LN02.03.01 : Machine Learning for Accelerated Prediction of Electronic Density of States

5:00 PM–7:00 PM Apr 3, 2018 (America - Denver)

PCC North, 300 Level, Exhibit Hall C-E

Byung Chul Yeo1 Sang Soo Han1

1, Korea Institute of Science and Technology, Seoul, , Korea (the Republic of)

Recently, artificial Intelligence (AI) and machine learning are unlocking the potentials with accelerated prediction of material properties for novel material discovery and design. Electronic density of states (DOS) is a key factor in condensed matter physics that determines the properties of metals. First-principles density-functional theory (DFT) calculations have typically been used to obtain the DOS despite their considerable computation cost. Herein, we report a fast machine-learning method for predicting the DOS patterns of multi-component alloy systems based on a principal component analysis. Within this framework, we input only three features based on the composition and atomic structure: the d-orbital occupation ratio, coordination number, and mixing factor. While the DFT method scales as O(N3), where N is the number of electrons in the system size, our pattern learning method takes only 1 minute on a single CPU core irrespective of N and therefore can scale as O(1). Furthermore, our method provides an accuracy of 91~98 % 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.