Nowadays there are algorithms, methods, and platforms that are being created to accelerate the discovery of materials that are able to absorb or adsorb $CO_2$ molecules that are in the atmosphere or during the combustion in power plants, for instance. In this work an asynchronous REST API is described to accelerate the creation of Carbon figures of merit knowledge, called Carbon Tables, because the knowledge is created from tables in scientific PDF documents and stored in knowledge graphs. The figures of merit knowledge creation solution uses a hybrid approach, in which heuristics and machine learning are part of. As a result, one can search the knowledge with mature and sophisticated cognitive tools, and create more with regards to Carbon figures of merit.

翻译：目前,正在创建各种算法、方法和平台,以加速发现能够吸收或吸附2美元二氧化碳分子的材料,如在大气中或发电厂燃烧期间。 在这项工作中,人们描述了一种非同步的REST API, 以加速创造优点知识的碳数字,称为碳表,因为知识来自科学的PDF文件的表格,并存储在知识图中。 创造价值知识的解决方案采用混合方法,其中超自然学和机器学习是其中的一部分。 因此,人们可以用成熟和先进的认知工具搜索知识,并创造更多关于优点的碳数字。