Machine-learned interatomic potentials for alloys and alloy phase diagrams

We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions.We compare two different approaches.Moment tensor potentials(MTPs)are polynomial-like functions of interatomic distances and angles.The Gaussian approximation potential(GAP)framework uses kernel regression,and we use the smooth overlap of atomic position(SOAP)representation of atomic neighborhoods that consist of a complete set of rotational and permutational invariants provided by the power spectrum of the spherical Fourier transform of the neighbor density.Both types of potentials give excellent accuracy for a wide range of compositions,competitive with the accuracy of cluster expansion,a benchmark for this system.While both models are able to describe small deformations away from the lattice positions,SOAP-GAP excels at transferability as shown by sensible transformation paths between configurations,and MTP allows,due to its lower computational cost,the calculation of compositional phase diagrams.Given the fact that both methods perform nearly as well as cluster expansion but yield off-lattice models,we expect them to open new avenues in computational materials modeling for alloys.

Chemically induced local lattice distortions versus structural phase transformations in compositionally complex alloys

Recent experiments show that the chemical composition of body-centered cubic(bcc)refractory high entropy alloys(HEAs)can be tuned to enable transformation-induced plasticity(TRIP),which significantly improves the ductility of these alloys.This calls for an accurate and efficient method to map the structural stability as a function of composition.A key challenge for atomistic simulations is to separate the structural transformation between the bcc and theωphases from the intrinsic local lattice distortions in such chemically disordered alloys.To solve this issue,we develop a method that utilizes a symmetry analysis to detect differences in the crystal structures.Utilizing this method in combination with ab initio calculations,we demonstrate that local lattice distortions largely affect the phase stability of Ti–Zr–Hf–Ta and Ti–Zr–Nb–Hf–Ta HEAs.If relaxation effects are properly taken into account,the predicted compositions near the bcc–hcp energetic equilibrium are close to the experimental compositions,for which good strength and ductility due to the TRIP effect are observed.

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.