Electrolyte genome to accelerate battery discovery
The Electrolyte Genome is essentially a database of molecules. It enables battery scientists looking for potential electrolytes to specify the desired parameters and properties, and the database returns a short list of promising candidate molecules. This has the potential to speed up the discovery timeline dramatically.
‘Electrolytes are a stumbling block for many battery technologies, whether the platform is designed for electric vehicles or a flow battery for grid applications,’ said Persson. ‘What we can do is calculate the properties of a large number of molecules and give experimentalists a much better set of materials to work with than if they were to explore all possible combinations.’
Lawrence Berkeley National Laboratory scientist Kristin Persson who led the project stressed that the new system can be used to take some of guesswork out of the discovery process but besides being faster and more efficient in screening out bad candidates, the Electrolyte Genome could help scientists generate novel ideas and help then to understand the chemical interactions when things do not go to plan.
‘It adds explanations to why certain things work or don’t work,’ Persson said. ‘Frequently we rely on trial and error. Having an explanation becomes very useful - we can apply the principles we’ve learned to future guesses. So the process becomes knowledge-driven rather than trial and error.’
The Electrolyte Genome uses the infrastructure of the Materials Project, a database of calculated properties of thousands of known materials, co-founded by Persson and Gerbrand Ceder of MIT. The researchers apply a funnel idea, doing a first screening of materials by applying a series of first principles calculations for properties that can be calculated quickly and robustly.
This whittles down the candidate pool, on which they do a second screening for another property, and so on.
‘If we can come up with an electrolyte that has a higher electrochemical window for multivalent batteries, or with larger solubility for certain redox molecules, if we can solve either of these, you suddenly enable the whole industry,’ Persson said. ‘It could be a game-changer.’
The concept was described in a recent essay in The Journal of Physical Chemistry Letters co-authored by Persson and her collaborators at Berkeley Lab and Argonne National Laboratory.
Once a shortlist of candidate molecules has been generated researchers can perform a more detailed computational evaluations, applying molecular dynamics simulations or other calculations as needed, for example to characterise the interactions of the different components.
The number of possible combinations of salts and solvents is almost infinite, plus impurities play a role. So Persson and her team do work closely with experimentalists to guide their research once candidates have been selected.
‘Because the space is so vast, we typically don’t throw the whole kitchen sink at it because it would take forever,’ Persson said. ‘We tend to take some base molecule or some idea, then we explore all the variations on that idea. That’s the way to attack it.’
The methodology has been validated with known electrolytes. Using the supercomputers at NERSC, the researchers can screen hundreds of molecules per day.
To date, more than 15,000 molecules for electrolytes—including 10,000 redox active molecules, hundreds of conductive network molecules, and salts, solvents, and more—have been calculated. Screening such quantities of molecules for suitable properties using traditional synthesis and testing techniques would take decades.
The Electrolyte Genome’s first major scientific finding—that magnesium electrolytes are very prone to forming ion pairs, which impacts several crucial aspects such as conductivity, charge transfer and stability of the electrolyte—was published in February in the Journal of the American Chemical Society.
The Electrolyte Genome is funded by the Joint Center for Energy Storage Research (JCESR), a US Department of Energy multi-partner Energy Innovation Hub announced in 2012, led by Argonne National Laboratory and including Berkeley Lab.