  Novel halide perovskites with improved stability and optoelectronic
properties can be designed via composition engineering at cation and/or anion
sites. Data-driven methods, especially high-throughput first principles
computations and subsequent analysis based on unique materials descriptors, are
key to achieving this goal. In this work, we report a density functional theory
(DFT) based dataset of 495 $ABX_3$ halide perovskite compounds, with various
atomic and molecular species considered at A, B and X sites, and different
amounts of mixing applied at each site using the special quasirandom structures
(SQS) approach for alloys. We perform GGA-PBE calculations on all 495
pseudo-cubic perovskite structures and around 250 calculations using the HSE06
functional, with and without spin-orbit coupling, both including geometry
optimization and static calculations on PBE optimized structures. Lattice
constants, decomposition energy, band gap, and theoretical photovoltaic
efficiency, are computed using each level of theory, and comparisons are made
with collected experimental values. Trends in the data are unraveled in terms
of the effects of mixing at different sites, fractions of specific elemental or
molecular species present in the compound, and averaged physical properties of
species at different sites. We perform screening across the perovskite dataset
based on multiple definitions of tolerance factors, deviation from cubicity,
computed stability and optoelectronic properties, leading to a list of
promising compositions and design principles for achieving multiple desired
properties. Our multi-objective, multi-fidelity, computational halide
perovskite alloy dataset, one of the most comprehensive to date, is available
open-source, and currently being used to train predictive and optimization
models for accelerating the design of novel compositions for superior
optoelectronic applications.
