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Ab initio pseudopotentials are a linchpin of modern molecular and condensedmatter electronic structure calculations. In this work, we employmulti-objective optimization to maximize pseudopotential softness whilemaintaining high accuracy and transferability. To accomplish this, we develop aformulation in which softness and accuracy are simultaneously maximized, withaccuracy determined by the ability to reproduce all-electron energy differencesbetween Bravais lattice structures, whereupon the resulting Pareto frontier isscanned for the softest pseudopotential that provides the desired accuracy inestablished transferability tests. We employ an evolutionary algorithm to solvethe multi-objective optimization problem and apply it to generate acomprehensive table of optimized norm-conserving Vanderbilt (ONCV)pseudopotentials (https://github.com/SPARC-X/SPMS-psps). We show that theresulting table is softer than existing tables of comparable accuracy, whilemore accurate than tables of comparable softness. The potentials thus affordthe possibility to speed up calculations in a broad range of applications areaswhile maintaining high accuracy.

Soft and transferable pseudopotentials from multi-objective optimization