中国物理B ›› 2021, Vol. 30 ›› Issue (5): 50706-050706.doi: 10.1088/1674-1056/abf134
所属专题： SPECIAL TOPIC — Machine learning in condensed matter physics
Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space
Wanrun Jiang(姜万润)1,2, Yuzhi Zhang(张与之)3,4, Linfeng Zhang(张林峰)5,†, and Han Wang(王涵)6,‡
- 1 Songshan Lake Materials Laboratory, Dongguan 523808, China;
2 Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
3 Beijing Institute of Big Data Research, Beijing 100871, China;
4 Yuanpei College of Peking University, Beijing 100871, China;
5 Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544, USA;
6 Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing 100088, China
Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space
Wanrun Jiang(姜万润)1,2, Yuzhi Zhang(张与之)3,4, Linfeng Zhang(张林峰)5,†, and Han Wang(王涵)6,‡
- 1 Songshan Lake Materials Laboratory, Dongguan 523808, China;
2 Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
3 Beijing Institute of Big Data Research, Beijing 100871, China;
4 Yuanpei College of Peking University, Beijing 100871, China;
5 Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544, USA;
6 Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing 100088, China
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.
中图分类号: (Neural networks, fuzzy logic, artificial intelligence)
- 07.05.Mh