Sun Wenhao, David Nicholas
Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA.
Faraday Discuss. 2025 Jan 14;256(0):614-638. doi: 10.1039/d4fd00112e.
Synthesis of predicted materials is the key and final step needed to realize a vision of computationally accelerated materials discovery. Because so many materials have been previously synthesized, one would anticipate that text-mining synthesis recipes from the literature would yield a valuable dataset to train machine-learning models that can predict synthesis recipes for new materials. Between 2016 and 2019, the corresponding author (Wenhao Sun) participated in efforts to text-mine 31 782 solid-state synthesis recipes and 35 675 solution-based synthesis recipes from the literature. Here, we characterize these datasets and show that they do not satisfy the "4 Vs" of data-science-that is: volume, variety, veracity and velocity. For this reason, we believe that machine-learned regression or classification models built from these datasets will have limited utility in guiding the predictive synthesis of novel materials. On the other hand, these large datasets provided an opportunity to identify anomalous synthesis recipes-which in fact did inspire new hypotheses on how materials form, which we later validated by experiment. Our case study here urges a re-evaluation on how to extract the most value from large historical materials-science datasets.
预测材料的合成是实现计算加速材料发现愿景所需的关键和最后一步。由于之前已经合成了如此多的材料，人们可能会预期从文献中挖掘合成方法会产生一个有价值的数据集，用于训练能够预测新材料合成方法的机器学习模型。在2016年至2019年期间，通讯作者（孙文豪）参与了从文献中挖掘31782个固态合成方法和35675个溶液基合成方法的工作。在此，我们对这些数据集进行了表征，并表明它们不满足数据科学的“4V”特性，即：体量、多样性、准确性和速度。因此，我们认为基于这些数据集构建的机器学习回归或分类模型在指导新型材料的预测合成方面效用有限。另一方面，这些大型数据集提供了一个识别异常合成方法的机会——事实上，这确实激发了关于材料形成方式的新假设，我们后来通过实验对这些假设进行了验证。我们在此的案例研究促使人们重新评估如何从大型历史材料科学数据集中提取最大价值。
