Artificial intelligence and experimental validation reveal atomic-scale basis for improved ‘water-in-salt’ battery performance
UPTON, N.Y. — A team of scientists from the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and Stony Brook University (SBU) used artificial intelligence (AI) to help them understand how zinc-ion batteries work — and potentially how to make them more efficient for future energy storage needs. Their study, published in the journal PRX Energy, focused on the water-based electrolyte that shuttles electrically charged zinc ions through the rechargeable battery during charging and use. The AI model tapped into how those charged ions interact with water under varying concentrations of zinc chloride (ZnCl2), a form of salt with high solubility in water.
The AI findings, validated by experiments at Brookhaven Lab’s National Synchrotron Light Source II (NSLS-II), show why high salt concentrations produce the best battery performance.
“AI is an important tool that can facilitate the advancement of science,” said Esther Takeuchi, chair of the Interdisciplinary Science Department (ISD) at Brookhaven Lab and the William and Jane Knapp Chair in Energy and the Environment at SBU. “The research done by this team provides an example of the insights that can be gained by combining experiment and theory enhanced by the use of AI.”
Amy Marschilok, manager of the Energy Storage Division of ISD and a professor of chemistry at SBU, added, “This work could help advance the development of robust zinc-ion batteries for large-scale energy storage. These batteries are particularly attractive for resilient energy applications because the water-based electrolyte is inherently safe and the materials use to make them are abundant and affordable.”
Water in salt
Like all batteries, zinc-ion batteries convert energy from chemical reactions into electrical energy, explained Deyu Lu, a staff scientist in the Theory and Computation Group of Brookhaven Lab’s Center for Functional Nanomaterials (CFN) who led this research.
“However, competing chemical reactions, such as those that split water molecules and produce hydrogen gas, can severely degrade battery performance,” he said. “If any of this energy is used in side reactions, you lose energy that is supposed to do work.”
Lu and his collaborators knew that previous studies had found that water splitting is suppressed in a special zinc chloride electrolyte where the salt concentration is so high it’s referred to as “water-in-salt,” in contrast to more common “salt-in-water” electrolytes. To figure out why the high-salt version was better, they wanted to capture the atomic-scale details of how zinc and chloride ions move and interact with water — and how that affects the electrolyte’s conductivity — at different salt concentrations.
But seeing these atomic-scale details is extremely challenging. So the team turned to a form of computer modeling enhanced by AI vision.
Developing AI vision
“Seeing these complex details would be impossible using conventional computing techniques,” Lu said. “Conventional simulation methods cannot handle the large number of atomic interactions with the desired accuracy to capture the timescales over which such systems evolve. Such calculations require enormous computing power, which would easily take many years.”
So instead of performing all the complex calculations that would be needed to fully simulate the ions’ interactions with water, the team used conventional simulations to generate a small number of simulation data, known as a “training set,” and fed it to an AI program. They used computing resources at the Theory and Computational Facility at CFN, a DOE Office of Science user facility, and Brookhaven Lab’s Scientific Computing and Data Facilities within the Computing and Data Sciences directorate (CDS).
“We needed a little bit of data collected by calculating a small number of interactions to kickstart the process of training an initial model,” said CDS’s Chuntian Cao, first author on the paper. “Then, we ran the model to generate more data to continue to improve the model’s predictions.”
At each step, the scientists ran their results through an ensemble of machine learning (ML) models to assess whether the predictions were accurate. Lu likened the process to calling several friends to help answer questions on “Who Wants to be a Millionaire,” a once-popular TV game show. “If the friends/models all agree, then it looks like you have good chance that you have an accurate prediction,” he noted.
But, as Cao pointed out, “When we find that some predictions have very large deviations in the ensemble of ML models, we return to doing the conventional calculations to get the correct answer. These new corrected data points are then added back to the training data to further refine the ML model.”
This iterative “active learning” process minimized the number of calculations that needed to be run in a computationally expensive way to complete the training of the ML model. And, after several rounds of training, the AI model could make predictions about much larger numbers of atomic interactions over longer and longer timescales.
“Chuntian ran the simulations with several thousands of atoms, a very large system, for hundreds of nanoseconds — an impossible task using the conventional methods. AI/ML is truly a game changer in the study of complex materials,” Lu said.
Stablizing water
The Brookhaven and Stony Brook scientists’ AI model revealed that high zinc chloride concentrations play the key role in stabilizing water molecules, protecting them from splitting.
In pure water, the oxygen atom in one water molecule (H2O) forms two so-called hydrogen bonds with hydrogen atoms in neighboring water molecules. These hydrogen bonds connect the water moleclues in a continuous network that makes the water molecules more reactive and susceptible to splitting, Lu said.
The team found that the number of hydrogen bonds drops rapidly as the zinc chloride concentration increases, disrupting the hydrogen-bond network. In the water-in-salt regime, only about 20% of the hydrogen bonds are left.
“Stabilizing the water molecules is an essential component of why high-concentration water-in-salt electrolytes work so well,” said Cao.
Read more on NSLS-II website
Image: Scientists used AI to model how zinc and chloride ions (gray and green spheres) at different concentrations would interact with and move through water (oxygen and hydrogen represented by red and white spheres) in an aqueous battery electrolyte. The AI-assisted modeling revealed that a high concentration of zinc chloride salt solution stabilizes water in the electrolyte while maintaining sufficiently high conductivity — characteristics that are essential for aqueous zinc-ion battery performance.
Credit: Chuntian Cao / Brookhaven National Laboratory