  Life and physical sciences have always been quick to adopt the latest
advances in machine learning to accelerate scientific discovery. Examples of
this are cell segmentation or cancer detection. Nevertheless, these exceptional
results are based on mining previously created datasets to discover patterns or
trends. Recent advances in AI have been demonstrated in real-time scenarios
like self-driving cars or playing video games. However, these new techniques
have not seen widespread adoption in life or physical sciences because
experimentation can be slow. To tackle this limitation, this work aims to adapt
generative learning algorithms to model scientific experiments and accelerate
their discovery using in-silico simulations. We particularly focused on
real-time experiments, aiming to model how they react to user inputs. To
achieve this, here we present an encoder-decoder architecture based on the
Transformer model to simulate real-time scientific experimentation, predict its
future behaviour and manipulate it on a step-by-step basis. As a proof of
concept, this architecture was trained to map a set of mechanical inputs to the
oscillations generated by a chemical reaction. The model was paired with a
Reinforcement Learning controller to show how the simulated chemistry can be
manipulated in real-time towards user-defined behaviours. Our results
demonstrate how generative learning can model real-time scientific
experimentation to track how it changes through time as the user manipulates
it, and how the trained models can be paired with optimisation algorithms to
discover new phenomena beyond the physical limitations of lab experimentation.
This work paves the way towards building surrogate systems where physical
experimentation interacts with machine learning on a step-by-step basis.
