Discovering new materials with the AiiDAlab platform: examples from a combined computational/experimental research laboratory

In Computational Materials Science, discovery of new materials often entails complex workflows - sequences of interdependent computational tasks. As they get more convoluted, the risk that such sequences remain usable only by a minority of experts increases. It becomes essential to provide an environment that enables their definition and deployment in a manner advocated by open science standards, facilitating reproducibility, sharing of data as well as dissemination of software.
AiiDAlab[1] is a web platform that enables computational scientists to package scientific workflows and computational environments and share them with their collaborators and peers.
By leveraging the AiiDA[2] workflow manager and its plugin ecosystem, developers get access to a growing range of simulation codes through a python API, coupled with automatic provenance tracking of simulations for full reproducibility.
I will show examples[3,4] from our laboratory at Empa where cooperation between experiment and simulation in the discovery of new materials is boosted by the AiiDAlab applications.
[1]A.Yakutovich et al. Comp. Mat. Sci (2020) submitted (arXiv arXiv:2010.02731)
[2]G. Pizzi et al. Comp. Mat. Sci. 111, 218 (2016)
[3]Q. Sun et al. Nano Lett. 9, 6429 (2020)
[4]Q.Sun et al. Adv. Mater. 32, 1906054 (2020)