AI/machine learning

AI Predicts New Materials for Clean Energy, Chemical Industries

Applications include identifying new green catalysts to enable the conversion of waste products to useful matter, green hydrogen generation, CO2 utilization, and the development of fuel cells. Novel catalysts also could be used to replace expensive and rare materials such as iridium, the metal used to generate green hydrogen and CO2 reduction products.

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Northwestern University and the Toyota Research Institute have successfully used technology from Stoicheia Inc. to accurately predict novel—and previously unknown—materials for the clean energy, chemical, and automotive industries.

The researchers combined Stoicheia's Megalibrary technology with artificial intelligence (AI) to come up with 19 new materials that have selected characteristics. Eighteen proved correct, representing 95% accuracy.

Stoicheia's Megalibrary technology creates more than 200 million positionally encoded and different-by- design nanomaterials on a 2×2-cm chip, becoming the foundation for data sets of a size and quality not previously achieved in materials science.

"This outcome is profound," said Chad A. Mirkin, chairman and cofounder of Stoicheia and a nanotechnology pioneer. "The predictions were outside the bounds of human scientific postulation, proving the power of Stoicheia's Megalibrary technology and machine learning and illuminating Stoicheia's path to breakthrough future discoveries critical to the clean energy, automotive, and chemical industries."

The research is detailed in the 23 December 2021 issue of the journal Science Advances.

Mirkin, who is the George B. Rathmann Professor of Chemistry at Northwestern University, is one of the study's senior authors.

To conduct the study, researchers used Stoicheia's Megalibrary technology to generate structural data for nanoparticles with complex compositions, structures, sizes, and morphologies. These data were then used to train machine-learning (ML) algorithms.

With limited physics and chemistry first principles, the ML models based upon the pure training data were able to predict complex structures of materials never before synthesized on Earth.

"This study illustrates the ability of Stoicheia to create novel complex materials faster than anybody on Earth, generate vast amounts of first-party, high-quality materials data, and train algorithms that will rapidly accelerate our understanding of the materials genome," said Andrey Ivankin, Stoicheia's co-founder and chief scientific officer. "As our models accelerate understanding, the gap between traditional discovery methodologies and Stoicheia's platform will compound. Stoicheia's virtuous cycle will finally harness the power of artificial intelligence in materials science to drive incredibly valuable solutions to the climate crisis."

The design space for new nanomaterials, which are often compared to the human genome, is inherently more complex. Rather than dealing with four-letter alphabet, the design space comprises 118 elements on the periodic table. And, when reduced to the nanoscale, changes in shape, size, phase morphology, crystal structure, and more compound the enormity of discerning structure and function of new nanomaterials. Interrogating this design space through traditional serial experimentation is a prohibitively expensive, slow, and inefficient process that leads to local winners, vs. the global, or best, winner for the problem at hand.

Stoicheia's technology is fueling processes in clean energy, automotive, and chemical industries. Identifying new green catalysts will enable the conversion of waste products and plentiful feedstocks to useful matter, green hydrogen generation, carbon dioxide utilization, and the development of fuel cells among other applications. Novel catalysts also could be used to replace expensive and rare materials like iridium, the metal used to generate green hydrogen and CO2 reduction products. As rare earth metals become more important globally, Stoicheia's technology is critical to a green and geopolitically secure future.