Melting Si: beyond density functional theory

TL;DR

This paper uses the random phase approximation (RPA) to accurately predict silicon's melting point, outperforming traditional density functional theory methods and highlighting RPA's potential for finite temperature property predictions.

Contribution

The study demonstrates that RPA can reliably predict silicon's melting point, surpassing density functional theory approximations and establishing a benchmark for functional development.

Findings

RPA predicts silicon melting point within 3% of experimental value.

Standard DFT underestimates melting point by 200 K.

Hybrid functionals overestimate melting point by 150 K.

Abstract

The melting point of silicon in the cubic diamond phase is calculated using the random phase approximation (RPA). The RPA includes exact exchange as well as an approximate treatment of local as well as non-local many body correlation effects of the electrons. We predict a melting temperature of about 1735 K and 1640 K without and with core polarization effects, respectively. Both values are within 3 % of the experimental melting temperature of 1687 K. In comparison, the commonly used gradient approximation to density functional theory predicts a melting point that is 200 K too low, and hybrid functionals overestimate the melting point by 150 K. We correlate the predicted melting point with the energy difference between cubic diamond and the beta-tin phase of silicon, establishing that this energy difference is an important benchmark for the development of approximate functionals. The current results establish that the RPA can be used to predict accurate finite temperature properties and underlines the excellent predictive properties of the RPA for condensed matter.