Computational workflow engine, matched with robotic platform, used to drive experiments for the first time

Computational and experimental strategies in supplies science are sometimes described as completely separate affairs. On one aspect, pc simulations are used to elucidate and predict the properties of supplies, together with novel ones that weren’t but synthesized. On the opposite, experiments check the precise conduct of supplies in managed conditions and serve to substantiate and validate computational predictions.

However the best way ahead for materials science is absolutely to have the 2 strategies go hand in hand, because it occurred in a new publication in Journal of Supplies Chemistry A, co-led by NCCR MARVEL’s members Giovanni Pizzi and Nicola Marzari with Empa’s Corsin Battaglia, and involving researchers from the Paul Scherrer Institute (PSI), Empa, EPFL, ETH Zurich, and Technische Universität Berlin.

The researchers demonstrated how the computational workflow engine AiiDA, developed inside NCCR MARVEL, can be utilized not solely to run simulations, however to run precise experiments, on this case on batteries, from controlling the experimental units, to archiving and analyzing the ensuing knowledge.

“There were several components involved in the setup,” explains Edan Bainglass, a scientist in Giovanni Pizzi’s group at PSI.

“To start with, there’s a robotic, Aurora, that may assemble as much as 36 coin cell batteries with completely different battery elements on the time.” As soon as the batteries are able to go, they’re positioned into racks of battery cyclers that repeatedly cost and discharge the batteries below completely different circumstances.

Subsequent, tomato, an open-source software program instrument developed at Empa, the Swiss Federal Laboratories for Supplies Science and Know-how, is used to manage the operation of cyclers, setting parameters equivalent to voltage and present and placing the batteries by way of the specified cycles of cost and discharge.

“The software takes care of communicating with the cyclers, collecting the data, and parsing the results directly into files that can be further processed,” explains Peter Kraus, important developer of tomato, previously in Empa and now in Technische Universität Berlin.

The core of the brand new research is strictly the mixing of AiiDA with tomato, in such a approach that one can write AiiDA workflows and use AiiDA to manage the biking of batteries. The benefit is that now a set of biking protocols could be assigned per pattern in a biking workflow, and a number of samples could also be submitted at a time, restricted solely by the variety of obtainable cyclers.

“Otherwise, for every experiment and every protocol that you would want to run with tomato, you’d have to do it one by one,” says Bainglass.

To illustrate, for instance, that an experiment implies first placing a battery by way of a formation cycle, to create the passivating electrode/electrolyte interface, after which later making use of long-term biking. As an alternative of organising every of those protocols manually per pattern, AiiDA permits researchers to organize an experiment by way of a graphical interface by packing collectively a number of protocols and submitting them as a batch on as many samples as you may deal with, with numerous checks alongside the best way.

“Plus, you have the usual benefits of AiiDA when it comes to collecting and analyzing the data, including full provenance tracking of the data. Furthermore, thanks to tailored web interfaces (apps) that we developed in AiiDAlab, our overall platform becomes a one-stop shop for batch submission, data collection, provenance tracking, and analysis,” says Bainglass.

“This is the first time that AiiDA is used to drive automated experiments rather than simulations, using the same underlying philosophy, data structure and workflow engine,” notes Giovanni Pizzi.

“Our tight integration of automated simulations and experiments is the first crucial step toward a fully integrated platform enabling in the future fully autonomous self-driving labs.”

Supplied by
Nationwide Centre of Competence in Research (NCCR) MARVEL