Artificial intelligence and machine learning are drastically changing the face of chemical research according to an international team including researchers from the University of Bath.
The team from the UK and the USA, including PhD student Daniel Davies from the Centre for Sustainable Chemical Technologies and Department of Chemistry, published a review on the growing potential of machine learning for chemical design.
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“Machine learning is a branch of artificial intelligence where computers are programmed by learning from data. These methods have been around for a while, used extensively by Google, Yahoo, Amazon etc, for targeted advertising, translation and spam filtering for example,” said Davies.
“More recently they are being used to realise self-driving car and human-like robot technology. They are only just being applied to the physical sciences in a big way and have huge implications for the role that computers take on in science,” he said.
Machine learning and artificial intelligence offer the possibility of training computers by using the properties of materials that we already know, to help identify the champion systems of the future. This can find trends that a human researcher may miss due to bias towards a given interpretation.
“In fact, the use of ‘big data’ and artificial intelligence has been referred to as the fourth industrial revolution or the fourth paradigm of science. Machine learning is now being used to speed up the scientific process, designing crucial materials and molecules that we need for sustainable development, more rapidly.
“The main purpose of the article is to explain where machine learning is starting to rise to specific challenges in molecular and materials research that simply cannot be solved without it. We also identify some key barriers that need to be overcome next. For example, finding ways in which chemicals and compounds are represented to computers that only ‘think’ in 1s and 0s, is a big challenge.
“Our final summary is: ‘As scientists embrace the inclusion of machine learning with statistically driven design in their research programmes, the number of applications is growing at an extraordinary rate. This new generation of computational science, supported by a platform of open source tools and data sharing, has the potential to revolutionise the molecular and materials discovery process.’ I think this reflects the take-home message well which is that we predict this area will become an integral part of the scientific method – not just a separate area of research.”
This machine learning, identifying patterns, is accelerating the discovery of new materials.
“In traditional computational approaches, the computer is little more than a calculator, employing a hard-coded algorithm provided by a human expert. By contrast, the performance of machine learning techniques improves by seeing more and more real examples,” said Dr Keith Butler from ISIS Neutron and Muon Source, lead author of the review in Nature.
“This is particularly exciting in the context of a facility like ISIS where we produce vast quantities of data, we are sitting on a data goldmine and now we are beginning to be able to leverage that,” he added.
The Centre for Sustainable Chemical Technologies (CSCT) brings together researchers across the University of Bath, including the Departments of Biology & Biochemistry, Chemical Engineering, Chemistry, Electrical Engineering, Mechanical Engineering, Pharmacy & Pharmacology, Physics and Social & Policy Sciences.
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