arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.04075cond-mat.mtrl-scics.LG
收藏

固态合成预测机器学习模型的热力学评估

Thermodynamic assessment of machine learning models for solid-state synthesis prediction

University of Minnesota（明尼苏达大学）

Jane Schlesinger, Simon Hjaltason, Nathan J. Szymanski, Christopher J. Bartel

阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

AI总结：

评估了机器学习模型预测固态材料合成可行性的热力学一致性，发现模型普遍高估合成可能性，但部分分数与热力学启发式趋势一致。

摘要：

机器学习模型最近被用于预测假设的固态材料是否可合成。这些模型旨在绕过固态相变的直接第一性原理建模，而是从成功合成材料的大型数据库中学习。在这里，我们评估了几个最近引入的合成预测模型与材料和反应热力学的对齐程度，通过相对于凸包的能量和考虑枚举合成反应的热力学选择性的度量来量化。使用成功合成配方的数据集确定了这两个量的可能界限，超出该界限的材料被认为不太可能被合成。以这些界限为背景，使用CHGNet基础势计算了通过Chemeleon生成模型生成的数千种新假设材料的热力学量。将四个最近发表的用于可合成性预测的机器学习模型应用于同一数据集，并将所得预测与计算的热力学进行比较。我们发现这些模型普遍高估了合成的可能性，但一些模型分数确实与热力学启发式趋势一致，对稳定性较差或没有计算为热力学选择性的可用合成配方的材料分配较低的分数。总的来说，这项工作识别了机器学习模型在材料合成中存在的差距，并引入了一种在缺乏大量负例（失败合成）的情况下评估其质量的新方法。

英文摘要：

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).

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑