通过机器学习筛选多源数据，以使其更安全的电池材料

On June 14, 2016, RoboSimian, an ape-like robot built by Jet Propulsion Laboratory researchers to rescue people from disaster areas, exploded in the lab and caught fire. The following year, a major cell phone manufacturer issued a global recall of its new tablets after reports of fires and explosions. Since then, there have been numerous accounts of similar incidents. In each case, lithium-ion batteries were identified as the root cause.

The problem with these batteries is their liquid electrolytes, which tend to vaporize or catch fire if a battery-powered device can't cool off quickly enough. Researchers are searching for solid electrolyte materials with good ionic conductivity and electrochemical stability to replace these potentially dangerous liquid electrolytes, but the search has been slow-going. It can take weeks to evaluate a single candidate material through experimentation or simulation, and there are more than 12,000 lithium-containing crystalline solids in the Materials Project database that could be promising candidates—not to mention the many thousands or millions of materials not yet cataloged.

使用MATLAB开发的机器学习模型^®, my colleagues and I found the needle in the haystack: a handful of exceptional solid electrolytes out of the more than 12,000 that we analyzed. Trained on a set of known good electrolytes and their atomistic structures, our MATLAB model appears to be over three times more likely to identify promising new materials than random guessing, and two times more likely than Stanford graduate students working in the field.

从多个来源组装数据

在埃文·里德（Evan Reed）教授的监督下，我们首先汇总了来自三个来源的数据：材料项目数据库，已发表论文和无机晶体结构数据库（ICSD），这是一个实验验证的原子结构的在线数据库。

首先，我们在材料项目数据库中确定了所有12,831个含锂的固体。在筛选结构稳定性，化学稳定性和低电子电导率后，我们消除了该初始组的92％以上。此外，我们汇编了有关材料的地球丰度及其预测成本的信息。这种最初的筛选使我们拥有300多种稳定的候选材料，如果只有其锂电导率足够快，则可能有望具有固体电解质材料。为此，我们转向机器学习。[1]

We began by combing through the scientific literature to find 40 solid crystalline materials for which researchers had characterized the crystal structure and measured the ionic conductivity at room temperature. About one-third of these 40 materials had sufficient ionic conductivity to be useful battery electrolytes, although these materials all have stability issues that prevent them from being adopted in solid state batteries. This mix of 40 fast and slow lithium-conducting materials would serve as a training set for a machine learning algorithm to rapidly predict the lithium conduction behavior in new materials.

然后，我们从ICSD下载了这40种材料的原子结构。使用这些数据，我们计算了20个特征，这些功能根据结构中原子的原子的位置，质量，电源质量和原子半径来表征每个晶体中局部原子排列和化学的特征。这些计算均在MATLAB中进行。我们选择的20个功能包括原子指标，例如每个原子的体积，锂键离子，锂邻居的数量和最小阴离子 - 氨基分离距离。我们认为，这20个功能可能基于我们的直觉或文献中的先前报告，与离子电导率相关。我们发现，在将机器学习应用于如此小的数据集时，使用此类“智能”功能（即基于材料物理学的知识的特征）至关重要。

选择机器学习模型

The next question was: Which combination of these 20 features would best predict the training data? Given our relatively small training set of 40 materials and just 20 features, and the ease and flexibility in modeling offered by MATLAB, we were able to consider more than 10,000,000 possible combinations of features and models.

统计和机器学习工具箱™使探索这些众多模型变得容易，包括最小二乘回归，健壮的回归，本地加权的最小二乘，SVM，逻辑回归和多类分类。我们为我们要测试的每个机器学习算法培训了一个模型，然后验证了算法与我们的培训数据的准确性。

仅凭原子特征训练的模型都没有为离子电导率提供足够的预测能力，但是多次模型确实可以。最终，我们确定了一个具有五个功能的最佳逻辑回归模型，该模型能够将训练集材料分类为10％的交叉验证误差。这对我们来说很有意义，因为逻辑回归分类器往往在像我们这样的小型培训集中表现良好。这种逻辑回归分类器将给出二元预测：该材料是否表现出足够的锂电导率，可以作为固体电解质材料有用？我们训练有素的模型准确地预测了10次。

We then turned this trained model loose on our 300+ remaining candidate materials (Figure 1).

分类器使我们能够消除93.3％的候选材料，从最初的12,831中只剩下21名潜在候选人。一旦训练了模型，此筛选步骤只需几秒钟即可完成。总而言之，我们通过筛选过程消除了99.8％的候选材料。

结果和下一步

为了测试预测的有效性，我们使用准确但缓慢的基于量子物理学的模拟在这些材料中模拟了锂传导。[2]到目前为止，我们发现，当我们遵循基于机器学习模型的建议时，我们发现新的锂离子导电材料的速度比使用简单的试用和错误要快三倍。我们甚至通过向模型和一组斯坦福博士提供相同的随机绘制材料清单来测试针对人类直觉的模型。材料科学的学生。该模型的准确性是学生在不到一千分之一的时间做出预测的同时进行预测的两倍。

我们模型确定的一些候选材料是完全出乎意料的。这些材料的原子结构是如此复杂，以至于我们没有科学的直觉来帮助我们确定材料是否具有足够的离子电导率。正如模型所预测的那样，事实证明他们确实进行了行为，它有助于验证我们的直觉。现在，我们可以将我们学到的知识纳入MATLAB机器学习模型的未来版本，我们希望随着更多的实验数据的报告，我们期望这将有所改善。我们发现的材料之一是如此令人兴奋，以至于我们为其申请了专利，并立即找到了一个感兴趣的公司合作伙伴来许可该专利并继续研究该材料。

We continue to perform some of these examinations, both here at Stanford and in collaboration with outside groups that are conducting studies on individual candidate materials. In the near future, one of these candidate materials may prove to be the solid electrolyte that replaces liquid electrolytes in lithium-ion batteries and makes exploding battery packs a thing of the past.

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