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.

翻译：计算材料发现依赖于生成合理的晶体结构。通常通过密度泛函理论方法判断其合理性，该方法虽然在零开尔文温度下通常准确，但往往倾向于预测实验上难以获得的低能结构。我们开发了一种结合成分与结构的可合成性评分方法，能够准确预测哪些化合物可在实验室中实际合成。我们运用该方法评估了来自Materials Project、GNoME和Alexandria数据库中未合成的结构，识别出数百种高可合成性候选材料。随后我们预测了合成路径，开展相应实验，并对16个目标产物的特性进行表征，成功合成了其中7种。整个实验流程仅耗时三天。我们的研究结果揭示了已知合成结构列表中的遗漏，为当前材料数据库的实际效用提供了见解，并展示了可合成性预测在材料发现中的核心作用。