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Generative AI for computational chemistry: A roadmap to predicting emergent phenomena.

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Area of Science:

  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Generative AI is rapidly advancing, offering potential breakthroughs in computational chemistry.
  • Current AI methods show promise in molecular structure sampling, force field development, and simulation acceleration.

Purpose of the Study:

  • To provide a structured overview of generative AI methods in computational chemistry.
  • To highlight applications and challenges of AI in predicting chemical phenomena.

Main Methods:

  • Review of fundamental concepts in generative AI and computational chemistry.
  • Discussion of widely used AI methods: autoencoders, generative adversarial networks, reinforcement learning, flow models, and language models.

Main Results:

  • Generative AI has demonstrated progress in molecular sampling, force field development, and speeding up simulations.
  • Selected applications include force field development and protein/RNA structure prediction.

Conclusions:

  • Generative AI faces challenges in becoming truly predictive, especially for emergent chemical phenomena.
  • Future AI models require integration of core chemical principles, particularly statistical mechanics, to meet predictive standards.