Modeling the structure and thermodynamics of high-entropy alloys

Abstract

Abstract

Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials

“…Due to these limitations it is, however, not clear whether such an ordering at ambient temperatures is real or an artifact of the underlying approximations (e.g., limited range of interaction parameters). The difficulties of converging a CE for multicomponent alloys has been also recently pointed out by Widom et al 16 on the example of finding the ground state of bcc NbMoTaW 17 where a conventional CE fails.…”

First Principles Calculation of the Entropy of Liquid Aluminum

“…The electronic density of states D(E), which comes as a byproduct of first principles calculations, determines the electronic entropy [22]. At low temperatures, all states below the Fermi energy E F are filled and all states above are empty.…”

Robust data-driven approach for predicting the configurational energy of high entropy alloys

“…Typically, the material properties are inherently linked to the actual state of chemical ordering, much efforts have been therefore devoted to analyze the degree of chemical ordering and to identify the order-disorder phase transitions [8,7,9,10]. Due to expensive time costs in experimental research, computational simulations, typically first-principles calculations are playing an increasingly central role in the investigation of various properties of HEAs [11,12,13].…”

“…Typically, the material properties are inherently linked to the actual state of chemical ordering, much efforts have been therefore devoted to analyze the degree of chemical ordering and to identify the order-disorder phase transitions [8,7,9,10]. Due to expensive time costs in experimental research, computational simulations, typically first-principles calculations are playing an increasingly central role in the investigation of various properties of HEAs [11,12,13].First-principles density functional theory (DFT) methods have established as a powerful and reliable tool in computational material science and have enabled critical advancements in materials properties and performance discovery [14,15]. With the increasing numerical efficiency and growing computing power (parallel and GPU computing), it is still difficult to address the challenge of DFT calculations in relatively large supercells (thousands of atoms) and intensive sampling (huge number of configurations) [16].…”

“…Step 6: Monte Carlo calculations of thermodynamics -If the prediction is accurate and stable enough, we can feed this efficient fitted model as a surrogate into Monte Carlo simulation for modeling thermodynamics and order-disorder phase transitions. But this step is not the focus of this paper and the interested reader can find more discussions in the recent review literature[11,10] used here for the calculation of total energy, with supercell of 16, 32, 64 and 128 for NbMoTaW and 20, 40, 80 and 160 for NbMoTaWV and NbMoTaWTi respectively.Fig. 3shows the bcc supercell lattice of NbMoTaW (128 atoms), NbMoTaWV and NbMoTaWTi (160 atoms).For each supercell size, 200 configurations are randomly drawn and the corresponding energy are calculated by DFT method.…”