IISc researchers use machine learning and amorphous materials to build high energy density batteries

The Indian Science Institute (IISC) researchers built batteries with higher energy density in a new study using machine learning model and amorphous materials.

Lithium ion batteries give strength in most electronics, while the battery has limited energy density to store a certain energy per mass or volume.

“You will have to explore alternative energy storage technologies to store more energy with the same mass or volume, IISC said.

Mr. Gopalakrishnan and his team examined how to increase the movement of ions in magnesium batteries, which can have higher energy density.

In the studies that apply a machine learning model, the team has shown that using amorphous materials as positive electrodes to create these batteries can significantly increase energy transfer rates.

Lithium ion or magnesium batteries contain a positive (cathode) and a negative (anode) electrode separated by a liquid electrolyte. A lithium or magnesium ion is replaced by anota or, on the contrary, with the device.

Twice the amount

“In magnesium batteries, each magnesium atom can actually replace two electrons, while each lithium atom can replace only one electron with an external circuit. So you can approach the amount of energy twice the amount of energy per atom.”

He added that the main bottleneck in commercializing magnesium batteries is that there is no good materials that can serve as cathode.

According to IISC, so far, scientists are looking at crystal materials with a large periodic regulation of atomic regulations. However, since magnesium moves very slowly in these materials, they cannot suck and release magnesium ions sufficiently sufficiently.

The team created the calculation model of an amorphous vanadium pentoxide material and calculated how fast magnesium ions could move.

In order to create such models, scientists typically use a method called the density functional theory (DFT), which typically model systems correctly.

Speed ​​and accuracy

To combine speed and accuracy, the team used a machine learning framework. Firstly, they used the density functional theory (DFT) to create data on how Amorf cathode will work on a small scale.

After training machine learning models on these data, they used the model to make molecular dynamic (MD) simulations.

IISC, “MD can model the material on a larger scale-Magnesium in the amorphous material and how much it moved and how long they took a better picture. The team, compared to the latest technology-product crystal magnesium materials, amorphous form of magnesium movement in the proportion of five size improvement observed.