Artificial Intelligence, Machine Learning, and Big Data for Corrosion Control – Qou Vadis?

The current era has seen amazing, paradigm shifting scientific developments. We now know that the size of small molecule and materials spaces is for all practical purposes, infinite. In many fields, this recognition has seen a rapid increase in automation and robotics, allowing synthesis of new molecules and materials and measurement of properties much faster than before. Unfortunately, corrosion science has not yet seen the benefit of these advances, but undoubtedly will. Finding ‘islands of chemical utility’ in vast chemical spaces requires AI and data driven machine learning. Machine learning algorithms are universal approximators, applicable to diverse applications, such as medicine, finance, manufacturing, social media etc. Recent developments in quantum machine learning methods, generative methods to design new molecules and materials with improved properties, massive ‘make on demand’ chemical libraries such as the 30+ billion ZINC-22 library, autonomous chemical discovery systems with no human in the loop, and other AI methods (evolutionary algorithms) are accelerating discovery of novel, useful molecules and materials. This paper summarizes technical developments in the corrosion inhibition field. We describe how data-driven machine learning methods generate models linking molecular properties to corrosion inhibition can predict performance of materials not yet synthesized or tested. We provide a perspective on the benefits of additive manufacturing, high throughput corrosion inhibitor testing, other AI methods such as evolutionary algorithms, and autonomous corrosion inhibitor design systems. These exciting technologies represent a paradigm shift in rapid discovery of novel, effective corrosion inhibitors for a range of metals and alloys, in diverse environments.