Papers
2
Total Citations
21
H-Index
2
About
Zikai Xie is pioneering the intersection of artificial intelligence and materials chemistry, with a focus on accelerating the discovery of advanced catalysts and functional nanomaterials. Their major contributions lie in developing AI-driven robotic platforms that autonomously explore vast chemical spaces, dramatically reducing the time needed to identify high-performance materials. In their landmark 2025 work on "Physics-informed, dual-objective optimization of high-entropy-alloy nanozymes by a robotic AI chemist," Xie demonstrated how a closed-loop system combining physics-informed machine learning with automated synthesis can optimize multiple competing properties simultaneously, earning 16 citations and establishing a new paradigm for materials discovery. Building on this, their 2026 study on "Optimizing toward Discovery: AI-Driven Exploration of Lewis Acid–Base Catalysts for PET Glycolysis" tackles the urgent challenge of plastic waste valorization, using artificial intelligence to efficiently navigate the sparsely explored catalyst landscape for polyethylene terephthalate (PET) depolymerization. By merging computational prediction with robotic experimentation, Xie is not only advancing fundamental understanding of catalytic mechanisms but also creating practical tools for sustainable chemistry, positioning them as a rising leader in the field of autonomous materials discovery.
Research Focus
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