Smart automation, AI to accelerate materials discovery, innovation

Autonomous Research System ARES research robot

The Air Force Research Laboratory’s Autonomous Research System (ARES) uses artificial intelligence and machine learning as part of a closed loop, automated scientific research process. The ARES platform is part of a next-generation research movement focused on human-machine partnering to create the next generation of materials for Air Force technology. (U.S. Air Force photo/David Dixon)

WRIGHT-PATTERSON AIR FORCE BASE, Ohio --To meet the demands of the ever-increasing pace of technological development in order to create the next generation of Air Force technology, researchers are challenged to find ways to shrink the timeline for materials discovery, development and deployment.

According to Dr. Benji Maruyama, a research scientist at the Materials and Manufacturing Directorate, Air Force Research Laboratory, this materials challenge can be met by a greater focus on machine learning, artificial intelligence (AI) and autonomous systems by the scientific community, which can exponentially increase the speed of materials discovery and lower the cost of technology over the long term.

“We need to reinvent the process of doing research,” said Maruyama.“Research is typically a slow process, with the time for materials conception and discovery to actual implementation on a platform for warfighters typically taking anywhere from 20 to 30 years in time. Today’s technological environment offers an opportunity for researchers to partner with research robots to advance understanding of multidimensional problems more rapidly.”

Maruyama is the coauthor of, “Accelerating the discovery of materials for clean energy in the era of smart automation,” recently published in Nature Review Journal, which discusses an integrated AI approach to materials discovery focused on the implementation of autonomous research systems in the laboratory environment. Maruyama is also the creator of the AFRL’s Autonomous Research System (ARES) which uses AI and machine learning to conduct autonomous experiments designed to optimize the synthesis of carbon nanotubes, which have tremendous potential for next-generation energy technology.

Today’s research problems are complex and highly dimensional, says Maruyama, with advanced materials impacting the full spectrum of daily life from energy generation, power, transportation, aerospace and more. This requires the continuous development of new materials that are able to meet increasingly demanding performance requirements.

Platforms such as ARES that are able to integrate AI with robotics and machine learning provide an opportunity to accelerate the development process, leading to innovative, new materials at a faster pace and ultimately, decreased cost, for Air Force and commercial uses.

“Machines can complete work in multi-dimensional spaces more quickly than human researchers,” said Maruyama. “For example, every time ARES conducts a new experiment, it can use AI to analyze the results and design the next best experiment to perform in closed-loop automation, resulting in hundreds of experimental interactions per day. By removing the need for the human researcher to conduct tedious lab work to collect data, it creates the opportunity for more human insight and creativity, leading to faster implementation of material solutions.”

According to Maruyama, next generation material development augmented by autonomous research and AI systems will likely experience “Moore’s Law” in which the rate of research will climb at an extremely high rate as the AI technology continues to improve, resulting in the discovery of innovative new materials faster than ever before. Moore’s Law, a computing term developed in the 1970s, was founded on the premise that the overall processing power for computers would double every two years as technology improved. If the law holds true for research, says Maruyama, the material advancement potential is enormous.

“We started work on carbon fiber in the 1950s, and it took a long time for it to be used on aircraft and platforms. We still have a ways to go before we even realize carbon fiber’s full potential. If we can continue to update our understanding of the AI and autonomous systems environment, we will drive the speed of material innovation for the better,” said Maruyama.

Maruyama and his coauthors’ work has attracted widespread interest in both academia and industry, as the concept of uniting technological advances in automation, robotics and computer science with research and development is a revolutionary perspective in the centuries old field of scientific discovery. The ability to accelerate development and save cost by revolutionary approaches to long-term research is extremely enticing across the spectrum.

“We envision a new paradigm for materials discovery emerging over the next five to ten years or so,” said Maruyama. “We are changing the face of research.”

The full text of “Accelerating the discovery of materials for clean energy in the era of smart automation,” in Nature Review Journal is accessible at:

Dr. Benji Maruyama and the AFRL team is in the process of trademarking ARES and plan to commercialize the ARES artificial intelligence software for widespread research use in diverse areas in the future.