Chemically complex multicomponent alloys possess exceptional properties derived from an inexhaustible compositional space.The complexity however makes interatomic potential development challenging.We explore two compl...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.展开更多
基金This project has received funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(grant agreement No.865855)The authors acknowledge support by the state of Baden-Württemberg through bwHPC and the German Research Foundation(DFG)through grant No.INST 40/575-1 FUGG(JUSTUS 2 cluster)+5 种基金We thank the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)for supporting this work by funding-EXC2075-390740016 under Germany’s Excellence StrategyWe acknowledge the support by the Stuttgart Center for Simulation Science(SimTech)K.G.and B.G.acknowledge support from the collaborative DFG-RFBR Grant(Grants No.DFG KO 5080/3-1,DFG GR 3716/6-1)K.G.also acknowledges the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)-Project-ID 358283783-SFB 1333/22022V.Z.acknowledges financial support received in the form of a PhD scholarship from the Studienstiftung des Deutschen Volkes(German National Academic Foundation)P.S.would like to thank the Alexander von Humboldt Foundation for their support through the Alexander von Humboldt Postdoctoral Fellowship Program.A.D.acknowledges support through EPSRC grant EP/S032835/1.
文摘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.
