Building Better Batteries with Amorphous Materials and Machine Learning

Published on Quantum Server Networks

Amorphous materials and machine learning for better batteries

From smartphones to electric vehicles, modern life relies heavily on rechargeable batteries. While lithium-ion batteries dominate today’s energy storage market, their limited energy density poses a challenge for powering the next generation of high-performance devices and vehicles. Researchers at the Indian Institute of Science (IISc) have now demonstrated a promising new approach: using amorphous materials as electrodes for magnesium batteries, guided by powerful machine learning models.

Why Look Beyond Lithium?

Lithium-ion technology has been incredibly successful, but its energy capacity is reaching practical limits. In contrast, magnesium batteries offer a tantalizing alternative: each magnesium atom can exchange two electrons, compared to just one for lithium. This means that, in theory, magnesium batteries can deliver nearly twice the energy density per atom.

The main obstacle lies in the cathode materials. Crystalline materials, with their ordered atomic structures, restrict the movement of magnesium ions, resulting in slow performance and poor energy transfer.

The Role of Amorphous Materials

The IISc team proposed a radical shift: what if cathodes were made amorphous—that is, without long-range atomic order? In such disordered structures, magnesium ions can diffuse more freely, overcoming the bottlenecks of crystalline materials.

Their model focused on amorphous vanadium pentoxide (V2O5), revealing that magnesium ion mobility improved by five orders of magnitude compared to crystalline equivalents. This breakthrough points toward cathode designs that could finally unlock the full potential of magnesium-based batteries.

Machine Learning Meets Materials Science

Simulating amorphous systems at the atomic scale is notoriously challenging. Traditional methods like density functional theory (DFT) provide accuracy but are computationally expensive, while molecular dynamics (MD) simulations are faster but less precise.

To overcome this, the IISc team developed a hybrid approach: they trained a machine learning model on DFT-generated data, then used it to accelerate MD simulations at larger scales. This combination preserved accuracy while vastly improving computational efficiency.

The result was a detailed picture of how magnesium ions move through amorphous structures—insights that would have been prohibitively slow to achieve with conventional methods alone.

Implications for Next-Generation Energy Storage

If validated experimentally, amorphous cathode materials could pave the way for commercial magnesium batteries—a leap forward for electric vehicles, grid-scale storage, and portable electronics. The potential benefits include higher energy density, improved safety compared to lithium-ion, and reduced reliance on lithium, which faces supply and cost challenges.

However, challenges remain. The stability of amorphous materials in real-world batteries is still uncertain, and further experimental work is needed to confirm their long-term viability.

A Different Pathway Forward

This research exemplifies how combining computational materials science with artificial intelligence can accelerate discovery and innovation. By breaking away from traditional crystalline designs and embracing structural disorder, scientists may find new solutions to long-standing energy storage problems.

As lead researcher Sai Gautam Gopalakrishnan put it: “Our work offers a completely different pathway to identify electrode materials for batteries and takes us a step closer to commercialization of magnesium batteries.”

Source: Original article published by TechXplore: Building better batteries with amorphous materials and machine learning . Based on research published in Small (2025). DOI: 10.1002/smll.202505851

*This blog article was prepared with the assistance of AI technologies.*

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#MagnesiumBatteries #AmorphousMaterials #MachineLearning #EnergyStorage #NextGenBatteries #MaterialsScience #CleanEnergy #BatteryInnovation #QuantumServerNetworks

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