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Caltech Scientists Use AI to Revolutionize Quantum Calculations of Atomic Vibrations

Researchers at Caltech have unveiled a groundbreaking artificial intelligence technique that drastically accelerates the study of quantum interactions in materials. At the heart of their work lies the ability to model atomic vibrations, also known as phonons, which play a crucial role in determining properties such as heat transport, thermal expansion, and phase transitions. This development marks a transformative moment in computational materials science, where traditionally weeks of calculations can now be achieved in seconds.

Led by Marco Bernardi, professor of applied physics, physics, and materials science at Caltech, and graduate student Yao Luo, the team set out to overcome one of the most significant challenges in quantum materials research: the overwhelming complexity of phonon interactions. These interactions are mathematically represented in tensors, multidimensional structures that grow exponentially in size as more particles are considered. Traditional approaches often required supercomputers running for hours or days to calculate interactions involving just three or four phonons.

The new AI-driven approach changes the game. By leveraging a technique known as CANDECOMP/PARAFAC tensor decomposition, adapted to account for the symmetries of physical systems, the researchers built a neural network capable of identifying only the essential mathematical components needed for accurate results. Instead of working with full, resource-intensive tensors, the method compresses the problem into a far smaller set of functions while maintaining precision.

According to Bernardi, the improvement is nothing short of remarkable: calculations that once took weeks of computing power can now be completed in just 10 seconds. The efficiency gain, ranging from 1,000 to 10,000 times faster than traditional methods, opens new opportunities for high-throughput materials screening. This means entire databases of materials can be analyzed quickly for their thermal and quantum properties, accelerating discoveries in fields such as electronics, renewable energy, and quantum computing.

Beyond phonons, the vision for this approach extends further. Bernardi and Luo anticipate that similar AI-powered compression methods could be applied to a wide variety of quantum interactions, bypassing the need to construct massive tensors altogether. By learning compressed representations directly, scientists could unlock a more complete understanding of how particles and excitations behave in complex materials.

Conclusion: This new AI technique developed at Caltech represents a paradigm shift in quantum materials research. By combining machine learning, tensor decomposition, and neural networks, scientists have opened the door to a future where calculations once deemed impossible become routine. With the potential to redefine how researchers approach heat transport, thermal dynamics, and quantum mechanics in materials, this breakthrough will likely accelerate innovation across physics, engineering, and technology.

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