15-Jan-2020 - Okinawa Institute of Science and Technology Graduate University

Man versus machine: Can AI do science?

Over the last few decades, machine learning has revolutionized many sectors of society, with machines learning to drive cars, identify tumors and play chess - often surpassing their human counterparts.

Now, a team of scientists based at the Okinawa Institute of Science and Technology Graduate University (OIST), the University of Munich and the CNRS at the University of Bordeaux have shown that machines can also beat theoretical physicists at their own game, solving complex problems just as accurately as scientists, but considerably faster.

In the study, recently published in Physical Review B, a machine learned to identify unusual magnetic phases in a model of pyrochlore - a naturally-occurring mineral with a tetrahedral lattice structure. Remarkably, when using the machine, solving the problem took only a few weeks, whereas previously the OIST scientists needed six years.

"This feels like a really significant step," said Professor Nic Shannon, who leads the Theory of Quantum Matter (TQM) Unit at OIST. "Computers are now able to carry out science in a very meaningful way and tackle problems that have long frustrated scientists."

The Source of Frustration

In all magnets, every atom is associated with a tiny magnetic moment - also known as "spin." In conventional magnets, like the ones that stick to fridges, all the spins are ordered so that they point in the same direction, resulting in a strong magnetic field. This order is like the way atoms order in a solid material.

But just as matter can exist in different phases - solid, liquid and gas - so too can magnetic substances. The TQM unit is interested in more unusual magnetic phases called "spin liquids", which could have uses in quantum computation. In spin liquids, there are competing, or "frustrated" interactions between the spins, so instead of ordering, the spins continuously fluctuate in direction - similar to the disorder seen in liquid phases of matter.

Previously, the TQM unit set out to establish which different types of spin liquid could exist in frustrated pyrochlore magnets. They constructed a phase diagram, which showed how different phases could occur when the spins interacted in different ways as the temperature changed, with their findings published in Physical Review X in 2017.

But piecing together the phase diagram and identifying the rules governing the interactions between spins in each phase was an arduous process.

"These magnets are quite literally frustrating," joked Prof. Shannon. "Even the simplest model on a pyrochlore lattice took our team years to solve."

Enter the machines

With increasing advances in machine learning, the TQM unit were curious as to whether machines could solve such a complex problem.

"To be honest, I was fairly sure that the machine would fail," said Prof. Shannon. "This is the first time I've been shocked by a result - I've been surprised, I've been happy, but never shocked."

The OIST scientists teamed up with machine learning experts from the University of Munich, led by Professor Lode Pollet, who had developed a "tensorial kernel" - a way of representing spin configurations in a computer. The scientists used the tensorial kernel to equip a "support vector machine", which is able to categorize complex data into different groups.

"The advantage of this type of machine is that unlike other support vector machines, it doesn't require any prior training and it isn't a black box - the results can be interpreted. The data are not only classified into groups; you can also interrogate the machine to see how it made its final decision and learn about the distinct properties of each group," said Dr Ludovic Jaubert, a CNRS researcher at the University of Bordeaux.

The Munich scientists fed the machine a quarter of a million spin configurations generated by the OIST supercomputer simulations of the pyrochlore model. Without any information about which phases were present, the machine successfully managed to reproduce an identical version of the phase diagram.

Importantly, when the scientists deciphered the "decision function" which the machine had constructed to classify different types of spin liquid, they found that the computer had also independently figured out the exact mathematical equations that exemplified each phase - with the whole process taking a matter of weeks.

"Most of this time was human time, so further speed ups are still possible," said Prof. Pollet. "Based on what we now know, the machine could solve the problem in a day."

"We are thrilled by the success of the machine, which could have huge implications for theoretical physics," added Prof. Shannon. "The next step will be to give the machine an even more difficult problem, that humans haven't managed to solve yet, and see whether the machine can do better."

Facts, background information, dossiers
  • machine-learning
  • artificial intelligence
  • pyrochlore
More about OIST
  • News

    Bubbles and whispers - glass bubbles boost nanoparticle detection

    Technology created by researchers at the Okinawa Institute of Science and Technology Graduate University (OIST) is literally shedding light on some of the smallest particles to detect their presence - and it's made from tiny glass bubbles. The technology has its roots in a peculiar physical ... more

    Seeing the light?

    What is light? It sounds like a simple question, but it is one that has occupied some of the best scientific minds for centuries. Now, a collaborative study with scientists at the Okinawa Institute of Science and Technology Graduate University (OIST) has added another twist to the story, tu ... more

    Recruiting manganese to upgrade carbon dioxide

    Carbon dioxide (CO2) is known as a greenhouse gas and plays an essential role in climate change; it is no wonder scientists have been looking for solutions to prevent its release in the environment. However, as a cheap, readily available and non-toxic carbon source, in the past few years th ... more

More about LMU
  • News

    Five-fold boost in formaldehyde yield

    Environmentally benign methods for the industrial production of chemicals are urgently needed. LMU researchers recently described such a procedure for the synthesis of formaldehyde, and have now improved it with the aid of machine learning. Formaldehyde is one of the most important feedstoc ... more

    An ultrafast glimpse of the photochemistry of the atmosphere

    Researchers at Ludwig-Maximilians-Universitaet (LMU) in Munich have explored the initial consequences of the interaction of light with molecules on the surface of nanoscopic aerosols. The nanocosmos is constantly in motion. All natural processes are ultimately determined by the interplay be ... more

    Molecular motors: Rotation on an eight-shaped path

    Molecular motors convert externally supplied energy into directional motions and are thus an important basis for future applications in nanotechnology. The first such motors were developed in the late 1990s, and since then a growing number of different systems have been established. A speci ... more

More about Université Bordeaux
  • News

    Wireless microengine made from a twisted fibre

    A highly efficient, micro-sized motor-cum-energy storage system has been presented by researchers from the Helmholtz-Zentrum Geesthacht (HZG) and the University of Bordeaux in the journal ‘Science’. The ‘microengine’ is made from polymeric micro-fibres, which are stiff at room temperature. ... more

    For a Better Contrast

    Magnetic resonance imaging (MRI) has emerged as one of the most powerful clinical imaging tools because of its superb spatial resolution and soft tissue contrast, especially when using contrast agents. In the European Journal of Inorganic Chemistry, scientists have presented a new kind of n ... more

    Linking up with Huisgen

    Fabrice Odobel, Laurent Fontaine, Vincent Rodriguez and co-workers from Nantes University, University of Maine and the University of Bordeaux respectfully, have successfully used Huisgen chemistry to create a new cross-linked system which enhances the stability of electro-optic polymers. T ... more