From autonomous vehicles to  Facial-Recognition Software, artificial intelligence (AI) is becoming an increasingly usual and effective tool with broad applications in everyday life., also having a growing impact on science. This overgrowth is mainly based on two factors; the vast accumulation of data in our increasingly digitized and automatized society and today’s fastest computers’ growing capability and speed for processing that data.

The most interesting scientific applications of scientific machine learning are those related to optimization and discovery of materials, where the answers are unknown beforehand.

AI, including machine learning (ML) and Deep learning, is a powerful tool for deriving new insights from extensive data set analysis. This collection of statistical methods has already proved to speed up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and use machine learning in solid-state systems.

On the other side, science evolution is also  increasingly based on “big data.” It is believed that AI is poised to be a powerful new tool for analysing this data and deriving disruptive discoveries from it. Hence, using basic ML algorithms, scientists can identify patterns that are almost impossible for humans to detect at speeds that are hundreds to thousands of times faster than traditional data analysis techniques.

Through close collaboration with Hi-Iberia, the GREENER group is committed to developing traditional and innovative AI approaches to tackle a wide variety of energy materials discovery and optimization.