AI Fueling Rapid Race For New Chip-Making Materials

The answer is that AI/AE, which combines machine learning and automated laboratories, is“ushering in a paradigm shift in materials science,” according to Taro Hitosugi, Ryota Shimizu and Naoya Ishizuki of the Tokyo Institute of Technology.“Using computer algorithms and robots to decide and perform all experimental steps, these systems require no human intervention.”

“Given the possible combinations of elements,” they continue,“there is an almost infinite number of new materials... Thus, optimizing high-dimensional synthesis parameters in a vast search space is necessary for materials synthesis... In a way, the world of materials is a frontier for exploration, much like space or the deep sea.”

AI/AE should enable a vast acceleration of the process of materials discovery and synthesis, not only in the semiconductor industry but across the spectrum of applied science, from electronics, energy, aerospace and defense to biology, chemistry and pharmaceuticals.

Writing in Nature Synthesis, Milad Abolhasani of North Carolina State University and Eugenia Kumacheva of the University of Toronto state that:

In other words, AI and robots can do the job much more efficiently than scientifically informed trial and error. Researchers led by Professor Alán Aspuru-Guzik of the Department of Chemistry at the University of Toronto write:

Aspuru-Guzik is also a professor of computer science, holds a Google Industrial Research Chair in Quantum Computing and is director of the Acceleration Consortium, a University of Toronto-based strategic initiative that aims to accelerate“the discovery of materials and molecules needed for a sustainable future” by bringing together researchers from industry, government and academia.

This may be the model for the US Commerce Department's semiconductor materials initiative. In addition, the department's AI/AE competition bears a strong resemblance to the SDL Grand Challenge proposed by the Washington-based Center for Strategic and International Studies (CSIS) think tank in its January 2024 report entitled“Self-Driving Labs: AI and Robotics Accelerating Materials Innovation.”

Declaring that“The development and adoption of alternative and new materials is central to US leadership in emerging technologies,” the CSIS report wonders“whether the United States is devoting sufficient policy attention and resources to securing the advantage in SDLs.”

At that time, according to CSIS, US spending on SDLs was less than $50 million and“not done in a directed, programmatic manner,” while Canada had awarded $200 million to the Acceleration Consortium at the University of Toronto.

In this context, the US Commerce Department's $100 million award will be a belated but meaningful step forward. Its five-year time frame matches the semiconductor industry's roadmap to 1nm process technology.

CSIS also reported that the University of Liverpool, Lawrence Berkeley National Lab, Argonne National Lab and Carnegie Mellon University were building SDLs, noting that“University of Liverpool researchers in 2020
used
a mobile platform robot arm to synthesize and search for catalysts across 10 design parameters, ultimately conducting 688 experiments over eight days completely autonomously and identifying chemical formulations that were 6 times better than the baseline.”

Imec, the Inter-university Microelectronics Centre headquartered in Belgium that conducts advanced R&D with and for the semiconductor industry, is using AI to identify new materials. For example, scientists affiliated with imec write:

The Johns Hopkins University Applied Physics Laboratory (APL) is using AI to accelerate the discovery of new materials that can withstand the extreme environments that characterize deep-sea exploration, space exploration, hypersonic vehicles and other uses related to national security.

Morgan Trexler, program manager for Science of Extreme and Multifunctional Materials at APL says,

Keith Caruso, chief scientist at APL's Research and Exploratory Development Department, adds that“The approach to building on existing materials will only ever yield limited improvements. To create groundbreaking materials, we need to make a fundamental leap.”

In Japan, the RIKEN National Research and Development Agency is applying high-performance computing and AI to drug discovery and genomic medicine.

Japanese analytical instrument maker Shimadzu Corporation, which works with Kobe University, is targeting“a platform for autonomous scientific discoveries by robots and AI” as its vision of future laboratories for the development of new materials, pharmaceuticals and biotechnology, including“smart cells” with altered genes.

Science China Press reports that“Following the success of large language models, the concept of large materials models as deep-learning computational models for materials design has attracted great interest.”

Researchers from Tsinghua University are seeking to develop models“capable of handling diverse material structures across most elements of the periodic table.”

Almost a year ago, China Daily reported that an AI-driven robotic chemist developed by Chinese scientists had synthesized a catalyst to generate oxygen from Martian meteorites.

“A stress test at minus 37 degrees Celsius, which mimics the temperature condition on Mars, showed that the catalyst can steadily produce oxygen without apparent deterioration, suggesting that it can work in the harsh conditions on Mars.”

What the Chinese are doing in AI-driven autonomous materials development for the semiconductor and other industries is not clear, but they are unlikely to ignore its possibilities.

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