Abstract
High entropy alloys (HEAs) are still a largely unexplored class of materials with high potential for applications in various fields. Motivated by the huge number of compounds in a given HEA class, we develop machine learning techniques, in particular artificial neural networks, coupled to ab initio calculations, in order to accurately predict some basic HEA properties: equilibrium phase, cohesive energies, density of states at the Fermi level and the stress-strain relation, under conditions of isotropic deformations. Known for its high tensile ductility and fracture toughness, the Co-Cr-Fe-Ni-Al alloy has been considered as a test candidate material, particularly by adjusting the Al content. However, further enhancement of the microstructure, mechanical and thermal properties is possible by modifying also the fractions of the base alloy. Using deep neural networks, we map structural and chemical neighborhood information onto the quantities of interest. This approach offers the possibility for an efficient screening over a huge number of potential candidates, which is essential in the exploration of multi-dimensional compositional spaces.
Highlights
High entropy alloys (HEAs) have gained significant interest in the past few years due to their potential use in various applications [1,2,3], encompassing fields like electronics, aerospace industry, hydrogen storage, coating technology, as well as base materials for components in nuclear facilities.Typically, HEAs exhibit superior strength [4], wear [5], corrosion [6,7], oxidation [8] and radiation [9]resistance, high hardness [10] and a good thermal stability [11]
We start our investigation by using a class of HEAs with equimolar ratios of the base elements (Co, Cr, Fe, Ni) and varying Al content, i.e., compounds of type CoCrFeNiAlx
The system can be described as a pseudobinary alloy, of type (CoCrFeNi)1−y Aly, with y = x/(4 + x ) [26]
Summary
High entropy alloys (HEAs) have gained significant interest in the past few years due to their potential use in various applications [1,2,3], encompassing fields like electronics, aerospace industry, hydrogen storage, coating technology, as well as base materials for components in nuclear facilities. Experimental investigations [23,24,25] of the CoCrFeNiAlx alloy have previously shown that the structural configuration changes from single FCC to a duplex phase FCC/BCC, and reaching a single BCC phase as the Al concentration increases This is further confirmed by theoretical stability calculations [26,27]. We focus here on four basic quantities, namely the total energy, the cohesive energy, the density of states at the Fermi energy and the stress induced by given strain These are significant for phase prediction and stability and can further bring insights into magnetic and mechanical properties of HEAs. Within our approach, the ANNs are trained using the collected DFT data, using for inputs readily available structural information, i.e., the proportion and the chemical environment of each species and supercell dimensions, together with additional chemical neighborhood information
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