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

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npj Computational Materials 2021年第1期7卷 312-319页

作者： Yuji Ikeda konstantin gubaev Jörg Neugebauer Blazej Grabowski Fritz Körmann Institute for Materials Science University of StuttgartStuttgartGermany Max-Planck-Institut für Eisenforschung GmbH DüsseldorfGermany Department of Materials Science and Engineering Delft University of TechnologyDelftThe Netherlands

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 *** 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 *** solve this issue,we develop a method that utilizes a symmetry analysis to detect differences in the crystal *** 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 *** 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.

关键词： alloys ductility distortion

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

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npj Computational Materials 2023年第1期9卷 1008-1022页

作者： konstantin gubaev Viktor Zaverkin Prashanth Srinivasan Andrew Ian Duff Johannes Kästner Blazej Grabowski Institute for Materials Science University of StuttgartPfaffenwaldring 5570569 StuttgartGermany Institute for Theoretical Chemistry University of StuttgartPfaffenwaldring 5570569 StuttgartGermany Scientific Computing Department STFC Daresbury LaboratoryWarrington WA44ADUK Present address:NEC Laboratories Europe GmbH Kurfürsten-Anlage 3669115 HeidelbergGermany

Chemically complex multicomponent alloys possess exceptional properties derived from an inexhaustible compositional *** complexity however makes interatomic potential development *** 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 *** models are equally accurate with excellent performance evaluated against *** 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 *** for compositions not in training,relative energy RMSEs at high temperatures are within a few meV/***-temperature molecular dynamics forces have similarly small RMSEs of about 0.15 eV/Åfor the disordered quaternary included in,and ternaries not part of *** achieve faster convergence with training size;GM-NNs are faster in *** learning is partially beneficial and should be complemented with conventional human-based training set generation.

关键词： alloy faster complex

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Machine-learned interatomic potentials for alloys and alloy phase diagrams

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npj Computational Materials 2021年第1期7卷 139-147页

作者： Conrad W.Rosenbrock konstantin gubaev Alexander VShapeev Livia B.Pártay Noam Bernstein Gábor Csányi Gus L.W.Hart Department of Physics and Astronomy Brigham Young UniversityProvoUTUSA84602 Skolkovo Institute of Science and Technology Skolkovo Innovation CenterNobel Str.3Moscow 143026Russia Department of Chemistry University of ReadingWhiteknightsReading RG66ADUK Department of Chemistry University of WarwickCoventry CV47ALUK US Naval Research Laboratory Center for Computational Materials ScienceWashingtonDC 20375USA Department of Engineering University of CambridgeTrumpington StreetCambridge CB21PZUK

We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of *** compare two different *** tensor potentials(MTPs)are polynomial-like functions of interatomic distances and *** 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 *** types of potentials give excellent accuracy for a wide range of compositions,competitive with the accuracy of cluster expansion,a benchmark for this *** 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 *** 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.

关键词： alloy alloys phase

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