Quantum and brain-like technologies with “defective” materials

Machine learning and physics-based simulations to design materials with defects

In this paper, published in ACS Nano, I show how machine learning and computer simulations can be used to design materials with defects for new types of computing and information storage. You might think that “defects” are always a bad thing; after all, if a product is “defective,” it doesn’t inspire a lot of confidence. But in materials, defects can be purposefully created and used to engineer better performing technologies.

Cooking with defects

If you’ve owned a set of kitchen knives for more than a few months and they aren’t totally rusted yet, you’re taking advantage of defects! The stainless steel in those knives is mostly iron, but the iron isn’t perfect — it has “defects” of other elements added in that actually make it better. All those other elements work together to keep the iron from rusting. It’s like adding chili powder to your dinner; a little goes a long way to transforming the food.

In this paper we wanted to answer the question: what kinds of materials and defects might be good candidates for new technologies? Stainless steel knives are pretty well figured out, so we focused on some more cutting-edge applications, things like quantum computing and neuromorphic engineering. With quantum computers we’re trying to build a new way of calculating things that your laptop might not be able to. With neuromorphic engineering we’re making artificial systems that mimic how the brain processes information.

I’m going to be upfront and say that both of these technologies are really, really early stage and I don’t spend much time worrying about how practical or impractical they might be in the short term. Instead, we want to understand the building blocks of these technologies and figure out how to engineer them to do what we want.

Designing an atom

So how does a material with a defect lead to a quantum computer, or mimic how the brain works? We can think about a flat sheet of atoms like the one in this picture:

A 2D sheet of carbon atoms. From Wikimedia Commons.

If I pop one of those atoms out of the sheet, that leaves a vacancy, which is a kind of defect. Even though all the atoms in the sheet are chemically bonded together into a material, we can think about that vacancy as a sort of “artificial atom.” It behaves kind of like an isolated atom in empty space, although it’s surrounded by all the “real” atoms in the sheet.

The cool thing is that the vacancy obviously isn’t a “real” atom from the periodic table. Instead, it’s something we created, so it makes sense to ask if we can engineer or design the properties of our “artificial atom” to be different from what we can already find in the regular old periodic table. It turns out, we can! By changing the atoms in the sheet, taking out individual atoms, and swapping in new atoms that are different from all the rest in the sheet, we can design all sorts of artificial atoms.

A zoo of defects

If you think about all the elements to choose from (94 naturally occurring ones), the different ways we can arrange them into sheets, and all the ways you could take an atom out or add a new one in, it’s pretty clear that there are a lot of possible defects. Way too many to make them all and test them in a laboratory, and even too many to simulate on a computer.

Luckily, we know some physics that helps narrow down the types of materials and defects that are worth looking at. We can throw out materials that conduct electricity, and defects that will never be stable.

That still leaves a lot of exploring to do, so we came up with around 10,000 different defect structures and chose over 1,000 to simulate on the computer. We trained deep learning and machine learning models to predict some of the most important properties of the materials and their defects — properties that can help guide us towards the best defects for quantum information and neuromorphic engineering. From the initial list of 10,000, we suggested a little over 100 defects that we think will be interesting to look at in laboratory experiments.

A defective memory

Some of these defects can be implanted and removed by applying a voltage. When the defect is there, the material conducts electricity differently than when it isn’t there. That forms a kind of “memory” where information is stored and retrieved based on how defects are patterned through the material. In a really simple way, this kind of memory is inspired by how the brain physically changes as memories are formed. It might seem a little unhinged to try to mimic the brain with defects in materials, but it turns out this kind of memory based on electrical conductivity in materials has already been used to “learn” to recognize hand-written numbers!

An atomically thin memory. From Nanowerk.

Emitting single photons of light

The other type of defect that we tried to find are called “single-photon emitters” or “quantum emitters.” A photon is a single packet of light, and these defects emit single photons! The fact that you can think about a single particle of light and create one with a defect is pretty cool. It’s also potentially useful, because some researchers have already demonstrated that it’s possible to do quantum computing with single photons.

Single photons of light emitted from a 2D material for quantum technologies. From photonics.com.

It’s important to remember that controlling materials at the atomic level is possible, but really hard. And artificial atoms are complicated things that require fine-tuning and manipulation to get just right. Paprika, plus garlic powder, plus cayenne pepper, plus oregano…There are a lot of ingredients to consider and a lot of experimentation required to get the recipe worked out.

We hope that our study provides some interesting ideas of where to start, and maybe more importantly, shows how techniques like deep learning and machine learning can be used to make progress in the huge, complicated field of defect engineering.

Getting in touch

If you liked this explainer or have any questions, feel free to reach out over email or connect with me on LinkedIn and Twitter.

If you’re interested in more technical details, you can read the paper here and check out the trained models on GitHub.

You can find out more about my projects and publications on my website or just read a bit more about me.