Multi-channel machine learning based nonlocal kinetic energy density functional for semiconductors

TL;DR

This paper introduces a multi-channel machine learning-based nonlocal kinetic energy density functional that significantly improves the modeling of semiconductors, especially in capturing covalent bonds, by extending previous models with multiple length-scale channels.

Contribution

The paper presents a novel multi-channel extension of the MPN KEDF, tailored for semiconductors, enhancing accuracy by integrating multiple real-space length scales.

Findings

CPN5 KEDF achieves high accuracy across tested semiconductors.

Multi-channel design improves the modeling of covalent bonds.

The approach offers a new pathway for generating KEDFs for semiconductors.

Abstract

The recently proposed machine learning-based physically-constrained nonlocal (MPN) kinetic energy density functional (KEDF) can be used for simple metals and their alloys [Phys. Rev. B 109, 115135 (2024)]. However, the MPN KEDF does not perform well for semiconductors. Here we propose a multi-channel MPN (CPN) KEDF, which extends the MPN KEDF to semiconductors by integrating information collected from multiple channels, with each channel featuring a specific length scale in real space. The CPN KEDF is systematically tested on silicon and binary semiconductors. We find that the multi-channel design for KEDF is beneficial for machine-learning-based models in capturing the characteristics of semiconductors, particularly in handling covalent bonds. In particular, the CPN5 KEDF, which utilizes five channels, demonstrates excellent accuracy across all tested systems. These results offer a new path for generating KEDFs for semiconductors.