MD simulations for predicting glass transition temperatures for cellulose derivatives
Lignocellulose-based materials have gain lots of attention in both academia and industry as it is expected to be able to replace the fossil-based materials, particularly as environment-friendly thermoplastic. For a practical use of lignocellulose-based materials, it is necessary to amass enormous data of mechanical properties of them. Nevertheless, it is very challenging to investigate and analyse their properties due to the fact that it can be affected simultaneously by several factors such as chemical/physical modification, degree of substitution, and so on. Computational methods in tandem with experiments could significantly contribute to studies on lignocellulose-based materials since computational approaches could control such factors as intended. Thus, computational methods not only can reduce the number of trial-and-error for experiments, but also provide complemental results which are not simple to obtain from experiments. In this project, we expect to build a database of lignocellulose derivatives using computational methods to which we can apply AI tools.