Encoding Materials Dynamics for Machine Learning Applications

Recent years witnessed an explosion of machine learning methods in all sectors. However, in the materials sector, and even more specifically in the biomaterials sector, although there have been numerous attempts at generalization, there has been a severe problem of coding the problem. The reason lies mainly in the temporal and spatial dimensions of the materials and their intrinsic complexity. In this contribution, we wish to suggest a possible universal coding of materials. This coding exploits a pseudo-semantic analysis and can be particularly advantageous in the study of polymeric biomaterials.