Inverse design of porous materials using artificial neural networks
(PHYSorg.com Chemistry)

The ability to generate optimized nanomaterials with artificial neural networks can significantly revolutionize the future of materials design in materials science. While scientists had progressively created small and simple molecules, complex crystalline porous materials remain to be generated using neural networks. In a recent report on Science Advances, Baekjun Kim and a team of researchers in the Department of Chemical and Biomolecular Engineering at the Korea Advanced Institute of Science and Technology, Republic of Korea, implemented a generative adversarial network.