Artificial Intelligence and Experimental Validation Reveal the Atomic-Scale Basis for Improving the Performance of “Water-in-Salt” Batteries<\/p>\n\n\n\n
Upton, New York\u2014A team of scientists from Brookhaven National Laboratory and Stony Brook University have used artificial intelligence (AI) to gain insights into how zinc-ion batteries work and explore ways to improve their efficiency to meet future energy storage needs. Their findings, published in the journal *PRX Energy*, focus on the water-based electrolyte responsible for transporting charged zinc ions during charging and use. The AI \u200b\u200bmodel analyzed how these charged ions interact with water in solutions of zinc chloride (ZnCl\u2082, a water-soluble salt) at different concentrations.<\/p>\n\n\n\n
The AI \u200b\u200bfindings were experimentally validated at the National Synchrotron Radiation Facility II (NSLS-II) at Brookhaven National Laboratory, demonstrating why high salt concentrations produce optimal battery performance.<\/p>\n\n\n\n
\u201cArtificial intelligence is a crucial tool for advancing scientific progress,\u201d said Esther Takeuchi, Director of the Division of Interdisciplinary Sciences at Brookhaven National Laboratory and William and Jane Knapp Professor of Energy and Environment at Stony Brook University. \u201cThis team\u2019s findings demonstrate the profound insights that can be gained by combining experimentation and theory with artificial intelligence.\u201d<\/p>\n\n\n\n
Amy Marschilok, Professor of Chemistry at Stony Brook University (SBU) and Manager of the Energy Storage Division at ISD, added, \u201cThis research helps advance the development of robust zinc-ion batteries for large-scale energy storage. These batteries are particularly attractive for applications requiring highly reliable energy because their water-based electrolytes are inherently safe, and the materials used to manufacture them are abundant and inexpensive.\u201d<\/p>\n\n\n\n
Deryu Lu, a scientist in the Theoretical and Computational Group at the Center for Functional Nanomaterials (CFN) at Brookhaven National Laboratory who led the research, explained that zinc-ion batteries, like all batteries, convert the energy generated by chemical reactions into electrical energy.<\/p>\n\n\n\n
\u201cHowever, competing chemical reactions, such as the splitting of water molecules to produce hydrogen, can severely degrade battery performance,\u201d he said. \u201cIf this energy is used for side reactions, then the energy that should have been used for work is lost.\u201d<\/p>\n\n\n\n
Lu and his collaborators knew that previous research had found that the water-breaking reaction was inhibited in a special zinc chloride electrolyte. This electrolyte had a very high salt concentration and was called a \u201cwater-in-salt\u201d electrolyte, in contrast to the more common \u201csalt-in-water\u201d electrolyte. To investigate why the high-salt electrolyte was superior, they wanted to capture the atomic-scale details of how zinc and chloride ions move and interact with water at different salt concentrations, and how this interaction affects the electrolyte’s conductivity.<\/p>\n\n\n\n
However, observing these atomic-scale details is extremely difficult. Therefore, the research team turned to a computer modeling method enhanced by artificial intelligence vision.<\/p>\n\n\n\n
Developing AI Vision<\/p>\n\n\n\n
Professor Lu stated, \u201cThese complex details cannot be observed using traditional computing techniques. Traditional simulation methods cannot handle the large number of atomic interactions with the required precision, thus failing to capture the timescale of evolution in such systems. Such calculations require enormous computing power and can easily take years.\u201d<\/p>\n\n\n\n
Therefore, instead of performing all the complex calculations required to simulate ion-water interactions, the research team used traditional simulation methods to generate a small amount of simulated data (called a \u201ctraining set\u201d) and fed it into the AI \u200b\u200bprogram. They utilized computing resources at the Theory and Computation Facility (CFN) (a user facility of the U.S. Department of Energy\u2019s Office of Science) and the Scientific Computing and Data Facility (CDS) within Brookhaven National Laboratory\u2019s Division of Computation and Data Science (CDS).<\/p>\n\n\n\n
\u201cWe need a small amount of data, collecting data by computing a small number of interactions to initiate the initial model training process,\u201d said Cao Chuntian of CDS, the paper\u2019s first author. \u201cThen, we run the model to generate more data, continuously improving the model\u2019s predictive capabilities.\u201d<\/p>\n\n\n\n
At each step, the scientists fed the results into a set of machine learning (ML) models to evaluate the accuracy of the predictions. Lu likened the process to calling several friends and asking them to help answer questions from the once-popular TV game show “Who Wants to Be a Millionaire?”. “If the friends\/models all agree, then your prediction is likely accurate,” he noted.<\/p>\n\n\n\n
But as Cao pointed out, “When we find that some predictions in the machine learning model ensemble are significantly biased, we re-perform traditional calculations to get the correct answers. Then, we add these new corrected data points back into the training data to further improve the machine learning model.”<\/p>\n\n\n\n
This iterative “active learning” process minimizes the high computational demands required to train the machine learning model. After several rounds of training, the AI \u200b\u200bmodel is able to predict interactions between a larger number of atoms over longer timescales.<\/p>\n\n\n\n
“Springfield performed simulations for hundreds of nanoseconds on a massive system of thousands of atoms\u2014a task that would have been impossible using traditional methods. AI\/machine learning has truly transformed the landscape of complex materials research,” Lu said.<\/p>\n\n\n\n
Stabilizing Water AI models developed by scientists at Brookhaven National Laboratory and Stony Brook University show that high concentrations of zinc chloride play a crucial role in stabilizing water molecules and preventing their fragmentation.<\/p>\n\n\n\n
Professor Lu explained that in pure water, the oxygen atom in a water molecule (H\u2082O) forms two hydrogen bonds with the hydrogen atom in an adjacent water molecule. These hydrogen bonds connect water molecules into a continuous network, making them more reactive and easier to break down.<\/p>\n\n\n\n
The research team found that as the concentration of zinc chloride increases, the number of hydrogen bonds decreases rapidly, disrupting the hydrogen bond network. In the water-in-salt system, only about 20% of the hydrogen bonds remain.<\/p>\n\n\n\n
Cao said, “Stabilizing water molecules is the key factor behind the remarkable effect of high-concentration water-in-salt electrolytes.”Browse the great range of batteries & chargers at Batterypcs.co.uk in UK.Low prices, big inventory, expert advice. Find your battery here!”<\/a><\/u><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"