Soft and transferable pseudopotentials from multi-objective optimization

TL;DR

This paper introduces a multi-objective optimization approach to generate pseudopotentials that are both softer and highly accurate, improving computational efficiency in electronic structure calculations.

Contribution

It develops a novel multi-objective optimization framework using evolutionary algorithms to produce a comprehensive set of optimized pseudopotentials with enhanced softness and transferability.

Findings

The new pseudopotentials are softer than existing ones with similar accuracy.

They are more accurate than pseudopotentials with comparable softness.

The approach enables faster electronic structure calculations without sacrificing accuracy.

Abstract

Ab initio pseudopotentials are a linchpin of modern molecular and condensed matter electronic structure calculations. In this work, we employ multi-objective optimization to maximize pseudopotential softness while maintaining high accuracy and transferability. To accomplish this, we develop a formulation in which softness and accuracy are simultaneously maximized, with accuracy determined by the ability to reproduce all-electron energy differences between Bravais lattice structures, whereupon the resulting Pareto frontier is scanned for the softest pseudopotential that provides the desired accuracy in established transferability tests. We employ an evolutionary algorithm to solve the multi-objective optimization problem and apply it to generate a comprehensive table of optimized norm-conserving Vanderbilt (ONCV) pseudopotentials (https://github.com/SPARC-X/SPMS-psps). We show that the resulting table is softer than existing tables of comparable accuracy, while more accurate than tables of comparable softness. The potentials thus afford the possibility to speed up calculations in a broad range of applications areas while maintaining high accuracy.