Soft and transferable pseudopotentials from multi-objective optimization

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.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this study, we use multi-objective optimization to maximize the softness of pseudopotentials while maintaining high accuracy and transferability. An evolutionary algorithm is employed to solve the optimization problem and generate a comprehensive table of optimized pseudopotentials. The results show that these pseudopotentials are softer and more accurate compared to existing tables, offering the potential to speed up calculations in various applications.