Predicting real-time scientific experiments using transformer models and reinforcement learning

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.Comment: 8 pages, 5 figures, conferenc

Embodied Evolution of Artificial Cells in a Hybrid Wet/Hard-ware Platform

No abstract available

Towards heterotic computing with droplets in a fully automated droplet-maker platform

The control and prediction of complex chemical systems is a difficult problem due to the nature of the interactions, transformations and processes occurring. From self-assembly to catalysis and self-organization, complex chemical systems are often heterogeneous mixtures that at the most extreme exhibit system-level functions, such as those that could be observed in a living cell. In this paper, we outline an approach to understand and explore complex chemical systems using an automated droplet maker to control the composition, size and position of the droplets in a predefined chemical environment. By investigating the spatio-temporal dynamics of the droplets, the aim is to understand how to control system-level emergence of complex chemical behaviour and even view the system-level behaviour as a programmable entity capable of information processing. Herein, we explore how our automated droplet-maker platform could be viewed as a prototype chemical heterotic computer with some initial data and example problems that may be viewed as potential chemically embodied computations

Evolution of oil droplets in a chemorobotic platform

Evolution, once the preserve of biology, has been widely emulated in software, while physically embodied systems that can evolve have been limited to electronic and robotic devices and have never been artificially implemented in populations of physically interacting chemical entities. Herein we present a liquid-handling robot built with the aim of investigating the properties of oil droplets as a function of composition via an automated evolutionary process. The robot makes the droplets by mixing four different compounds in different ratios and placing them in a Petri dish after which they are recorded using a camera and the behaviour of the droplets analysed using image recognition software to give a fitness value. In separate experiments, the fitness function discriminates based on movement, division and vibration over 21 cycles, giving successive fitness increases. Analysis and theoretical modelling of the data yields fitness landscapes analogous to the genotype–phenotype correlations found in biological evolution. , Trevor Hinkley, James Ward Taylor Kliment Yane

The Evolution of Active Droplets in Chemorobotic Platforms

There is great interest in oil-in-water droplets as simple systems that display astonishingly complex behaviours. Recently, we reported a chemorobotic platform capable of autonomously exploring and evolving the behaviours these droplets can exhibit. The platform enabled us to undertake a large number of reproducible experiments, allowing us to probe the non-linear relationship between droplet composition and behaviour. Herein we introduce this work, and also report on the recent developments we have made to this system. These include new platforms to simultaneously evolve the droplets’ physical and chemical environments and the inclusion of selfreplicating molecules in the droplets

A probabilistic chemical programmable computer

The exponential growth of the power of modern digital computers is based upon the miniaturisation of vast nanoscale arrays of electronic switches, but this will be eventually constrained by fabrication limits and power dissipation. Chemical processes have the potential to scale beyond these limits performing computations through chemical reactions, yet the lack of well-defined programmability limits their scalability and performance. We present a hybrid digitally programmable chemical array as a probabilistic computational machine that uses chemical oscillators partitioned in interconnected cells as a computational substrate. This hybrid architecture performs efficient computation by distributing between chemical and digital domains together with error correction. The efficiency is gained by combining digital with probabilistic chemical logic based on nearest neighbour interactions and hysteresis effects. We demonstrated the implementation of one- and two- dimensional Chemical Cellular Automata and solutions to combinatorial optimization problems.Comment: 20 page manuscript, 6 figures, 112 page supplementary volum

Adaptive artificial evolution of droplet protocells in a 3D-printed fluidic chemorobotic platform with configurable environments

Few studies have explored the effect of a changing environment on artificial chemical evolution. Here, the authors develop an evolutionary platform that alters the physical environment of droplet protocells, showing that a population of simple chemical species can adapt to its surroundings, in analogy to natural evolution

A programmable chemical computer with memory and pattern recognition

Current computers are limited by the von Neumann bottleneck, which constrains the throughput between the processing unit and the memory. Chemical processes have the potential to scale beyond current computing architectures as the processing unit and memory reside in the same space, performing computations through chemical reactions, yet their lack of programmability limits them. Herein, we present a programmable chemical processor comprising of a 5 by 5 array of cells filled with a switchable oscillating chemical (Belousov–Zhabotinsky) reaction. Each cell can be individually addressed in the ‘on’ or ‘off’ state, yielding more than 2.9 × 1017 chemical states which arise from the ability to detect distinct amplitudes of oscillations via image processing. By programming the array of interconnected BZ reactions we demonstrate chemically encoded and addressable memory, and we create a chemical Autoencoder for pattern recognition able to perform the equivalent of one million operations per second

Automatic generation of 3D-printed reactionware for chemical synthesis digitization using ChemSCAD

We describe a system, ChemSCAD, for the creation of digital reactors based on the chemical operations, physical parameters, and synthetic sequence to produce a given target compound, to show that the system can translate the gram-scale batch synthesis of the antiviral compound Ribavirin (yield 43% over three steps), the narcolepsy drug Modafinil (yield 60% over three steps), and both batch and flow instances of the synthesis of the anticancer agent Lomustine (batch yield 65% over two steps) in purities greater than or equal to 96%. The syntheses of compounds developed using the ChemSCAD system, including reactor designs and analytical data, can be stored in a database repository, with the information necessary to critically evaluate and improve upon reactionware syntheses being easily shared and versioned