We are pleased to announce the release of a new Virtual Special Issue on the topic of Shaping the Future of Materials Science with Machine Learning.
As we approach the end of the second decade of the 21st century, we may envisage a completely different paradigm for generating knowledge, which might become a reality in a few decades. According to this new paradigm, artificial systems (machines!) will be able to generate knowledge; that is to say, for the first time in history, knowledge would be created without human intervention. This prediction is based upon developments in machine learning following decades of intense research, which have achieved innovative leaps in recent years. Most notably, relevant progress in prediction schemes, classification methods, and advanced modelling have made it possible for machines to outperform humans in various intellectually-demanding tasks.
In this article selection, the reader will find compelling examples from different areas of materials science. Just to give a flavour of what has been compiled, we mention the diversity of materials and applications in a sample of interesting papers. From the development of concrete for civil construction, as presented in the paper Machine learning in concrete strength simulations: Multi-nation data analytics, by Chou et al., to the design of lithium-ion batteries, as explained in Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries, by Shandiz and Gauvin. Common to these papers is evidence that machine learning does enhance human capability in predicting the properties of materials. This is also beautifully illustrated in the proposal Material synthesis and design from first principle calculations and machine learning, by Takahashi and Tanaka, who use a database of materials and their properties to “teach” machine learning methods to predict new materials with desirable traits; a similar approach is adopted by Khan, Shamsi and Choi in the contribution entitled Correlating dynamical mechanical properties with temperature and clay composition of polymer-clay nanocomposites, which relies on support vectors and artificial neural networks to identify non-linear correlations between temperature and composition and mechanical properties in polymer-clay nanocomposites.
Click here to read the editorial and browse all the articles.