Center for Materials Research by Information Integration (cMI2), Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), 1-2-1 Sengen, Tsukuba, Ibaraki, 305-0047, Japan.
Phys Chem Chem Phys. 2018 Sep 12;20(35):22585-22591. doi: 10.1039/c7cp08280k.
Exploring new liquid electrolyte materials is a fundamental target for developing new high-performance lithium-ion batteries. In contrast to solid materials, disordered liquid solution properties have been less studied by data-driven information techniques. Here, we examined the estimation accuracy and efficiency of three information techniques, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and exhaustive search with linear regression (ES-LiR), by using coordination energy and melting point as test liquid properties. We then confirmed that ES-LiR gives the most accurate estimation among the techniques. We also found that ES-LiR can provide the relationship between the "prediction accuracy" and "calculation cost" of the properties via a weight diagram of descriptors. This technique makes it possible to choose the balance of the "accuracy" and "cost" when the search of a huge amount of new materials was carried out.
探索新型液态电解质材料是开发新型高性能锂离子电池的基本目标。与固态材料相比，无序液态溶液的性质较少通过数据驱动的信息技术进行研究。在这里，我们通过使用配位能和熔点作为测试液体性质，检查了三种信息技术，即多元线性回归（MLR）、最小绝对值收缩和选择算子（LASSO）和具有线性回归的穷举搜索（ES-LiR）的估计准确性和效率。然后，我们确认 ES-LiR 在这些技术中给出了最准确的估计。我们还发现，ES-LiR 可以通过描述符的权重图提供“预测精度”和“计算成本”之间的关系。当对大量新材料进行搜索时，该技术使得在“准确性”和“成本”之间进行权衡成为可能。
