Mechanistic origin of high retained strength in refractory BCC high entropy alloys up to 1900K

The body centered cubic (BCC) high entropy alloys MoNbTaW and MoNbTaVW show exceptional strength retention up to 1900K. The mechanistic origin of the retained strength is unknown yet is crucial for finding the best alloys across the immense space of BCC HEA compositions. Experiments on Nb-Mo, Fe-Si and Ti-Zr-Nb alloys report decreased mobility of edge dislocations, motivating a theory of strengthening of edge dislocations in BCC alloys. Unlike pure BCC metals and dilute alloys that are controlled by screw dislocation motion at low temperatures, the strength of BCC HEAs can be controlled by edge dislocations, and especially at high temperatures, due to the barriers created for edge glide through the random field of solutes. A parameter-free theory for edge motion in BCC alloys qualitatively and quantitatively captures the strength versus temperature for the MoNbTaW and MoNbTaVW alloys. A reduced analytic version of the theory then enables screening over >600,000 compositions in the Mo-Nb-Ta-V-W family, identifying promising new compositions with high retained strength and/or reduced mass density. Overall, the theory reveals an unexpected mechanism responsible for high temperature strength in BCC alloys and paves the way for theory-guided design of stronger high entropy alloys.Comment: This version corrects the theory and provides more extensive explanation

On the impact of lattice parameter accuracy of atomistic simulations on the microstructure of Ni-Ti shape memory alloys

Ni-Ti is a key shape memory alloy (SMA) system for applications, being cheap and having good mechanical properties. Recently, atomistic simulations of Ni-Ti SMAs have been used with the purpose of revealing the nano-scale mechanisms that control superelasticity and the shape memory effect, which is crucial to guide alloying or processing strategies to improve materials performance. These atomistic simulations are based on molecular dynamics modelling that relies on (empirical) interatomic potentials. These simulations must reproduce accurately the mechanism of martensitic transformation and the microstructure that it originates, since this controls both superelasticity and the shape memory effect. As demonstrated by the energy minimization theory of martensitic transformations [Ball, James (1987) Archive for Rational Mechanics and Analysis, 100:13], the microstructure of martensite depends on the lattice parameters of the austenite and the martensite phases. Here, we compute the bounds of possible microstructural variations based on the experimental variations/uncertainties in the lattice parameter measurements. We show that both density functional theory and molecular dynamics lattice parameters are typically outside the experimental range, and that seemingly small deviations from this range induce large deviations from the experimental bounds of the microstructural predictions, with notable cases where unphysical microstructures are predicted to form. Therefore, our work points to a strategy for benchmarking and selecting interatomic potentials for atomistic modelling of shape memory alloys, which is crucial to modelling the development of martensitic microstructures and their impact on the shape memory effect

Highly conductive, ionic liquid-based polymer electrolytes

In this manuscript is reported a thermal and impedance spectroscopy investigation carried out on quaternary polymer electrolytes, to be addressed as separators for lithium solid polymer batteries, containing large amount of the N-methyl-N-propylpyrrolidinium bis(fluorosulfonyl)imide ionic liquid. The target is the development of Li+ conducting membranes with enhanced ion transport even below room temperature. Polyethylene oxide and polymethyl methacrylate were selected as the polymeric hosts. A fully dry, solvent-free procedure was followed for the preparation of the polymer electrolytes, which were seen to be self-consistent and handled even upon prolonged storage periods (more than 1 year). Appealing ionic conductivities were observed especially for the PEO electrolytes, i.e., 1.6 × 10-3and 1.5 × 10-4 S cm-1 were reached at 20 and -20°C, respectively, which are ones the best, if not the best ion conduction, never detected for polymer electrolytes

Intelligenza artificiale nella didattica universitaria: lo studio di un caso per la rilevazione delle discariche abusive nelle zone urbane di Genova

Il cognitive computing sta rivoluzionando le competenze e le conoscenze che devono essere acquisite dagli allievi universitari che si devono cimen-tare con la risoluzione dei problemi complessi, ciò è dovuto alla irruzione “dirompente” delle tecnologie quali: cloud, Big Data, IoT, dispositivi mobi-li e social network. Il cognitive computing, in particolare, rappresenta la tecnologia più dirompente che le integra tutte. In questo lavoro gli autori presentano lo studio di un caso, la rilevazione delle discariche abusive nelle zone urbane della città di Genova. Il lavoro è stato svolto con la collaborazione di IBM-Italia e l’assessorato all’ambiente del comune di Genova. L’algoritmo sviluppato consente la rilevazione e la segnalazione di ogni tipo di rifiuto in real time attraverso metodi di intelli-genza artificiale adoperando Watson-IBM. L’algoritmo è stato insignito come il miglior algoritmo italiano di IA mai sviluppato per questo dominio applicativo ed è stato premiato da IBM alla presenza dell’assessore all’ambiente del comune di Genova e del vicepresidente IBM Cloud Italia Alessandro la Volpe, durante la convention Party Cloud per Genova tenu-tasi a Milano nei giorni 11-12 Novembre 2018. Il premio è andato al giova-ne studente Vincenzo De Francesco il quale ha continuato su questa temati-ca nella sua tesi triennale. Questo lavoro, oltre a discutere lo studio di un caso, vuole riflettere su alcune implicazioni che questa tecnologia dirom-pente sta causando sulla didattica universitaria (e non solo) e su come alcu-ni skill, richiesti dalle aziende, ma non disponibili ancora nei nostri corsi di studio universitari, possono essere costruiti in corsi specifici come quel-lo di Cognitive Computing Systems impartito presso il Dipartimento di In-gegneria Elettrica e Tecnologie dell’Informazione dell’Università di Napoli Federico II

Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential

The prediction of atomistic fracture mechanisms in body-centred cubic (bcc) iron is essential for understanding its semi-brittle nature. Existing atomistic simulations of the crack-tip under mode-I loading based on empirical interatomic potentials yield contradicting predictions and artificial mechanisms. To enable fracture prediction with quantum accuracy, we develop a Gaussian approximation potential (GAP) using an active learning strategy by extending a density functional theory (DFT) database of ferromagnetic bcc iron. We apply the active learning algorithm and obtain a Fe GAP model with a converged model uncertainty over a broad range of stress intensity factors (SIFs) and for four crack systems. The learning efficiency of the approach is analysed, and the predicted critical SIFs are compared with Griffith and Rice theories. The simulations reveal that cleavage along the original crack plane is the atomistic fracture mechanism for {100} and {110} crack planes at T = 0 K, thus settling a long-standing issue. Our work also highlights the need for a multiscale approach to predicting fracture and intrinsic ductility, whereby finite temperature, finite loading rate effects and pre-existing defects (e.g., nanovoids, dislocations) should be taken explicitly into account.</p

Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential

The prediction of atomistic fracture mechanisms in body-centred cubic (bcc) iron is essential for understanding its semi-brittle nature. Existing atomistic simulations of the crack-tip under mode-I loading based on empirical interatomic potentials yield contradicting predictions and artificial mechanisms. To enable fracture prediction with quantum accuracy, we develop a Gaussian approximation potential (GAP) using an active learning strategy by extending a density functional theory (DFT) database of ferromagnetic bcc iron. We apply the active learning algorithm and obtain a Fe GAP model with a converged model uncertainty over a broad range of stress intensity factors (SIFs) and for four crack systems. The learning efficiency of the approach is analysed, and the predicted critical SIFs are compared with Griffith and Rice theories. The simulations reveal that cleavage along the original crack plane is the atomistic fracture mechanism for {100} and {110} crack planes at T = 0 K, thus settling a long-standing issue. Our work also highlights the need for a multiscale approach to predicting fracture and intrinsic ductility, whereby finite temperature, finite loading rate effects and pre-existing defects (e.g., nanovoids, dislocations) should be taken explicitly into account.</p

Measurement and prediction of the transformation strain that controls ductility and toughness in advanced steels

New-generation multi-phase martensitic steels derive their high strength from the body-centered cubic (BCC) phase and high toughness from transformation of the metastable face-centered cubic (FCC) austenite that transforms into martensite upon loading. In spite of its critical importance, the in-situ transformation strain (or "shape deformation" tensor), which controls ductility and toughness, has never been measured in any alloy where the BCC lath martensite forms and has never been connected to underlying material properties. Here, we measure the in-situ transformation strain in a classic Fe-Ni-Mn alloy using high-resolution digital image correlation (HR-DIC). The experimentally obtained results can only be interpreted using a recent theory of lath martensite crystallography. The predicted in-situ transformation strain agrees with the measurements, simultaneously demonstrating the method and validating the theory. Theory then predicts that increasing the FCC to BCC lattice parameter ratio substantially increases the in-situ transformation strain magnitude. This new correlation is demonstrated using data on existing steels. These results thus establish a new additional basic design principle for ductile and tough alloys: control of the lattice parameter ratio by alloying. This provides a new path for development of even tougher advanced high-strength steels. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd

Cross-kink unpinning controls the medium-to high-temperature strength of body-centered cubic NbTiZr medium-entropy alloy

The deformation mechanisms of a NbTiZr body-centered cubic (BCC) medium-entropy alloy (MEA) are investigated by tensile testing at various temperatures. The yield strength (YS) shows a strong temperature dependence from 77 K to 300 K, while being insensitive to temperatures between 300 K and 873 K, followed by a significant drop at 1073 K. TEM investigations show that the alloy deformation is controlled by screw-dislocation slip. Screw-dislocations with cross-kinks/jogs are frequently observed at all temperatures except at 1073 K. The deformed microstructure at 473 K reveals dislocations loops/debris indicating the dominance of cross-kink strengthening at moderate to high temperatures, leading to a temperature insensitive YS. The behavior of NbTiZr is consistent with the cross-kink strengthening mechanism, as also confirmed by the comparison between observed and predicted values of the activation volume. The TEM investigations at 1073 K are consistent with the annihilation of cross-kinks/edge dipoles, which can explain the observed strength drop above this temperature