Ab initio vibrational free energies including anharmonicity for multicomponent alloys

The unique and unanticipated properties of multiple principal component alloys have reinvigorated the field of alloy design and drawn strong interest across scientific disciplines.The vast compositional parameter space makes these alloys a unique area of exploration by means of computational design.However,as of now a method to compute efficiently,yet with high accuracy the thermodynamic properties of such alloys has been missing.One of the underlying reasons is the lack of accurate and efficient approaches to compute vibrational free energies—including anharmonicity—for these chemically complex multicomponent alloys.In this work,a density-functional-theory based approach to overcome this issue is developed based on a combination of thermodynamic integration and a machine-learning potential.We demonstrate the performance of the approach by computing the anharmonic free energy of the prototypical five-component VNbMoTaW refractory high entropy alloy.

Performance of two complementary machine-learned potentials in modelling chemically complex systems

Chemically complex multicomponent alloys possess exceptional properties derived from an inexhaustible compositional space.The complexity however makes interatomic potential development challenging.We explore two complementary machine-learned potentials—the moment tensor potential(MTP)and the Gaussian moment neural network(GM-NN)—in simultaneously describing configurational and vibrational degrees of freedom in the Ta-V-Cr-W alloy family.Both models are equally accurate with excellent performance evaluated against density-functional-theory.They achieve root-mean-square-errors(RMSEs)in energies of less than a few meV/atom across 0 K ordered and high-temperature disordered configurations included in the training.Even for compositions not in training,relative energy RMSEs at high temperatures are within a few meV/atom.High-temperature molecular dynamics forces have similarly small RMSEs of about 0.15 eV/Åfor the disordered quaternary included in,and ternaries not part of training.MTPs achieve faster convergence with training size;GM-NNs are faster in execution.Active learning is partially beneficial and should be complemented with conventional human-based training set generation.

High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials

Accurate prediction of thermodynamic properties requires an extremely accurate representation of the free-energy surface.Requirements are twofold—first,the inclusion of the relevant finite-temperature mechanisms,and second,a dense volume–temperature grid on which the calculations are performed.A systematic workflow for such calculations requires computational efficiency and reliability,and has not been available within an ab initio framework so far.Here,we elucidate such a framework involving direct upsampling,thermodynamic integration and machine-learning potentials,allowing us to incorporate,in particular,the full effect of anharmonic vibrations.The improved methodology has a five-times speed-up compared to state-of-the-art methods.We calculate equilibrium thermodynamic properties up to the melting point for bcc Nb,magnetic fcc Ni,fcc Al,and hcp Mg,and find remarkable agreement with experimental data.A strong impact of anharmonicity is observed specifically for Nb.The introduced procedure paves the way for the development of ab initio thermodynamic databases.