Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement Learning

TL;DR

This paper introduces a Transformer-based encoder-decoder architecture combined with reinforcement learning to simulate, predict, and manipulate real-time scientific experiments, enabling accelerated discovery and exploration beyond physical limitations.

Contribution

It presents a novel approach integrating Transformer models with reinforcement learning to model and control real-time scientific experiments in silico.

Findings

Successfully modeled chemical oscillations with the architecture.

Paired models with RL controllers for real-time manipulation.

Demonstrated potential to discover new phenomena beyond lab limits.

Abstract

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.