摘要
Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface(PES)is the philosopher's stone for unraveling the nature of matter via atomistic simulation.This has been particularly challenging for multi-component alloy systems due to the complex and non-linear nature of the associated PES.In this work,we develop an accurate PES model for the Al-Cu-Mg system by employing deep potential(DP),a neural network based representation of the PES,and DP generator(DP-GEN),a concurrent-learning scheme that generates a compact set of ab initio data for training.The resulting DP model gives predictions consistent with first-principles calculations for various binary and ternary systems on their fundamental energetic and mechanical properties,including formation energy,equilibrium volume,equation of state,interstitial energy,vacancy and surface formation energy,as well as elastic moduli.Extensive benchmark shows that the DP model is ready and will be useful for atomistic modeling of the Al-Cu-Mg system within the full range of concentration.
作者
Wanrun Jiang
Yuzhi Zhang
Linfeng Zhang
Han Wang
姜万润;张与之;张林峰;王涵(Songshan Lake Materials Laboratory,Dongguan 523808,China;Institute of Physics,Chinese Academy of Sciences,Beijing 100190,China;Beijing Institute of Big Data Research,Beijing 100871,China;Yuanpei College of Peking University,Beijing 100871,China;Program in Applied and Computational Mathematics,Princeton University,Princeton,NJ 08544,USA;Laboratory of Computational Physics,Institute of Applied Physics and Computational Mathematics,Beijing 100088,China)
基金
the National Natural Science Foundation of China(Grant No.11871110)
the National Key Research and Development Program of China(Grant Nos.2016YFB0201200 and 2016YFB0201203)
Beijing Academy of Artificial Intelligence(BAAI).
