AI-enhanced discovery and accelerated synthesis of metal phosphosulfides

TL;DR

This paper combines computational predictions, machine learning, and high-throughput synthesis to discover and develop new metal phosphosulfides efficiently, overcoming traditional experimental challenges in this complex material class.

Contribution

It introduces a comprehensive workflow integrating theory, AI, and experimental methods for rapid discovery and synthesis of phosphosulfides, including the first Si- and Ge-based compounds.

Findings

Identified 19 new thermodynamically stable phosphosulfides.

Developed a machine learning model for accurate band gap prediction.

Successfully synthesized four new phosphosulfides in four experiments.

Abstract

Metal phosphosulfides have emerged as unique multifunctional materials, but they present unique synthesis challenges compared to more established material classes such as oxides and nitrides. As a consequence, experimental development and theoretical understanding of phosphosulfides have focused on individual compounds rather than on accelerated broad-range exploration. In this work, we first evaluate the synthesizability and band gaps of 909 hypothetical ternary phosphosulfides by density functional theory. We find 19 previously unknown thermodynamically stable compounds, including the first Si- and Ge-based phosphosulfides. For rapid band gap prediction, we then develop a multi-fidelity machine learning model to translate semilocal density functional theory band gaps into experimentally calibrated band gaps. Importantly, we extend the accelerated material development workflow to the experimental domain by demonstrating a route to high-throughput synthesis and characterization of virtually any phosphosulfide material system. The method is based on thin-film combinatorial libraries and yields over 100 unique compositions in each experiment, enabling us to synthesize four distinct phosphosulfide compounds in only four combinatorial experiments without prior synthesis recipes and without compromising on material quality. Thus, we argue that accelerated materials development workflows combining theory, artificial intelligence, synthesis, and characterization can be viable even for experimentally challenging inorganic materials.