The Hidden Potential of Defects: How AI and DFT are Redefining the Future of Carbon-Based Materials
- Mary Taiwo Ajide
- Jun 11
- 4 min read
Rethinking Imperfections: A New Era of Materials Science
Imagine a material that could revolutionise energy storage, catalysis, and nano-electronics — one that could be tuned at the atomic level to exhibit entirely new properties. Graphene and its close relative, Highly Oriented Pyrolytic Graphite (HOPG), have long been at the forefront of materials science, yet unlocking their full potential has remained a challenge.
That is, until now.
At the core of this research is a counterintuitive idea: What if imperfection is actually an advantage? Instead of treating defects as flaws, what if they could be engineered to enhance electronic properties? This study set out to do just that, using cutting-edge computational techniques and artificial intelligence (AI) to investigate how vacancy defects and elemental doping could transform HOPG into a next-generation material.
From Pristine to Purposeful: Engineering the HOPG-Water Interface
Understanding how defects influence HOPG requires quantum-level precision. To achieve this, we turned to Density Functional Theory (DFT) — a powerful computational method capable of predicting electronic structure and material behaviour at the atomic level.
However, there’s a major problem:
🔹 Simulating real-world interactions, especially at the HOPG-water interface, is computationally expensive and time-consuming.
🔹 Traditional simulations could take months, even years, making large-scale studies impractical.
Exploring Artificial Intelligence (AI). 🧠💡
By deploying on-the-fly Machine Learning Force Fields (MLFF) — an advanced AI-driven approach — we bypassed computational bottlenecks while maintaining near-quantum accuracy. This unprecedented efficiency allowed us to:
✅ Simulate water interactions with HOPG
✅ Investigate how defects reshape the electronic landscape
✅ Analyse how doping with Nitrogen (N), Oxygen (O), and Sulphur (S) modifies electronic properties
This fusion of AI and quantum mechanics accelerates material discovery in previously impossible ways.
Defects as Design Tools: A New Electronic Landscape
The results? Striking.
🔹 Vacancy defects were not just imperfections — they were transformative (see Figure 7, which illustrates how the presence of defects alters the crystallographic orientation of pristine, defect-free HOPG). These defects created localised electronic states, disrupting the π-conjugated network and introducing new energy levels within the band gap. Instead of behaving purely as a conductor, defect-engineered HOPG could be tailored to exhibit semiconducting or even magnetic properties.
🔹 Doping further enhanced tunability, as illustrated in Figure S19, which shows how the electronic band structure of materials changes when vacancy defects or substitutional defects (i.e., foreign atoms) are introduced into the hexagonal lattice of highly oriented pyrolytic graphite (HOPG):
• C–N bonding improved conductivity, making HOPG an ideal candidate for high-performance electronics.
• C–O interactions introduced mid-gap states, which could enhance catalytic activity.
• C–S defects localised electronic states, impacting conductivity, reactivity, and charge transport mechanisms.
The ability to precisely engineer electronic properties in carbon materials opens exciting new possibilities — from next-generation batteries and supercapacitors to sensors and high-performance coatings.
AI-Powered Materials Discovery: A Game-Changer
One of the most groundbreaking aspects of this research is the integration of AI in materials science.
🚀 Traditional quantum simulations are notoriously slow, but AI-driven machine learning force fields (see Figures 1 and 2) allow us to:
✔️ Skip a large percentage of expensive calculations
✔️ Achieve near-quantum accuracy
✔️ Fast-track materials discovery from years to months or even weeks
This demonstrates a scalable, data-driven pathway for optimising materials in real time — redefining the way we approach materials engineering.
The Road Ahead: AI-Designed Materials for the Future
This study isn’t just about HOPG — it’s about the future of materials science. By leveraging AI and quantum simulations, we are entering a new era where materials can be designed with unprecedented precision.
🔹 Ultra-efficient energy storage 🔋
🔹 High-performance catalysts ⚡
🔹 Advanced nano-electronics 🏗️
🔹 Self-learning experimental labs combining AI and robotics 🤖
By turning defects into opportunities, this research lays the foundation for a future where AI and materials science work hand in hand to push the boundaries of innovation.
This is more than a scientific advancement — it’s a blueprint for the future of materials engineering.
Final Thoughts
Defects are no longer flaws. They are the key to unlocking next-generation materials. 🔑
With AI accelerating discoveries and machine learning force-fields bridging the gap between computational cost and accuracy, we are witnessing a paradigm shift in materials science. The future is filled with unimaginable possibilities, and this research is paving the way toward them.
What are your thoughts on the role of AI in materials discovery? Could defect engineering be the key to unlocking entirely new functionalities?
Let’s discuss! 🔬💡✨
See the referenced research study below to explore all implemented figures.
References
Ajide, M.T., Naeiji, P., Klug, J. and English, N.J., 2025. Machine learning force field predictions of structural and dynamical properties in HOPG defects and the HOPG-water interface with electronic structure analysis. ACS Omega, [online early access]. Available at: https://doi.org/10.1021/acsomega.5c02543 [Accessed 10 June 2025].
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