In a time when the pressing challenges of sustainability and energy efficiency loom, the quest for innovative solutions has never been more critical.
At the heart of this quest is an unexpected partnership between artificial intelligence (AI) and material science. AI isn’t just changing how we make small-scale discoveries in the lab – it’s changing how we find new properties and materials that will impact our approach to global issues.
Researchers can predict material behaviours and accelerate material discovery using machine learning (ML) algorithms and large datasets.
This isn’t just about saving costs — it’s about creating a sustainable future in which we can tackle key global challenges like sustainability and energy efficiency head-on.
What’s the Big Deal About Using AI for Material Discovery?
Traditional methods in materials discovery are painfully slow and time-consuming. It takes 10-20 years for material to go from the laboratory research phase to hit the market, which isn’t fast enough when we have huge global issues that only worsen daily.
However, the usage of AI means we can reduce this timeframe by a huge amount.
The nature of material science means that there are almost an unlimited number of possibilities in terms of different materials and properties to be discovered, making it difficult to find the ones that will solve our global problems.
With AI, we have digital tools to predict how different materials will behave and interact in various conditions without performing physical experiments. This way, it’s much faster to identify which combinations have the highest chance of success by carrying out large amounts of simulations.
Instead of using the old method of guesswork and physical trials, researchers can quickly carry out many simulations and digital experiments to focus on promising candidates from the start. This significantly speeds up the discovery process and reduces waste and costs of the research.
Examples of AI Applications in Material Science
With these new digital methods, researchers have made quite a few interesting material discoveries that impact our daily lives.
Self Healing Materials
One material is at the foundation of urban environments: concrete.
Due to its low cost and versatility, concrete is the world’s most commonly used building material. However, fractures can affect concrete and reduce its durability overtime, which isn’t cheap or quick to fix.
ML has been used to investigate a new type of concrete called self-healing concrete. Specialised microbes can help seal up any cracks in concrete. By using ML to determine how quickly concrete can be self-repaired using a combination of the right bacteria and fibres, researchers are progressing in better understanding this novel material.
Another area where self-healing materials have a significant impact is the aerospace industry. Since aircraft must endure high loads and stresses, they will inevitably degrade overtime, especially when weather conditions are involved. Self-healing materials are increasing the lifespan of these aircraft components by repairing any damage done.
Plastics
Plastics are at the forefront of sustainable development. They’re used in applications such as biodegradable water bottles, heat-resistant car parts, flexible electronics, and more.
At the heart of plastics are polymers, which are made up of many small repeating units called monomers. These monomers give polymers unique properties like flexibility, strength, and durability.
AI is being used to accelerate the search for suitable polymers to help society transition from fossil fuels to polymers made from biomass and waste. This will reduce greenhouse gas emissions from polymer manufacturing.
Like the discovery process of self-healing materials, ML speeds up the polymer discovery process by a huge amount so researchers can quickly identify promising candidates for sustainable polymers. They can also quickly analyze huge datasets of chemical properties and polymer structures at a rate that wasn’t achievable with conventional methods.
Quantum Dots
In 2023, Moungi Bawendi, Louis Brus, and Aleksey Yekimov won the Nobel Prize for developing and discovering quantum dots.
But exactly are quantum dots?
They’re tiny semiconductor particles with optical and electrical properties to larger particles. In particular, they’re extremely useful for optical applications like fluorescent markers in medical imaging and display technologies like quantum dot light-emitting diodes that offer improved display performance.
Moreover, they’re also great for creating more efficient solar panels. Quantum dots can absorb and convert sunlight into electricity more efficiently than traditional solar panels. This means they help us create solar panels with higher conversion efficiencies. It’s led to achieving a record of 25% solar cell efficiency, making the reality of a sustainable energy future more realistic.
The issue is that quantum dots exist in a unique state of matter. This gives them unique properties but makes them challenging to control and predict. As a result, it’s been difficult to apply them in the real-world despite their high potential for practical use.
ML has been used to understand better how synthesis condition changes affect quantum dots’ properties. This approach also helps improve quality control by producing quantum dots of the same shape and size each time, helping us make them safer and more stable in real-world applications like solar cells.
A Sustainable and Energy-Efficient World
Integrating AI and ML into material science has changed how we find new materials and our perspective on how we will approach global issues like sustainability and energy efficiency.
It’s taking the form of applications like self-healing concrete that leads to more durable buildings, discovering polymers for more sustainable plastics, and improving our understanding of quantum dots so they can boost solar energy efficiency.
That isn’t to say this quest to find the perfect material doesn’t have any road bumps. We still need to overcome challenges like the ethical considerations surrounding AI because of data privacy concerns when massive datasets are used in these simulations and experiments.
Nevertheless, using AI for material discovery presents a hopeful path to finding solutions to form a more sustainable and energy-efficient future.
