Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry

Area of Science:

• Chemistry
• Computer Science
• Materials Science
• Drug Discovery
• Catalysis

Background:

• Artificial intelligence (AI) is increasingly utilized in chemical research.
• Publications leveraging AI for data-driven insights and predictive models are rising.
• AI impacts molecular representation, generative models, and experimental automation.

Purpose of the Study:

• To present diverse perspectives on AI's impact in chemistry.
• To explore AI's role in computation, discovery, and experimental support.
• To discuss challenges and redefine problems for accelerated AI-driven chemical research.

Main Methods:

• Compilation of ten diverse perspectives from experts in experimental chemistry, computational chemistry, computer science, and engineering.
• Analysis of AI applications across drug discovery, catalysis, chemical automation, chemical physics, and materials chemistry.
• Discussion of themes including AI for computation, facilitating discovery, supporting experiments, and enabling technologies.

Main Results:

• AI is accelerating simulations and exploration of chemical space.
• AI facilitates experimental planning and laboratory automation.
• Diverse applications of AI are evident in drug discovery, catalysis, and materials science.

Conclusions:

• AI is a transformative force across all aspects of chemical research.
• Addressing challenges and redefining problems is key to maximizing AI's impact.
• Interdisciplinary collaboration is crucial for advancing AI in chemistry.