Figure8

Figure 8. Validity and time cost of machine-learning force fields. (A) Comparison of the DFT energies and the DeepPot-SE predicted energies on the testing snapshots. Reproduced from Ref.^[110]. Copyright 2018, Curran Associates Inc.; (B) Phonon band structure and DOS of fcc Al using DFT (blue dashed lines), and optimized (red solid lines) and original (green dashed lines) ML interatomic potentials. Reproduced with permission^[162]. Copyright 2019, AIP Publishing; (C) Correlation functions of liquid water from DPMD and PI-AIMD. Reproduced with permission^[164]. Copyright 2018, Elsevier; (D) Computational cost of MD steps versus system size with DPMD, TIP3P, PBE + TS, and PBE0 + TS. Reproduced with permission^[164]. Copyright 2018, Elsevier. DFT: Density functional theory; DOS: density of states; ML: machine learning; DPMD: deep potential molecular dynamics; PI-AIMD: path-integral Ab initio molecular dynamics; MD: molecular dynamics; TIP3P: transferable intermolecular potential with 3 points; PBE: Perdew-Burke-Ernzerhof functional; TS: Tkatchenko-Scheffler functional.