A Synthesizability-Guided Pipeline for Materials Discovery

TL;DR

This paper introduces a synthesizability score for crystal structures to improve materials discovery, successfully predicting and experimentally synthesizing several new compounds rapidly and efficiently.

Contribution

It develops a combined compositional and structural synthesizability score and demonstrates its effectiveness in predicting synthesizable materials and pathways.

Findings

Successfully synthesized 7 out of 16 predicted compounds

Identified hundreds of highly synthesizable candidates

Completed experimental validation in just three days

Abstract

Computational materials discovery relies on the generation of plausible crystal structures. The plausibility is typically judged through density functional theory methods which, while typically accurate at zero Kelvin, often favor low-energy structures that are not experimentally accessible. We develop a combined compositional and structural synthesizability score which provides an accurate way of predicting which compounds can actually be synthesized in a laboratory. We use it to evaluate non-synthesized structures from the Materials Project, GNoME, and Alexandria, and identified several hundred highly synthesizable candidates. We then predict synthesis pathways, conduct corresponding experiments, and characterize the products across 16 targets, successfully synthesizing 7 of 16. The entire experimental process was completed in only three days. Our results highlight omissions in lists of known synthesized structures, deliver insights into the practical utility of current materials databases, and showcase the central role synthesizability prediction can play in materials discovery.