Content of SPECIAL TOPIC—Machine learning in condensed matter physics in our journal

Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For selected: Download citations EndNote Ris BibTeX Toggle thumbnails Select Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space Wanrun Jiang(姜万润), Yuzhi Zhang(张与之), Linfeng Zhang(张林峰), and Han Wang(王涵) Chin. Phys. B, 2021, 30 (5): 050706.   DOI: 10.1088/1674-1056/abf134 Abstract （703）   HTML （20）    PDF （787KB）（332）       Knowledge map    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. Select Quantitative structure-plasticity relationship in metallic glass: A machine learning study Yicheng Wu(吴义成), Bin Xu(徐斌), Yitao Sun(孙奕韬), and Pengfei Guan(管鹏飞) Chin. Phys. B, 2021, 30 (5): 057103.   DOI: 10.1088/1674-1056/abdda5 Abstract （519）   HTML （3）    PDF （724KB）（135）       Knowledge map    The lack of the long-range order in the atomic structure challenges the identification of the structural defects, akin to dislocations in crystals, which are responsible for predicting plastic events and mechanical failure in metallic glasses (MGs). Although vast structural indicators have been proposed to identify the structural defects, quantitatively gauging the correlations between these proposed indicators based on the undeformed configuration and the plasticity of MGs upon external loads is still lacking. Here, we systematically analyze the ability of these indicators to predict plastic events in a representative MG model using machine learning method. Moreover, we evaluate the influences of coarse graining method and medium-range order on the predictive power. We demonstrate that indicators relevant to the low-frequency vibrational modes reveal the intrinsic structural characteristics of plastic rearrangements. Our work makes an important step towards quantitative assessments of given indicators, and thereby an effective identification of the structural defects in MGs. Select Artificial neural network potential for gold clusters Ling-Zhi Cao(曹凌志), Peng-Ju Wang(王鹏举), Lin-Wei Sai(赛琳伟), Jie Fu(付洁), and Xiang-Mei Duan(段香梅) Chin. Phys. B, 2020, 29 (11): 117304.   DOI: 10.1088/1674-1056/abc15d Abstract （486）   HTML    PDF （966KB）（209）       Knowledge map    In cluster science, it is challenging to identify the ground state structures (GSS) of gold (Au) clusters. Among different search approaches, first-principles method based on density functional theory (DFT) is the most reliable one with high precision. However, as the cluster size increases, it requires more expensive computational cost and becomes impracticable. In this paper, we have developed an artificial neural network (ANN) potential for Au clusters, which is trained to the DFT binding energies and forces of 9000 AuN clusters (11 ≤ N ≤ 100). The root mean square errors of energy and force are 13.4 meV/atom and 0.4 eV/Å, respectively. We demonstrate that the ANN potential has the capacity to differentiate the energy level of Au clusters and their isomers and highlight the need to further improve the accuracy. Given its excellent transferability, we emphasis that ANN potential is a promising tool to breakthrough computational bottleneck of DFT method and effectively accelerate the pre-screening of Au clusters’ GSS.