Materials Science (cond-mat.mtrl-sci)

Abstract: In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties; ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software; The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.

Abstract: We employed first-principles calculations to investigate the thermoelectric transport properties of the compound As$_2$Se$_3$. Early experiments and calculations have indicated that these properties are controlled by a kind of native defect called antisites. Our calculations using the linearized Boltzmann transport equation within the relaxation time approximation show good agreement with the experiments for defect concentrations of the order of 10$^{19}$ cm$^{-3}$. Based on our total energy calculations, we estimated the equilibrium concentration of antisite defects to be about 10$^{14}$ cm$^{-3}$. These results suggest that the large concentration of defects in the experiments is due to kinetic and/or off-stoichiometry effects and in principle it could be lowered, yielding relaxation times similar to those found in other chalcogenide compounds. In this case, for relaxation time higher than 10 fs, we obtained high thermoelectric figures of merit of 3 for the p-type material and 2 for the n-type one.

Abstract: Despite a clear demonstration of bistability in spin-crossover (SCO) materials, the absence of long-range magnetic order and poor electrical conductivity limit their prospect in spintronic and nanoelectronic applications. Intending to create hybrid devices made of spin-crossover (SCO)-2D architecture, here, we report an easily processable Fe-based SCO nanostructures grown on 2D reduced graphene oxide (rGO). The heterostructure shows enhanced cooperativity due to formation of interfacial charge transfer induced inter-molecular interaction. The spin transition temperature is controlled by tuning the coverage area of SCO nanostructured networks over the 2D surfaces, thus manipulating hysteresis (aka memory) of the heterostructure. The enhanced magnetic coupling of the heterostructure leads to the spontaneous magnetization states with a large coercive field of $\sim$ 3000 Oe. Additionally, the low conductivity of the pristine SCO nanostructures is addressed by encapsulating them on suitable 2D rGO template, enabling detection of magnetic bistable spin states during high-spin/low-spin conductance change. This adds spin functionality in conductance switching for realizing hybrid 2D spintronic devices. Ab-inito calculations, on the experimentally proposed nanostructures, corroborate the enhanced magnetic interaction in the proposed architecture facilitated by interfacial charge transfer and provide insights on the microscopic mechanism.

Abstract: The control of mortar rheology is of paramount importance in the design of systems and structures in 3D printing concrete by extrusion. This is particularly sensitive for two-component (2K) processes that use an accelerator to switch the printed mortar very quickly from a liquid behavior to a sufficiently solid behavior to be able to be printed. It is necessary to set up simple and effective tests within a precise methodological framework to qualify materials evolving so quickly in an industrial context. It is obvious that inline solutions, that is to say, post-printing solutions, will be more desirable than benchtop-type solutions reproducing the printing conditions as well as possible, but imperfectly. After some main key points about measuring the structuration of mortars, we propose an original inline test using a pocket shear vane tester. The protocols are precisely described and the simplicity and quality of the results are demonstrated.

Abstract: Electron ptychography provides highly sensitive, dose efficient phase images which can be corrected for aberrations after the data has been acquired. This is crucial when very precise quantification is required, such as with sensitivity to charge transfer due to bonding. Drift can now be essentially eliminated as a major impediment to focused probe ptychography, which benefits from the availability of easily interpretable simultaneous Z-contrast imaging. However challenges have remained when quantifying the ptychographic phases of atomic sites. The phase response of a single atom has a negative halo which can cause atoms to reduce in phase when brought closer together. When unaccounted for, as in integrating methods of quantification, this effect can completely obscure the effects of charge transfer. Here we provide a new method of quantification that overcomes this challenge, at least for 2D materials, and is robust to experimental parameters such as noise, sample tilt.

Abstract: We investigate the effect of nuclear quantum effects (NQEs) of hydrogen atoms on the elasticity of ice VII at high pressure and ambient temperature conditions using ab initio path-integral molecular dynamics (PIMD) calculations. We find that the NQEs of hydrogen contributes to the transition of ice VII from a static disordered structure to a dynamically disordered structure at pressures exceeding 40 GPa. This transition is marked by a discontinuous increase of the elastic constants. Comparison of ab initio molecular dynamics and PIMD calculations reveal that NQEs increase the elastic constants of ice by about 20% at 70 GPa and 300 K.

Abstract: MXenes are an emerging class of 2D materials of interest in applications ranging from energy storage to electromagnetic shielding. MXenes are synthesized by selective etching of layered bulk MAX phases into sheets of 2D MXenes. Their chemical tunability has been significantly expanded with the successful synthesis of double transition metal MXenes. While knowledge of the structure and energetics of double transition metal MAX phases is critical to designing and optimizing new MXenes, only a small subset of these materials been explored. We present a comprehensive dataset of key properties of MAX phases obtained using density functional theory within the generalized gradient approximation exchange-correlation functionals. Energetics and structure of 8,712 MAX phases have been calculated and stored in a queryable, open database hosted at nanoHUB.