by Augustine Jenevan Irranious
20 January 2026
In the 1940s, British mathematician Alan Turing built machines that cracked Nazi codes and laid the foundations for what we now call Artificial Intelligence (AI). Decades later, AI has moved from theory into our daily lives, including academic work, in ways Turing himself might not have imagined.
In my own research in chemistry, I see how AI speeds up the work that once consumed weeks. Searching for reaction pathways or key literature used to mean sifting through countless papers and archives. Now, AI-powered databases bring relevant research to my screen in seconds, even surfacing hidden connections I might have missed. I make extensive use of AI-powered databases such as Reaxys, which allow me to retrieve relevant book chapters or journal articles within seconds, a process that would otherwise take hours of manual searching.
For my undergraduate research I used these AI-enabled computational methods to optimize and analyze defect formation energies, dopant effects, ion migration pathways, and mechanical properties which made the research process more efficient and then a purely manual, trial-and-error approach. The AI also helped me to compare the computational results with experimental literature by extracting relevant numerical values to identify gaps in the field
For my undergraduate research, I incorporated AI-enabled computational methods to optimize and analyze defect formation energies, dopant effects, ion migration pathways, and mechanical properties of materials. This significantly streamlined my workflow, making the research process far more efficient than a purely manual, trial-and-error approach.
Writing has also changed. As a graduate student, I sometimes struggle to phrase collaboration emails or research summaries clearly. AI tools like ChatGPT help me draft professional messages, correct grammar, and organize ideas, turning an anxious task into an efficient first step.
The same transformation is visible in the broader scientific world. AI modeling, for example, has helped accelerate vaccine development by streamlining candidate selection and reducing trial-and-error. This process was once measured in decades, and is now measured in years.
AI is not replacing scientists; it is amplifying our capacity to think, test, and discover. Like Turing’s machine, it is a catalyst, not a substitute. Whether we embrace it or not, AI will continue to grow. The question for researchers is not if we use it, but how we develop the skills to make the most of it for advancing knowledge and improving society.