algorithms. The approximations are suggested by&mdash;and adapted to&mdash;the structure of the datadriven&nbsp;iterative solver and result in no significant loss of solution accuracy. The performance&nbsp;of the nearest-neighboralgorithmsare assessed with respect to material data set size at the&nbsp;example of 3D elasticity and elasto-plasticity test cases. We show that computations including&nbsp;up to one billion material data points on a single processor are feasible within a few seconds&nbsp;execution time. Solver ; Computation ; Computer science ; Computational science ; k-nearest neighbors algorithm ; Finite element method ; Set (abstract data type) ; Projection (relational algebra) ; Data structure ; Data point ; Data set ; Algorithm ; Theoretical computer science ; Artificial intelligence ; Engineering ; Structural engineering Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Advanced Neural Network Applications
Authors: Jinbo Pan | Yanfang Zhang | Jingda Zhang | Huta Banjade | Jie Yu | Liping Yu | Shixuan Du | Adrienn Ruzsinszky | Zhenpeng Hu | Qimin Yan
Source: Bulletin of the American Physical Society (article, 2021-03-15)
Field: Materials Science — Materials Chemistry

Auxetic two-dimensional transition metal selenides and halides Auxetic two-dimensional (2D) materials provide a promising platform for biomedicine, sensors, and many other applications at the nanoscale. In this work, utilizing a hypothesis-based data-driven approache, we identify multiple materials with remarkable in-plane auxetic behavior in a family of buckled monolayer 2D materials. These materials are transition metal selenides and transition metal halides with the stoichiometry MX (M = V, Cr, Mn, Fe, Co, Cu, Zn, Ag, and X = Se, Cl, Br, I). First-principles calculations reveal that the desirable auxetic behavior of these 2D compounds originates from the interplay between the buckled 2D structure and the weak metal–metal interaction determined by their electronic structures. We observe that the Poisson’s ratio is sensitive to magnetic order and the amount of uniaxial stress applied. A transition from positive Poisson’s ratio (PPR) to negative Poisson’s ratio (NPR) for a subgroup of MX compounds under large uniaxial stress is predicted. The work provides a guideline for the future design of 2D auxetic materials at the nanoscale. Auxetics ; Materials science ; Monolayer ; Transition metal ; Halide ; Nanoscopic scale ; Stoichiometry ; Poisson's ratio ; Metal ; Condensed matter physics ; Poisson distribution ; Nanotechnology ; Composite material ; Chemistry ; Inorganic chemistry ; Physics ; Physical chemistry ; Metallurgy ; Mathematics MXene and MAX Phase Materials ; Cellular and Composite Structures ; Bone Tissue Engineering Materials
Authors: Ling Gu | Chen Ji | Wanting Ji
Source: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (article, 2021-10-23)
Field: Materials Science — General Materials Science

The Development of a Kind of Composite Material Lightweight Airborne Equipment Bracket research-article The Development of a Kind of Composite Material Lightweight Airborne Equipment Bracket Share on Authors: Ling Gu Cetc Wuhu Diamond Aircraft Manufacture Co., Ltd, China Cetc Wuhu Diamond Aircraft Manufacture Co., Ltd, ChinaSearch about this author , Chen Ji Cetc Wuhu Diamond Aircraft Manufacture Co., Ltd, China Cetc Wuhu Diamond Aircraft Manufacture Co., Ltd, ChinaSearch about this author , Wanting Ji Cetc Wuhu Diamond Aircraft Manufacture Co., Ltd, China Cetc Wuhu Diamond Aircraft Manufacture Co., Ltd, ChinaSearch about this author Authors Info & Claims AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureOctober 2021 Pages 3030–3032https://doi.org/10.1145/3495018.3501228Online:14 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access China ; Diamond ; Citation ; Aeronautics ; Engineering ; Computer science ; Manufacturing engineering ; World Wide Web ; Materials science ; History ; Metallurgy ; Archaeology Material Properties and Applications
Authors: JSC “UEC-Aviadvigatel” | Александр Козлов | Farit Mukhutdinov | Aleksei Sazhenkov
Source: Perm National Research Polytechnic University Aerospace Engineering Bulletin (article, 2021-01-01)
Field: Materials Science — General Materials Science

ELEMENTAL ANALYSIS OF DISPERSED EMISSION PRODUCTS OF AN AIRCRAFT ENGINE FOR DIAGNOSING ITS PARTS EROSIVE WEAR The study of advanced diagnostic methods and tools for aircraft engines shows that spectral elemental analysis of aerosol emission products has recently become quite widespread in the engine maintenance programs of foreign manufacturers for detecting the turbomachinery parts erosive wear. To determine the elemental composition of aerosols at the gas turbine engine (GTE) outlet, opto-emission and mass spectrometric approaches have been used in various implementations. X-ray photoelectron spectroscopy and inductively coupled plasma mass spectrometry (ICP-MS) are recognized as the most informative and accessible laboratory methods. The most common sampling method is filtration with Teflon analytical membranes. Artificial intelligence technologies are used worldwide to identify and localize the source of the elements flow (a potential area of the GTE parts erosive wear). It has been found that the main problems of applying a multi-element analysis are associated with the sampling procedure, with the potential simultaneous element flow from several engine parts having a similar chemical composition; as well as with the variability of the “background” atmospheric aerosol composition, which is an unavoidable component of atmospheric air, especially in ground-level conditions. The article also presents the first results of the elemental analysis of aerosol particle emission samples from a modern Russian aircraft engine using the X-ray fluorescence method. Elemental analysis ; Aerosol ; Process engineering ; Mass spectrometry ; Particulates ; Environmental science ; Materials science ; Computer science ; Chemistry ; Engineering ; Chromatography Material Properties and Applications ; Cyclone Separators and Fluid Dynamics ; Vacuum and Plasma Arcs
Authors: Soo-Ah Jin | Tero Kämäräinen | Patrick Rinke | Orlando J. Rojas | Milica Todorović
Source: ChemRxiv (preprint, 2021-04-26)
Field: Chemistry — Analytical Chemistry

Machine Learning as a Tool to Engineer Microstructures: Morphological Prediction of Tannin-Based Colloids Using Bayesian Surrogate Models Oxidized tannic acid (OTA) is a useful biomolecule with a strong tendency to form complexes with metals and proteins. In this study we open the possibility to further the application of OTA when assembled as supramolecular systems, which typically exhibit functions that correlate with shape and associated morphological features. We use artificial intelligence (AI) to selectively engineer OTA into particles encompassing 1-dimensional (1D) to 3-dimensional (3D) constructs. We employed Bayesian regression to correlate colloidal suspension conditions (pH and p&lt;i&gt;K&lt;/i&gt;&lt;sub&gt;a&lt;/sub&gt;) with the size and shape of the assembled colloidal particles. Fewer than 20 experiments were found to be sufficient to build surrogate model landscapes of OTA morphology in the experimental design space, which were chemically interpretable and endowed predictive power on data. We produced multiple property landscapes from the experimental data, helping us to infer solutions that would satisfy, simultaneously, multiple design objectives. The balance between data efficiency and the depth of information delivered by AI approaches testify to their potential to engineer particles, opening new prospects in the emerging field of particle morphogenesis, impacting bioactivity, adhesion, interfacial stabilization and other functions inherent to OTA. Adhesion ; Colloid ; Tannic acid ; Biological system ; Surrogate model ; Suspension (topology) ; Artificial intelligence ; Nanotechnology ; Particle (ecology) ; Materials science ; Computer science ; Colloidal particle ; Machine learning ; Mathematics ; Chemistry ; Chemical engineering ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Cultural Heritage Materials Analysis