AI Used To Hunt Quantum Materials

Scientists have created an artificial intelligence tool called SCIGEN that can potentially speed up the hunt for novel quantum materials. Such unusual substances, displaying odd electronic and magnetic characteristics, are promising candidates to be the next generation’s building blocks of quantum computers, nanoscale electronics, and next-generation energy devices.

The study employs machine learning and rigorous geometric rules in combination to produce millions of candidate materials, some of which seem both stable and strange enough to be interesting.

Quantum materials are center-stage for modern physics and chemistry. Their strange behaviors, say superconducting or exotic magnetic properties, can power revolutionary technology. The problem is that they are very difficult to discover.

The number of atomic arrangements available is so enormous that it is virtually impossible to look through all of them. Despite a couple of decades of work, scientists have been successful in identifying only a few stable candidates for such phenomena as quantum spin liquids, which have the potential for quantum computing.

This bottleneck spurred researchers at MIT and collaborating institutions to try a different tack. Instead of allowing the AI to generate materials at random, they told it to replicate known patterns in order to induce quantum behavior.

SCIGEN, or Structural Constraint Integration in GENerative model, works by guiding a standard form of generative AI called diffusion models. They normally start with some random noise and progressively move that towards building a structure. But if left to their own devices, they like to stay near what they’ve been trained with and venture into very few unusual geometries.

What makes SCIGEN special is that it brings in rules into the game. At each step, the system guides the model toward specific geometries of the lattice, such as honeycomb, kagome, or Archimedean structures. Such structures are most interesting to physicists because these tend to host exotic states such as high-temperature superconductors or odd magnetic orders.

"We don’t need 10 million new materials to save the world, we just need one really good material," says Mingda Li, MIT’s Class of 1947 Career Development Professor and lead author of the study.

To test the method, the group used SCIGEN to generate about 10 million inorganic compounds that have Archimedean lattice tilings. These tilings, made of repetition shapes like triangles, squares, or hexagons, are aesthetically pleasing in mathematics and physically intriguing.

The researchers then screened them through a four-step process that cut out unstable or chemically unreasonable candidates. A million or so survived the first sieve. They selected 26,000 for more extensive simulations using density functional theory (DFT), a standard quantum mechanical workhorse.

The result was surprising. Fully more than 95 percent of the DFT calculations converged. Over half of those materials proved to be structurally stable, their atoms settling into low-energy structures. Better still, 41 percent showed magnetic ordering, a characteristic often linked with exotic physics.

Yes, it’s easy to forecast materials on a computer; it’s another thing to produce them in a lab. To push the idea further, the team tried to synthesize two of the forecasted compounds: TiPd₀.₂₂Bi₀.₈₈ and Ti₀.₅Pd₁.₅Sb. Both were subjected to tests as paramagnetic and diamagnetic.

While not the exotic magnets scientists want most, both findings were in line with the forecasts, proving that SCIGEN can in fact produce materials that can be synthesized and tested in reality.