Yeo, Byung Chul Kim, Donghun Kim, Chansoo Han, Sang Soo 2024-01-19T20:04:50Z 2024-01-19T20:04:50Z 2021-09-02 2019-04-10 2045-2322 https://pubs.kist.re.kr/handle/201004/120108 Electronic density of states (DOS) is a key factor in condensed matter physics and material science that determines the properties of metals. First-principles density-functional theory (DFT) calculations have typically been used to obtain the DOS despite the considerable computation cost. Herein, we report a fast machine learning method for predicting the DOS patterns of not only bulk structures but also surface structures in multi-component alloy systems by a principal component analysis. Within this framework, we use only four features to define the composition, atomic structure, and surfaces of alloys, which are the d-orbital occupation ratio, coordination number, mixing factor, and the inverse of miller indices. While the DFT method scales as O(N-3) in which N is the number of electrons in the system size, our pattern learning method can be independent on the number of electrons. Furthermore, our method provides a pattern similarity of 91 similar to 98% compared to DFT calculations. This reveals that our learning method will be an alternative that can break the trade-off relationship between accuracy and speed that is well known in the field of electronic structure calculations. English NATURE PUBLISHING GROUP TRANSITION-METALS POTENTIAL MODEL TOTAL-ENERGY DESIGN Pattern Learning Electronic Density of States Article 10.1038/s41598-019-42277-9 1 SCIENTIFIC REPORTS, v.9 SCIENTIFIC REPORTS 9 scie scopus 000463984600037 2-s2.0-85064248511 Multidisciplinary Sciences Science & Technology - Other Topics Article TRANSITION-METALS POTENTIAL MODEL TOTAL-ENERGY DESIGN Machine learning Principal component analysis Electronic density of states Density functional theory