Think of them as master Lego builders, only at an atomic scale. Engineers at CU Boulder have taken a major step forward in combing advanced computer simulations with artificial intelligence to try to predict how electronics, like the transistors in your cell phone, will fail.

In the latest study, researchers mapped out the physics of small building blocks made up of atoms, then used machine learning techniques to estimate how larger structures created from those same building blocks might behave. It’s a bit like looking at a single Lego brick to try to predict the strength of a much larger castle.

It’s a pursuit that could be a boon for the electronics that underpin our daily lives, from smartphones and electric cars to emerging quantum computers. One day, engineers could use the team’s methods to pinpoint in advance weak points in the design of electronic components.

The project is part of a larger focus on how the world of very small things, such as the wiggling of atoms, can help people build new and more efficient computers—even ones that take their inspiration from human brains. Artem Pimachev, a research associate in aerospace engineering at CU Boulder, is a co-author of the new study.

Rather than wait for years to figure out why devices fail, our methods can give us a priori knowledge on how a device is going to work before we even build it.

Heating up

Their latest research focuses on a big sticking point in the electronics industry: Hotspots.

And, no, that doesn’t mean the mobile WiFi hookups. Most modern computing tools carry a large number of imperfections––small defects in electronic components that cause heat to build up at certain sites, a bit like how a bicycle slows down when you ride over rough terrain. Such “hotspots” also make your smartphone a lot less efficient.

The problem is that engineers drawing on computer simulations, or models, struggle to predict ahead of time where those weak points are likely to turn up.

We can use physics models to understand systems with approximately 100 atoms in them. But that doesn’t compare to the billions of atoms in these devices.

From atoms to devices

Think back to those individual Lego bricks, which, in this case, are clumps of 16 silicon and germanium atoms, the main ingredients in many computer components.

In the new study, researchers developed a computer model that uses artificial intelligence to learn the physical properties within those building blocks—or how atoms and electrons come together to determine the energy landscape within a material. The model can then extrapolate from those basic blocks to estimate the distribution of energy in a much larger chunk of atoms.

It collects information from each individual unit and combines them to predict the final properties of the collective system, which can be made up of two, three or more units.

The team still has a long way to go before it can pinpoint all of the potential weak points in a device the size of your phone. But, so far, the group’s model has proved effective.

The researcher is also drawing on her understanding of how heat and energy flow at very small scales to not just improve existing devices, but also help create the devices of the future.

What I want to do is poke at this world of atoms in your handheld device and understand how materials and electronics come together to make a device work.

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Scientists and engineers are on a quest to develop electronic devices that are compatible with our bodies: think of materials that can help wire neurons back together after brain injuries, or diagnostic tools that can easily be absorbed within the body.

A family of self-assembling peptides, called π-conjugated oligopeptides, has shown promise for becoming the basis of the next-generation of these electronic, biocompatible materials. But identifying the right molecular sequences to create the optimal self-assembled nanostructures would require testing thousands of possibilities that each take approximately one month to test in the lab.

Assoc. Prof. Andrew Ferguson and his collaborators have sped up that process by developing machine learning tools that can screen for the best candidates. By screening 8,000 candidates of self-assembled peptides, the team was able to rank each design. That paves the way for experimentalists to test the most promising candidates.

The results were published in The Journal of Physical Chemistry B. The paper was also selected as the ACS Editors’ Choice, which offers free public access to new research of importance to the global scientific community, and to be featured on the journal cover.

“By understanding data science, materials science, and molecular science, we were able to find an innovative way to screen for new possible candidates,” Ferguson said. “The fact that this paper was chosen as an ACS Editors’ Choice shows that there is a lot of interest in coupling artificial intelligence to domain science. It’s an important problem that is of broad interest to the physical chemistry community.”

Ranking peptides for experimentalists

To help find the best candidates, Ferguson and graduate student Kirill Shmilovich screened a family of π-conjugated oligopeptides using machine learning and molecular simulation. The set included 8,000 potential peptides, if researchers kept the same core and just changed the three amino acids on each side of the molecule. (The amino acids on the sides are symmetrical—if you change one on one side, it changes on the other side, as well.)

Using a form of machine learning known as active learning or Bayesian optimization to guide molecular simulations, they were able to construct reliable data-driven models of how the sequence of the peptide influenced its properties after considering only 186 peptides.

The model predictions could then be reliably extrapolated to predict the properties of the rest of the peptide family. The process also removed human bias from the equation, letting artificial intelligence find features of peptide designs that researchers hadn’t considered before that actually made them better candidates.

They then ranked each peptide and handed off their results to their experimental collaborators, who will then test the top candidates in the lab. Next, they hope to expand their system to include trying out different π-conjugated cores, while feeding new experimental data back into the loop to further strengthen their models.

They also hope to use this machine learning system for designing proteins, optimizing self-assembling colloids to make atomic crystals, and even to one day incorporate these tools into a self-driving laboratory, where artificial intelligence would take data, create predictions, run experiments, then feed that data back to the model—all without human intervention.

“This is a method that could be useful in many different domains,” Ferguson said.

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