Marko Petković, Pablo Romero-Marimon, Vlado Menkovski, Sofia Calero
TL;DR本文基于 Deep Learning 方法，开发一个新模型，能够更加有效的预测含有孔隙结构的晶体物质的热吸附性能，该模型能够考虑到晶体的空间对称性和孔隙结构，并且在实验验证中表现良好。
Abstract
Efficiently predicting properties of porous crystalline materials has great
potential to accelerate the high throughput screening process for developing
new materials, as simulations carried out using first principles model are
often computationally expensive. To effectively make use o