AI and RSC FIRST 2026 event

Artificial intelligence in chemistry is moving from a specialist topic into a core research capability. The question facing chemists is no longer whether AI will influence their work, but how they can use it effectively and responsibly.

We spoke to Helen Pain, chief executive at the Royal Society of Chemistry, to get her viewpoints on AI in chemistry. Read our interview with Helen and discover advice for the researcher community.

What Happened

AI in chemistry has moved beyond a specialist field and is now influencing almost every stage of the scientific process, from literature discovery and experiment planning to materials design and autonomous laboratories. At the same time, the technology is advancing faster than.

• Join us in Xiamen, China in November at RSC FIRST 2026: AI in Chemistry.

• RSC FIRST 2026 is an opportunity for the global community to shape that future together.

• We show that this even allows the use of wavefunction methods in CSP.

Key Details

We have enough real-world examples to demonstrate impact, but we are still early enough to shape how AI develops in a way that is scientifically rigorous, ethical and globally beneficial. You do not need to become a computer scientist.

• The presented workflow is broadly applicable to different molecular materials, without the need for a single periodic calculation at the reference level of theory.

• This paper presents tailored Δ-ML models that allow screening a wide range of crystal candidates while adequately describing the subtle interplay between intermolecular interactions such as H-bonding and many-body dispersion effects.

• The findings reveal the potential of LLMs to aid in scientific research, particularly in the efficient construction of structured datasets, which can help train models, predict, and assist in the synthesis of new metal–organic.

Why It Matters

What matters is becoming an informed scientist who knows when and how to use AI effectively. Where do you see the most immediate commercial opportunities for AI in chemistry right now?

• This work highlights the potential of AI in accelerating scientific discovery by bridging the gap between computational tools and experimental research.

• The findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: ‘Data’, ‘New applications’, ‘Machine learning algorithms’, and ‘Education’ from ASLLA Symposium, Gangneung, Republic of Korea.

What Reports Say

Coverage of the story so far points to:

• Continued reporting by The Royal Society of Chemistry as more details emerge