Griesemer Sean D, Baldassarri Bianca, Zhu Ruijie, Shen Jiahong, Pal Koushik, Park Cheol Woo, Wolverton Chris
Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA.
Department of Mechanical Engineering and Materials Science, Center for Extreme Materials, Duke University, Durham, NC 27708, USA.
Sci Adv. 2025 Jan 3;11(1):eadq1431. doi: 10.1126/sciadv.adq1431.
The computational search for new stable inorganic compounds is faster than ever, thanks to high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive because of the enormous search space and the cost of DFT calculations. To aid these searches, recommendation engines have been developed. We conduct a systematic comparison of the performance of previously developed recommendation engines, specifically ones based on elemental substitution, data mining, and neural network prediction of formation enthalpy. After identifying ways to improve the recommendation engines, we find the neural network to be superior at recommending stable Heusler compounds. Armed with improved recommendation engines, we identify tens of thousands of compounds that are stable at zero temperature and pressure, now available in the Open Quantum Materials Database. We summarize this diverse pool of compounds, including the elusive mixed anion compounds, and two of their many applications: thermoelectricity and solar thermochemical fuel production.
借助高通量密度泛函理论（DFT），对新型稳定无机化合物的计算搜索比以往任何时候都要快。然而，由于巨大的搜索空间和DFT计算成本，稳定化合物的搜索仍然非常昂贵。为了辅助这些搜索，人们开发了推荐引擎。我们对先前开发的推荐引擎的性能进行了系统比较，特别是基于元素替代、数据挖掘和形成焓神经网络预测的推荐引擎。在确定改进推荐引擎的方法后，我们发现神经网络在推荐稳定的赫斯勒化合物方面表现更优。有了改进后的推荐引擎，我们识别出了数以万计在零温度和压力下稳定的化合物，现在可在开放量子材料数据库中获取。我们总结了这一多样的化合物库，包括难以捉摸的混合阴离子化合物，以及它们的众多应用中的两个：热电和太阳能热化学燃料生产。
