In their paper, published yesterday in Nature, researchers reviewed the rapid progress in machine learning for the chemical sciences.
Almost every technological advance in human history is accompanied by the discovery or development of new materials, from the blending of copper and zinc to form bronze to the fabrication of high-quality silicon microchips that power digital technology. Designing materials for a specific demand is a mind-boggling task; a random mix-and-match of atomic building blocks could yield any one of an infinite number of possible compounds. Historically, the discovery of materials has involved a combination of chance, intuition, and trial and error - but this could all be set to change thanks to artificial intelligence.
An international team of scientists from the UK and the USA have recently published this review on the growing potential of machine learning for chemical design.
Humans have always enjoyed reasoning and intuition capabilities that far exceed those of machines. But scientists are now starting to turn to artificial intelligence driven solutions to accelerate their own materials discovery and optimisation processes. “Machine learning is a subfield of artificial intelligence that has evolved rapidly in recent years. 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" explains Keith Butler from ISIS Neutron and Muon Source, lead author of the review.
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. Artificial intelligence approaches consider all available data equally and find trends that a human researcher may miss due to bias towards a given interpretation.
But what's fueling the progress in this field? An important driver for the explosion of artificial intelligence in chemistry is the growth of open-source databases. “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" explains Keith Butler. Olexandr Isayev of UNC School of Pharmacy sees an often overlooked key advantage of artificial intelligence driven studies “they allow researchers to achieve an optimal balance between exploring a new parameter space and exploiting the possibilities in the current region."
As more and more researchers embrace artificial intelligence a new generation of computational science continues to grow. Fueled by open-source tools and data sharing artificial intelligence has the potential to revolutionise molecular and materials discovery as we know it.
The full research publication can be read here: K.T. Butler et al. “Machine learning for molecular and materials science" Nature volume 559, pages 547–555 (2018) DOI: 10.1038/s41586-018-0337-2
ISIS Neutron and Muon Source scientist Keith Butler is lead author of the review published in the prestigious journal Nature. For further information contact firstname.lastname@example.org.