ML-guided screening of chalcogenide perovskites as solar energy materials

TL;DR

This paper introduces a data-driven framework combining machine learning and experimental data to identify stable, feasible, and sustainable chalcogenide perovskites for photovoltaic applications.

Contribution

It develops a new tolerance factor via SISSO, integrates multiple predictive models, and provides a multi-objective ranking method for chalcogenide perovskites.

Findings

Identified promising new chalcogenide perovskites for solar energy.

Derived a more accurate tolerance factor for perovskite stability.

Established a transferable screening strategy balancing performance and sustainability.

Abstract

Chalcogenide perovskites have emerged as promising absorber materials for next-generation photovoltaic devices, yet their experimental realization remains limited by competing phases, structural polymorphism, and synthetic challenges. Here, we present a fully data-driven and experimentally grounded screening and ranking framework to assess the stability and experimental feasibility of chalcogenide perovskites, integrating interpretable analytical descriptors, machine-learning models, and sustainability metrics. Using a curated experimental dataset of halide and chalcogenide compounds, we derive a new tolerance factor via the SISSO (sure independence screening and sparsifying operator) algorithm that more accurately distinguishes perovskite-forming compositions than established tolerance-factor-based screening criteria. This descriptor is combined with generative crystal structure prediction, composition-based bandgap estimation, and machine-learning-based feasibility assessment to systematically explore a wide chemical space of hypothetical chalcogenide perovskites. The resulting candidates are further evaluated using sustainability indicators, enabling multi-objective ranking tailored to both single-junction and tandem photovoltaic architectures. Beyond identifying several promising and previously unexplored chalcogenide perovskites, this work demonstrates a transferable screening strategy for chemically constrained materials spaces that balances optoelectronic performance, experimental viability, and long-term sustainability.