Alán Aspuru-Guzik

TL;DR: This work highlights how hybrid quantum-classical approaches to quantum simulation and quantum machine learning could yield substantial progress using noisy-intermediate scale quantum devices, whereas fault-tolerant, error-corrected quantum computers are still in their development phase.

TL;DR: This study introduces the Quantum MOF (QMOF) database, a publicly available database of computed quantum-chemical properties for more than 14,000 experimentally synthesized MOFs and demonstrates how machine learning models trained on the QMOF database can be used to rapidly discover MOFs with targeted electronic structure properties.

TL;DR: A review of machine learning-assisted nanoparticle synthesis can be found in this article, which discusses different machine learning approaches for the synthesis of semiconductor, metal, carbon-based and polymeric nanoparticles.

TL;DR: This work has explored the maximum Na content in P2-type layered oxides and discovered that the high-content Na in the host enhances the structural stability and promotes the oxidation of low-valent cations to their high oxidation states (in this case Ni2+).