面向大规模电池材料虚拟数据库开发的广义信息学框架
New Generalized Informatics Framework for Development of Large Scale Virtual Battery Material Databases
摘要：
在本文中，我们介绍了一种预测超过10万种与电池材料相关的尖晶石化合物容量的方法，从中我们提出了20种最有前途的候选材料。在电池设计中，选择合适的材料是困难的，因为有太多的指标需要考虑，包括容量，这是一个基本的工程特性。使用报告的实验数据作为我们的出发点，我们演示了如何构建一个数据集，为电池材料的选择提供指导。虽然我们专注于锂基尖晶石结构的容量相关的电极材料在电池中的使用，在这里开发和演示的方法可以适应其他属性，结构和占位。此外，理论容量通常用作电池材料的材料设计的指导。在本文中，我们展示了这是如何不足以代表实验测量，而我们的方法弥补了这一差距，并提供了一个准确的计算表示的实验数据。
英文摘要：
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.
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