ICMCTF 2022 Session F5-2-MoA: In-Silico Design of Novel Materials by Quantum Mechanics and Classical Methods II

Monday, May 23, 2022 3:40 PM in Room Town & Country C

Monday Afternoon

Start	Invited?	Item
3:40 PM		F5-2-MoA-7 Theoretical Investigation of Sluggish Diffusion in Nitride Films of High-Entropy Alloys Ganesh Kumar Nayak (Montanuniversität Leoben); Andreas Kretschmer, Paul Heinz Mayrhofer (TU Wien, Austria); David Holec (Montanuniversität Leoben) The concept of alloying was revolutionized in multi-component or high-entropy alloys (HEA), where five or more elements are distributed randomly on a crystalline lattice in equiatomic or near-equiatomic composition. Thereby, no element acts as a principal component and four core effects have been postulated to stem from this configuration: high configurational entropy, severe lattice distortion, sluggish diffusion, and cocktail effects. Since we still lack a proper quantification of the sluggish diffusion, this work focuses on this topic applied to the case of high-entropy nitrides (HENs). These ceramic materials possess high hardness and good thermal stability and are hence attractive for high-temperature applications. The HEN systems that have been considered for this ab initio study are non-magnetic and structurally stable systems with the metals distributed on the metal sublattice by special quasi-random structure (SQS) methods. For each HEN system and each species, we determined migration barriers corresponding to vacancy-driven elementary point-defect migration mechanisms for crystalline solids. The change in diffusion w.r.t. migration barrier, while going up from ternary to hexinary systems, will be presented. Our results suggest that the impact of the local composition and increasing high-entropy environment can significantly alter these results. Our analyses focus on comparing low and high entropy systems (as measured by the number of elements) for systems exhibiting low and large local distortions, and similar and different nominal bond lengths of the forming binary nitrides. From our preliminary results, the claimed sluggishness of the diffusion in HENs should be more composition and/or environment-specific rather than generalizing for all high entropy systems.
4:20 PM	Invited	F5-2-MoA-9 Machine Learning Assisted Ab Initio Thermodynamics of Novel Materials Prashanth Srinivasan (University of Stuttgart); Fritz Körmann (Max-Planck Institut für Eisenforschung); Blazej Grabowski (University of Stuttgart) Recent developments in machine learning techniques has immensely benefited ab initiomodeling of materials. Interatomic potentials such as the moment tensor potential (MTP) (Shapeev, 2016) that are trained to high temperature density-functional theory (DFT) data are able to predict energies and forces of atomic configurations highly accurately. They are thus able to statistically sample a much wider part of the phase space in a fast and efficient manner. In combination with a systematic thermodynamic integration method (Direct Upsampling), they can be used to calculate total free energies of even complicated systems such as high entropy alloys (HEAs) to 1 meV accuracy (Grabowski et al., 2019, Ferrari et al., 2020) up to the melting point. Apart from static and electronic energies, this also includes vibrational contributions including anharmonicity which significantly affect thermodynamic properties such as specific heat capacity and bulk modulus at high temperatures. Here, firstly, we demonstrate these results for a bunch of refractory BCC systems ranging from single- to five-component alloys. We break-down the total free energies into various individual contributions. We compare a contrasting trend in the anharmonic free energies beyond the quasi-harmonic approximation in certain BCC refractories, some of which show a positive contribution and some a negative contribution. We narrow this feature down to the density of states (DOS) and the first- and second- neighbor forces and illustrate a difference in bonding behavior between the two sets of BCC elements. Secondly, we also show the applicability of the MTPs to design novel shape memory alloy materials, where a MTP trained to DFT data predicts the stress- and temperature-induced phase transformations in these alloys.
5:00 PM	Invited	F5-2-MoA-11 Materials Design Principles of Amorphous Cathode Coatings for Lithium-ion Battery Applications Jianli Cheng, Kristin Persson (Lawrence Berkeley National Laboratory (LBNL)) Cathode surface coatings have been the foremost solution to suppress cathode degradation and improve cycling performance of lithium-ion batteries (LIBs). In this work, we carry out an extensive high-throughput computational study to develop materials design principles governing amorphous cathode coating selections for LIBs. Our high-throughput screening includes descriptors to evaluate the thermodynamic stability, electrochemical stability, chemical reactivity with electrolytes and cathodes, and ionic diffusivities in the cathode coatings. We consider the commonly used Li₃PS₄ and LiPF₆ as the solid and liquid electrolytes, respectively, and categorize the coating materials based on their chemical reactivity with the electrolytes. We reveal the formidable challenge of mitigating oxygen diffusion when selecting an ideal cathode coating, and suggest a few promising materials that pass all the criteria in our high-throughput study. Combining the screening results and detailed ionic diffusion analysis of the selected cathode coatings, we summarize the general guidelines for selecting amorphous cathode coatings.