In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

Here we summarize recent progress in machine learning for the chemical sciences. We
outline machine-learning techniques that are suitable for addressing research questions in …

Accelerating the discovery of advanced materials is essential for human welfare and
sustainable, clean energy. In this paper, we introduce the Materials Project (www …

Graph networks are a new machine learning (ML) paradigm that supports both relational
reasoning and combinatorial generalization. Here, we develop universal MatErials Graph …

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …

Over the past decades, Li-ion battery (LIB) has turned into one of the most important
advances in the history of technology due to its extensive and in-depth impact on our life. Its …

The screening of novel materials with good performance and the modelling of quantitative
structure-activity relationships (QSARs), among other issues, are hot topics in the field of …

Inorganic–organic hybrid materials,, such as organically templated metal oxides, metal–
organic frameworks (MOFs) and organohalide perovskites have been studied for decades …

Data science models, although successful in a number of commercial domains, have had
limited applicability in scientific problems involving complex physical phenomena. Theory …