A new challenge ahead: Automating Science Discovery

The reason I started this blog was to document my progress as I delved into machine learning. One of the primary motivations for doing that was that I was preparing a proposal to the UK’s Engineering and Physical Science Reaearch Council for a call for feasibility studies bringing together physical science and artificial intelligence. After an Expression of Interest, an audition(!), that I did from Los Angeles at 3am local time, writing and submitting a full proposal and then attending an interview, I was unsurprisingly thrilled to learn that our bid was successful.

One of the key requirements for the proposal call was that we develop not just the use of AI in the physical sciences but also new AI. Below is the summary of our project describing the particular area of the physical sciences that we will focus on and the challenges we have set ourselves.

“De-mixing is one of the most ubiquitous examples of self-assembly, occurring frequently in complex fluids and living systems. It has enabled the development of multi-phase polymer alloys and composites for use in sophisticated applications including structural aerospace components, flexible solar cells and filtration membranes. In each case, superior functionality is derived from the microstructure, the prediction of which has failed to maintain pace with synthetic and formulation advances. The interplay of non-equilibrium statistical physics, diffusion and rheology causes multiple processes with overlapping time and length scales, which has stalled the discovery of an overarching theoretical framework. Consequently, we continue to rely heavily on trial and error in the search for new materials.”

“Our aim is to introduce a powerful new approach to modelling non-equilibrium soft matter, combining the observation based empiricism of machine learning with the fundamental based conceptualism of physics. We will develop new methods in machine learning by addressing the broader challenge of incorporating prior knowledge of physical systems into probabilistic learning rules, transforming our capacity to control and tailor microstructure through the use of predictive tools. Our goal is to create empirical learning engines, constrained by the laws of physics, that will be trained using microscopy, tomography and scattering data. In this feasibility study, we will focus on proof-of-concept, exploring the temperature / composition parameter space for a model blend, building the foundations for our ambition of using physics informed machine learning to automate and accelerate experimental materials discovery for next generation applications.”

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