Pattern Learning Electronic Density of States

Abstract

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 pattern 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 scale as O(1) regardless of N. Furthermore, our method provides a pattern similarity 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.

arXiv comment8 pages, 5 figures

References in corpus (14)

• Quantum Machine Learning
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Machine learning phases of matter
• Big Data of Materials Science - Critical Role of the Descriptor
• Learning phase transitions by confusion
• By-passing the Kohn-Sham equations with machine learning
• Finding Density Functionals with Machine Learning
• How to represent crystal structures for machine learning: towards fast prediction of electronic properties
• Tight-binding analysis of the electronic structure of dilute bismide alloys of GaP and GaAs
• Fast Algorithm for Finding the Eigenvalue Distribution of Very Large Matrices
• Challenges in Large Scale Quantum Mechanical Calculations
• Machine learning for many-body physics: The case of the Anderson impurity model
• Computational Complexity in Electronic Structure
• Self-consistent supercell approach to alloys with local environment effects

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