Unsupervised learning-based structural analysis: Search for a characteristic low-dimensional space by local structures in atomistic simulations
arXiv:2107.14311 · doi:10.1103/PhysRevB.105.075107
Abstract
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 datapoints 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.
16 pages, 13 figures
References in corpus (10)
- Accurate determination of crystal structures based on averaged local bond order parameters
- Machine Learning Unifies the Modelling of Materials and Molecules
- On-the-fly machine learning force field generation: Application to melting points
- Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference
- Recent progress with large-scale ab initio calculations: the CONQUEST code
- Accurate Force Field for Molybdenum by Machine Learning Large Materials Data
- Autonomously revealing hidden local structures in supercooled liquids
- Liquid-liquid transition in supercooled silicon determined by first-principles simulation
- Large scale and linear scaling DFT with the CONQUEST code
- In operando active learning of interatomic interaction during large-scale simulations