Perovskite Discovery

Abstract

Current inverse materials discovery methods face a trade-off between broad exploration of chemical space and control over chemical validity, synthesisability, and target properties. Here, we present the COMPosition Aware Search Strategy (COMPASS), a constrained multi-objective, multi-fidelity search framework for crystalline composition spaces. COMPASS introduces a discrete mixed-site encoding for material families with fixed site stoichiometries and up to two species mixed on each crystallographic site. This encoding preserves chemical identity, allowing empirical chemical rules and property constraints to be evaluated directly during optimisation. COMPASS combines fast composition-only screening with a constrained genetic algorithm, structure-based verification using machine-learning interatomic potentials, and active learning to improve the low-fidelity model. Applied to mixed-site ABX3 perovskites, COMPASS identifies 15,922 computationally promising candidates satisfying chemical, novelty, stability, and band-gap criteria. In the same constrained discovery task, COMPASS achieves an approximately two-orders-of-magnitude higher yield of desired candidates than the tested open-source MatterGen baselines [Zeni et al., Nature, 2025, 639, 624--632]. These results position COMPASS as a framework for chemically well-understood discovery problems where chemical constraints can guide search through large composition spaces.