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Mini Review: Synergizing Driven Quantum Dynamics, AI, and Quantum Computing for Next-Gen Materials Science.

Opeyemi S Akanbi1, Jack P Shannon1, Jerome Delhommelle2

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Synergistic approaches combining quantum dynamics, artificial intelligence (AI), and quantum computing accelerate the discovery of next-generation quantum materials. These methods enable rapid exploration and design of materials with enhanced properties for advanced applications.

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Area of Science:

  • Materials Science
  • Quantum Physics
  • Computational Chemistry

Background:

  • Next-generation materials design is advancing rapidly with automated platforms.
  • Artificial intelligence (AI) and robotics are key components in modern synthesis planning and execution.

Purpose of the Study:

  • To analyze synergistic approaches combining quantum dynamics, AI/machine learning, and quantum computing.
  • To accelerate the discovery and design of quantum materials with enhanced properties and novel functionalities.

Main Methods:

  • Utilizing driven quantum dynamics to understand material response to time-dependent fields.
  • Employing AI/machine learning for rapid exploration of vast material design spaces.
  • Leveraging quantum computing to identify novel quantum phases and optimize material properties.

Main Results:

  • Synergistic approaches provide access to complex material behaviors and properties.
  • Successful applications demonstrated in quantum batteries, solar cells, and quantum information processing.
  • Identification of novel quantum phases and materials with superior performance.

Conclusions:

  • The integration of quantum dynamics, AI, and quantum computing is crucial for future materials discovery.
  • Continued research in AI for quantum computing and quantum machine learning will further advance next-gen materials.
  • These synergistic methods promise to unlock new frontiers in materials science and technology.