论文标题
大型虚拟电池材料数据库开发的新广义信息学框架
New Generalized Informatics Framework for Development of Large Scale Virtual Battery Material Databases
论文作者
论文摘要
在本文中，我们介绍了一种预测与电池材料相关的超过100,000种尖晶石化合物的能力的方法，我们从中提出了20种最有希望的候选材料。在电池设计中，很难选择适当的材料，因为有很多指标需要考虑，包括能力是基本的工程属性。使用报告的实验数据作为起点，我们演示了如何构建一个数据集，该数据集为选择电池材料的选择提供指南。尽管我们专注于与电池中用途相关的电极材料的基于LI的尖晶石结构的容量，但此处开发和证明的方法可以适应其他特性，结构和现场占用。此外，理论能力通常用作电池材料材料设计的指南。在本文中，我们展示了这不足以表示实验测量值，而我们的方法论缩小了这一差距并提供了实验数据的准确计算表示。
In this paper, we introduce an approach for the prediction of capacity for over 100,000 spinel compounds relevant for battery materials, from which we propose the 20 most promising candidate materials. In the design of batteries, selecting the proper material is difficult because there are so many metrics to consider, including capacity which is a fundamental engineering property. Using reported experimental data as our starting point, we demonstrate how we can build a dataset that provides a guide for the selection of battery materials. Although we focus on capacity of Li based spinel structures for electrode materials relevant for usage in batteries, the methodology developed and demonstrated here can be adapted to other properties, structures, and site occupancies. Further, theoretical capacity is often used as a guideline for material design of battery materials. In this paper, we show how this is insufficient for representing experimental measurements, while our methodology closes this gap and provides an accurate computational representation of experimental data.
