What led to the rapid progress in machine learning for chemical sciences

Aug 1, 2018 | 3522 Views

Artificial intelligence is changing the face of chemical science radically. from high quality silcion chips that are being used to power digital technology, or bronce being made out of gopper or zinc. the discovery of new material has always go hand in hand with technological advances in our history.

An international team of scientists from the UK and the USA, including Ph.D. 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.
Daniel said: "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.

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. 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 1's and 0's, is a big challenge.

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.

Dr. Keith Butler from ISIS Neutron and Muon Source, lead author of the review, said: "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."

To help identify the champion systems of the future, machine learning and AI offer the possibility of training computers by using the properties of materials that they already have and know about.

Mixing and matching of atmoic blocks can bring out infinite numbers of compounds, and a mind boggling task is designining materials for a specific demand.

Human researchers are bias towards interpretations due to this they might end up missing the trends, on the other hand artificial intelligence approaches all available data equally.

Source: HOB,CCO