Machine learning models have recently emerged to predict whether hypothetical solid-state materials can be synthesized. These models aim to circumvent direct first-principles modeling of solid-state phase transformations, instead learning from large databases of successfully synthesized materials. Here, we assess the alignment of several recently introduced synthesis prediction models with material and reaction thermodynamics, quantified by the energy with respect to the convex hull and a metric accounting for thermodynamic selectivity of enumerated synthesis reactions. A dataset of successful synthesis recipes was used to determine the likely bounds on both quantities beyond which materials can be deemed unlikely to be synthesized. With these bounds as context, thermodynamic quantities were computed using the CHGNet foundation potential for thousands of new hypothetical materials generated using the Chemeleon generative model. Four recently published machine learning models for synthesizability prediction were applied to this same dataset, and the resultant predictions were considered against computed thermodynamics. We find these models generally overpredict the likelihood of synthesis, but some model scores do trend with thermodynamic heuristics, assigning lower scores to materials that are less stable or do not have an available synthesis recipe that is calculated to be thermodynamically selective. In total, this work identifies existing gaps in machine learning models for materials synthesis and introduces a new approach to assess their quality in the absence of extensive negative examples (failed syntheses).

翻译：近年来，机器学习模型被用于预测假设性固态材料是否可被合成。这些模型旨在绕过对固态相变进行直接第一性原理建模，转而从大规模成功合成材料数据库中学习。本研究评估了若干近期提出的合成预测模型与材料及反应热力学的一致性——以相对于凸包的能量以及考量枚举合成反应热力学选择性的指标进行量化。通过收集一个成功合成配方的数据集，确定了这两个热力学量超出后材料即被认为难以合成的可能界限。以此界限为基准，利用CHGNet基组势计算了由Chemeleon生成模型产生的数千种新型假设性材料的热力学量。将四种近期发表的针对可合成性预测的机器学习模型应用于同一数据集，并将其预测结果与计算热力学数据进行了对比。我们发现这些模型普遍高估了合成可能性，但部分模型得分确实呈现与热力学启发法的关联趋势——对稳定性较差或缺乏热力学上具有可选择性的合成路径的材料赋予较低得分。总体而言，本研究识别了机器学习模型在材料合成领域的现有不足，并引入了一种在缺乏大量负样本（合成失败案例）的情况下评估模型质量的新方法。