Data from: A polymer dataset for accelerated property prediction and design

Emerging computation- and data-driven approaches are particularly useful for rationally designing materials with targeted properties. Generally, these approaches rely on identifying structure-property relationships by learning from a dataset of sufficiently large number of relevant materials. The learned information can then be used to predict the properties of materials not already in the dataset, thus accelerating the materials design. Herein, we develop a dataset of 1,073 polymers and related materials and make it available at http://khazana.uconn.edu/. This dataset is uniformly prepared using first-principles calculations with structures obtained either from other sources or by using structure search methods. Because the immediate target of this work is to assist the design of high dielectric constant polymers, it is initially designed to include the optimized structures, atomization energies, band gaps, and dielectric constants. It will be progressively expanded by accumulating new materials and including additional properties calculated for the optimized structures provided.新兴的计算与数据驱动方法，在理性设计具备目标性能的材料领域展现出尤为突出的应用价值。通常而言，这类方法需要通过从足够规模的相关材料数据集中学习，来挖掘并确立结构与性能之间的关联关系。习得的知识可用于预测未纳入该数据集的材料的性能，从而加速材料设计流程。本研究中，我们构建了包含1073种聚合物及相关材料的数据集，并将其公开于http://khazana.uconn.edu/ 平台。本数据集采用统一的制备流程：通过第一性原理计算（first-principles calculations）生成相关数据，其材料结构既可取自公开数据源，也可通过结构搜索方法自主获取。鉴于本研究的近期目标是助力高介电常数聚合物的设计开发，该数据集初始收录内容包括优化后的材料结构、原子化能、能带隙以及介电常数。后续将通过持续新增材料条目、为已收录的优化结构补充计算更多性能参数，对本数据集进行逐步扩容。