For most of history, discovering new materials was a slow and uncertain process shaped by chance breakthroughs and years of trial and error. Vulcanized rubber, for example, was discovered by accident, while lithium-ion batteries took decades to refine. Scientists spent years experimenting, hoping something would eventually work. But things look very different now. By 2026, AI is no longer a buzzword. It has become the secret weapon in laboratories worldwide, turning materials science from a manual craft into a fast, data-driven discipline.

Saying Goodbye to Guesswork: Materials Informatics

Developing a new material traditionally required 10, 15, or even 20 years from idea to product. AI is changing that timeline. Enter Materials Informatics. Machine learning tools can now process large volumes of data on atomic structures and properties, and predict how a material will behave before anyone mixes a single chemical in the laboratory.

Rather than mixing materials to see what happens, researchers train AI models such as Variational Autoencoders or Generative Adversarial Networks using large databases like the Materials Project. These models can generate new and unconventional crystal structures that human researchers might not consider. As a result, the next superconductor, battery electrode, or space-age alloy could appear on a screen, ready for testing.

Today, AI adoption is already delivering measurable returns on investment across various sectors. In R&D, industry leaders such as Bridgestone and Goodyear have pioneered the use of AI-driven simulations to transform tire compound development. By leveraging these virtual environments, they reduced traditional trial-and-error testing, shorten time-to-market for high-performance materials, and lower experimental costs. At DKSH, we enable our partners to achieve similar outcomes by providing high-quality analytical data to support these predictive models.

Sharper Production: AI in Quality Control

In a manufacturing environment, computer vision systems now handle quality inspection tasks in places like semiconductor fabs and aerospace plants that were once manual. Deep learning scans surfaces under the microscope, identifying cracks or defects most people would miss, with an accuracy rate above 99%. In the production of materials such as carbon fiber or specialty steel, AI monitors every sensor, including temperature, pressure, and humidity, and adjusts the process in real time. This results in fewer batches falling out of specifications and helps companies avoid unnecessary material waste and costs.

Then there’s predictive maintenance. Instead of waiting for machines to fail, AI analyzes changes in sound, vibration in equipment such as CNC machines and chemical reactors, and flags potential issues weeks in advance. This allows maintenance to be scheduled in advance and helps keep production running smoothly.

In production and Quality Assurance (QA) phase, the impact of AI is even more immediate. In the semiconductor industry, for example, Samsung utilizes AI-powered visual inspection systems to identify microscopic defects on silicon wafers before they move to the next production stage. This precision helps maximize yield and reduce waste. Global manufacturers such as PepsiCo have implemented AI-driven predictive maintenance to monitor equipment health in real time. By detecting potential issues earlier, we help our partners avoid costly unplanned downtime and maintain a consistent production flow.

The Autonomous Lab: Science on Autopilot

This is where the self-driving laboratory comes into play. Picture a lab that runs itself: AI plans experiments, robots handle the mixing, and tools like X-ray diffraction and electron microscopy feed results directly back to the computer. The cycle continues without interruption, learning from each result and improving over time.

Over the past few years, these laboratories have helped fast-track green tech, including green steel and compostable plastics. AI can fine-tune properties such as plastics’ molecular weight and cross-linking at remarkable speed.  This is opening the door to practical alternatives to traditional single-use plastics.

What’s Next: From Atoms to the Planet

Looking ahead to the end of the decade, the combination of AI and quantum computing is set to significantly expand what is possible. Imagine simulating how a bridge’s steel will age over 50 years in just a few hours on a computer or evaluating how a spacecraft’s heat shield will perform long before it ever leaves Earth.

Sustainability is advancing as well. AI can support the design of materials optimized for easier disassembly, so they can be recycled more easily once they are no longer in use. This is an important step for the circular economy and for reducing the environmental footprint for manufacturing.

Summary

AI is no longer just a tool. It has become a core part of modern materials science. For R&D teams, it offers deeper insight into the hidden world of atoms. For engineers, it is a safety system that keeps quality high. At DKSH Technology, we are actively part of this shift, connecting advanced AI-powered instruments with partners across Southeast Asia. By providing the right tools and technical expertise, we help our partners strengthen their AI models and stay ahead in a world that’s moving faster than ever. As AI continues to evolve, the gap between material discovery and manufacturing continues to narrow, and we are working with you to make “materials on demand” a practical reality for the next industrial era.

Sources:

• Revolutionizing Tire Quality Control: AI’s Impact on Research, Development, and Real-Life Applications | MDPI
• Bridgestone Enhances Virtual Tyre Development Capabilities | Bridgestone Newsroom
• Optimizing Semiconductor Defect Classification with Generative AI | Nvidia Technical Blog
• PepsiCo: AI Powers Manufacturing and Supply Chain Innovation | Manufacturing Digital