2021/07/29 by Ryo Tamura, Momo Matsuda, Jianbo Lin +3
Biochemistry, Genetics and Molecular Biology · Chemistry · Materials Science · Mathematics · Physics and Astronomy · #Advanced Electron Microscopy Techniques and Applications #Atom (system on chip) #Binary number #Biological system #Chemistry #Component (thermodynamics) #Computational chemistry #Computer science #Locality #Machine Learning in Materials Science #Materials science #Mathematics #Molecular dynamics #Physics #Process (computing) #Silicon #Space (punctuation) #Statistical physics #Theoretical and Computational Physics #Thermodynamics #cond-mat.mtrl-sci
paper · pdf · doi:10.1103/physrevb.105.075107
16 pages, 13 figures
arxiv created 2021/07/29 · openalex publication_date 2022/02/03 · openalex created_date 2022/02/08 · arxiv updated 2022/02/16 · openalex updated_date 2026/07/13
Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of data points for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system.