Litcius/Paper detail

Inverse design of nanoporous crystalline reticular materials with deep generative models

Zhenpeng Yao, Benjamín Sánchez-Lengeling, N. Scott Bobbitt, Benjamin J. Bucior, Sai Govind Hari Kumar, Sean P. Collins, Thomas D. Burns, Tom K. Woo, Omar K. Farha, Randall Q. Snurr, Alán Aspuru‐Guzik

2021Nature Machine Intelligence379 citationsDOIOpen Access PDF

Topics & Concepts

AutoencoderNanoporousComputer scienceNanotechnologyMaterials scienceArtificial intelligenceDeep learningMetal-Organic Frameworks: Synthesis and ApplicationsMachine Learning in Materials ScienceCatalysis and Oxidation Reactions
Inverse design of nanoporous crystalline reticular materials with deep generative models | Litcius