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Accuracy of ab initio methods in predicting the crystal structures of metals: A review of 80 binary alloys

2005; Elsevier BV; Volume: 29; Issue: 3 Linguagem: Inglês

10.1016/j.calphad.2005.01.002

1873-2984

Stefano Curtarolo, Dane Morgan, Gerbrand Ceder,

Machine Learning in Materials Science

Predicting and characterizing the crystal structure of materials is a key problem in materials research and development. We report the results of ab initio LDA/GGA computations for the following systems: AgAu, AgCd, AgMg, AgMo∗, AgNa, AgNb∗, AgPd, AgRh∗, AgRu∗, AgTc∗, AgTi, AgY, AgZr, AlSc, AuCd, AuMo∗, AuNb, AuPd, AuPt∗, AuRh∗, AuRu∗, AuSc, AuTc∗, AuTi, AuY, AuZr, CdMo∗, CdNb∗, CdPd, CdPt, CdRh, CdRu∗, CdTc∗, CdTi, CdY, CdZr, CrMg∗, MoNb, MoPd, MoPt, MoRh, MoRu, MoTc∗, MoTi, MoY ∗, MoZr, NbPd, NbPt, NbRh, NbRu, NbTc, NbY ∗, NbZr∗, PdPt, PdRh∗, PdRu∗, PdTc, PdTi, PdY, PdZr, PtRh, PtRu, PtY, PtTc, PtTi, PtZr, RhRu, RhTc, RhTi, RhY, RhZr, RuTi, RuTc, RuY, RuZr, TcTi, TcY, TcZr, TiZr∗, Y Zr∗ (∗= systems in which the ab initio method predicts that no compounds are stable). A detailed comparison to experimental data confirms the high accuracy with which ab initio methods can predict ground states.